Posted on

Leakage after left atrial appendage occlusion associated with higher risk of adverse events

Leakage after left atrial appendage occlusion associated with higher risk of adverse events

Patients who had leakage to the left atrial appendage due to incomplete device sealing after left atrial appendage occlusion (LAAO) experienced more clotting and bleeding events within a year following their procedure compared with patients who had no leaking, according to a study presented at the American College of Cardiology’s 71st Annual Scientific Session.

The study, which included data from more than 50,000 patients, is by far the largest to date to assess how leaking after LAAO affects the likelihood of adverse health outcomes. The results suggest that even small leaks are associated with a 10%-15% higher risk of adverse events.

Our study shows that any leak matters, and we should find ways to optimize the procedure to minimize the proportion of patients who end up with leaks. Because this is a preventative procedure, it is important to find ways to master this and prevent leaks from happening. Fortunately, there are indications that leaking might be less common with newer devices and improved techniques.”


Mohamad Alkhouli, MD, professor of medicine at Mayo Clinic and study’s lead author

Stroke is a major concern in patients with atrial fibrillation (AFib), the most common heart rhythm disorder. Blood thinners are the primary medical option for reducing the risk of strokes, which are caused when a blood clot blocks an artery in the brain; however, blood thinners are not suitable for many patients due to drug interactions, bleeding complications, cost, frequent blood checks with warfarin or other reasons. The WATCHMAN device, approved by the U.S. Food and Drug Administration in 2015, is designed to help prevent strokes by sealing off the heart’s left atrial appendage where blood can pool and clot. It is now widely used to reduce the risk of stroke in patients with AFib who cannot tolerate long-term use of blood thinners.

For the study, researchers analyzed data from 51,333 patients who underwent LAAO procedures with the WATCHMAN device between 2016-2019 as recorded in the ACC’s LAAO Registry, a database that includes nearly all LAAO procedures conducted in the U.S. and is part of the College’s NCDR registries. Echocardiograms were used to classify the size of any leaks around the device an average of 45 days after a LAAO procedure, a standard part of the clinical follow-up for this procedure. Registry data also included information about subsequent adverse health events occurring in the years after the procedure.

Overall, researchers found that 73.4% of patients had no leaks, 25.8% had small leaks (greater than zero but less than 5 millimeters across) and 0.7% had large leaks (greater than 5 millimeters)—proportions similar to those reported in previous registry studies and clinical trials.

While relatively few patients—roughly 2%-3%—experienced adverse events in the year following their LAAO procedure, the researchers found that the relative risk of these events varied significantly between patients with and without leaks. Compared to patients who had no leaking, those with small leaks had a 10% higher relative risk of suffering any major adverse events, an 11% higher relative risk of major bleeding complications, and a 15% higher relative risk of clotting-related events, including stroke, systemic embolization and transient ischemic attacks.

The study revealed no significant differences in the rate of adverse events between patients with large leaks and those with small or no leaks, which Alkhouli said is likely due to the use of anticoagulants in these patients. Instructions for the WATCHMAN device indicate that patients who have residual leaks greater than 5 millimeters across should be considered to have a failed procedure, and they are generally treated with anticoagulants.

Alkhouli said leaking is relatively common with LAAO procedures because the size and shape of the left atrial appendage varies widely from person to person, which can make it difficult for operators to position the device in a way that completely seals off the appendage. He noted that the shape of the appendage portion where the leaked blood flows into could affect the associated risks. More recent technologies, such as the use of CT scans to image the appendage and software that allows users to practice the procedure virtually before operating on a patient, could help operators place the WATCHMAN device in a more optimal position. Alkhouli said newer generation WATCHMAN device designs, which were introduced in 2021, could also help to reduce the risk in some patients.

The study was based on registry data reflecting real-world practices, meaning variations in the measurement of leaks could exist and may impact the results of the study, Alkhouli said. He added that future studies could help clarify whether using blood thinners in some patients with leaks smaller than 5 millimeters would be helpful in reducing stroke risk.

This study was simultaneously published online in the JACC: Clinical Electrophysiology at the time of presentation. The study was funded by Boston Scientific, maker of the WATCHMAN device.

Alkhouli will present the study, “Residual Leaks Post Left Atrial Appendage Occlusion,” on Sunday, April 3, at 12:15 p.m. ET / 16:15 UTC in the Main Tent, Hall D.

Posted on

Heart complications after a stroke increase the risk of future cardiovascular events

Heart complications after a stroke increase the risk of future cardiovascular events
stroke
A blood clot forming in the carotid artery. Credit: copyright American Heart Association

People who survive an ischemic stroke are much more likely to develop major heart complications during the first month after their stroke, and, as a result, they also have an increased risk of death, heart attack or another stroke within five years, compared to people who don’t develop heart problems soon after a stroke, according to new research published today in Stroke.

Ischemic stroke is the most common type of stroke—accounting for 87% of all strokes—and occurs when blood flow to the brain is blocked. After a stroke, people often have cardiovascular complications, known as stroke-heart syndrome. Heart complications include acute coronary syndrome, angina (chest pain), heart rhythm issues such as atrial fibrillation, arrhythmia and ventricular fibrillation; heart attack; heart failure or Takotsubo syndrome (broken heart syndrome), a type of stress-induced temporary enlargement of a part of the heart that impacts its ability to pump effectively. These conditions increase the risk of disability or death in the short term, yet the long-term consequences for people with stroke-heart syndrome is unknown.

“We know heart disease and stroke share similar risk factors, and there’s a two-way relationship between the risk of stroke and heart disease. For example, heart conditions such as atrial fibrillation increase the risk of stroke, and stroke also increases the risk of heart conditions,” said Benjamin J.R. Buckley, Ph.D., lead author of the study and a postdoctoral research fellow in preventive cardiology at the Liverpool Centre for Cardiovascular Science, University of Liverpool in the United Kingdom. “We wanted to know how common newly diagnosed heart complications are after a stroke and, importantly, whether stroke-heart syndrome is associated with increased risk of long-term major adverse events.”

Researchers analyzed the medical records of more than 365,000 adults treated for ischemic stroke at more than 50 health care sites predominantly in the United States, between 2002 and 2021. People who were diagnosed with stroke-heart complications within four weeks after a stroke were matched to an equal number of stroke survivors who did not have these heart complications within four weeks (the control group).

After adjusting for potential confounding factors, such as age, sex and race/ethnicity, and comparing the stroke survivors who had new heart complications to those who did not, the analysis found:

  • Overall, among all stroke survivors in the study, about 1 in 10 (11.1%) developed acute coronary syndrome, 8.8% were diagnosed with atrial fibrillation, 6.4% developed heart failure, 1.2% exhibited severe ventricular arrythmias and 0.1% developed ‘broken heart’ syndrome within four weeks after the stroke.
  • Risk of death within five years after a stroke significantly increased among the participants with new heart complications: 49% more likely if they had developed acute coronary syndrome; 45% more likely if they had developed atrial fibrillation/flutter; and 83% more likely if they developed heart failure. Severe ventricular arrhythmias doubled the risk of death.
  • Chance of hospitalization and heart attack within five years after a stroke was also significantly higher among those who developed heart complications within the one-month window.
  • Stroke survivors with Takotsubo syndrome were 89% more likely to have a major heart event within the five years after their stroke.
  • People who developed atrial fibrillation after stroke were 10% more likely to have a second stroke within five years after their stroke.
  • People with stroke and newly diagnosed cardiovascular complications were 50% more likely to have a recurrent stroke within five years after the first stroke.

“I was particularly surprised by how common stroke-heart syndrome was and the high rate of recurrent stroke in all subgroups of adults with stroke-heart syndrome” Buckley said. “This means that this is a high-risk population where we should focus more secondary prevention efforts.”

The study’s results build on the understanding of the two-way link between the brain and the heart and extend this understanding to long-term health outcomes. “We are working on additional research to determine how stroke-heart syndrome may be better predicted,” Buckley said.

“We also need to develop and implement treatments to improve outcomes for people with stroke-heart syndrome,” Buckley said. “For example, comprehensive exercise-based rehabilitation may be helpful after a stroke, so for people with stroke and newly developed heart complications, it should also be beneficial, maybe even more so. I think this is an interesting area for future research.”

Study limitations include that it is a retrospective analysis and knowing whether the heart complications diagnosed following an ischemic stroke were caused by stroke or rather contributed to the stroke, is unclear.

“This research underscores why it’s so important for neurologists and cardiologists to work hand-in-hand with their patients and each other to understand why the first stroke occurred and perform a comprehensive assessment to identify new risk factors for another stroke and for cardiovascular disease that may require initiation of prevention therapies,” said Lee H. Schwamm, M.D., volunteer chair of the American Stroke Association Advisory Committee and the C. Miller Fisher Chair in Vascular Neurology at Massachusetts General Hospital in Boston. “The American Stroke Association recommends a personalized secondary stroke prevention plan for every stroke survivor.”


Even with statins, high triglycerides may increase risk of second stroke


More information:
Stroke (2022). www.ahajournals.org/doi/STROKEAHA.121.037316

Citation:
Heart complications after a stroke increase the risk of future cardiovascular events (2022, March 31)
retrieved 31 March 2022
from https://medicalxpress.com/news/2022-03-heart-complications-future-cardiovascular-events.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.

Posted on

Banks should be watchful of economic impact of risk events: RBI Deputy Governor Jain

Banks should be watchful of economic impact of risk events: RBI Deputy Governor Jain

Individual financial institutions, especially banks, should be watchful of the economic impact of risk events such as theCovid-19 pandemic and potential economic disruption due to geo-political events in Europe, and take adequate measures to maintain their resilience, according to Reserve Bank of India Deputy Governor MK Jain.

The Deputy Governor observed that the nature and frequency of risks faced by the financial system today are unparalleled and unpredictable.

“In this regard, it is important to recognise the inter-linkages between quality of governance and resilience of financial institutions.

“Even as high quality governance enhances resilience, poor corporate governance is a source of risk to financial institutions and the financial system,” Jain said in his keynote address at CAFRAL.

He observed that effective internal defences will help in building organisations that are strong, resilient, disciplined and enjoy the benefits of sustained growth and customer confidence.

It will also pre-empt supervisory action and the attendant reputational risks that arise in case transgressions are detected.

He underscored the importance of governance structures and practices in the banks, which should prioritise protection of the interest of their depositors.

Banks enjoy high leverage as they can raise a substantial amount of uncollateralised deposits, and perform the function of liquidity and maturity transformation, he said.

RBI’s assessment and findings

In recent years, RBI’s assessment of oversight and assurance functions has received greater focus given their importance in addressing the root cause of problems.

The common weaknesses in oversight and assurance functions include failure / delay in detection and reporting of non-compliance, persisting sub-par compliance, deficiencies in compliance testing with respect to inadequate coverage and limited transaction testing, persisting irregularities due to non-addressing of root causes and not ensuring sustainability of compliance.

Further, the compliance operation was often found to be inadequately staffed and the quality of staff was also found be wanting.

Risk management

On the risk management front, the central bank found a disconnect between the Risk Appetite Framework as approved by the board and actual business strategy and decision making, weak risk culture which was amplified by the absence of guidance from the senior management, improper risk assessment, repeated exceptions to risk policies, conflict of interest, especially in related party transactions, and absence or faulty enterprise-wide risk management.

Operational risk was seen to be high on account of people risk, elevated IT and technology risk, and high outsourcing risks, Jain said.

On the internal audit front, RBI found the audit process unable to capture irregularities, certain areas were not covered under the scope of audit, and compliance and audit were not collaborating with each other.

It came across lack of ownership and accountability, inadequate review of practices that require alignment to address the interests of all stakeholders, and non-compliance/ delay in compliance with audit observations .

Supervisory expectations

The Deputy Governor observed that oversight and assurance functions have a key role in value creation for a financial institution, strengthening public confidence, preserving and enhancing its reputation, and maintaining the integrity of its business and management.

“The board should engage with the oversight and assurance functions and assure them of direct and unfettered access.

“The “tone from the top” would set the pace for a sound organisation culture that values honesty and integrity,” he said.

Jain emphasised that appointment and removal of heads of oversight and assurance functions should have stringent barriers and they must be independent of executive management.

Assurance functionaries should not perform tasks on which they are required to take a view independent of the risk takers.

Weaknesses and irregularities recurring

On the recurrence of weaknesses and irregularities, the Deputy Governor said: “My expectation from the banks is that they make serious efforts towards overall improvement and sustainability in their compliance.”

Jain noted that the quality of deliberations, the level of challenge provided to executive management, and the time allocated to important agenda items is often inadequate.

‘The board members should focus on strategic and important matters… Many times, a large number of agenda items are included, including table items, which do not allow for proper evaluation of proposals. The board also needs to work in a cohesive manner,” he said.

Cybersecurity

Jain underscored the need for the board to start looking at cyber risk as an enterprise-wide risk management issue, rather than a pure IT security issue, owing to its firm-wide implications.

Adequate investments in technology should be ensured.

“In its role of oversight, the board needs to oversee the overall cybersecurity management, including appropriate risk mitigation strategies, systems, processes, and controls.

“Whether the institution has the appropriate skills, resources, and approaches in place to minimise cyber risk and mitigate any damage that may occur also needs to be seen,” the Deputy Governor said.

It is important to ensure that financial institutions are board-driven and do not end up being dominated by individuals. Experience has shown that this leads to undesirable consequences, he added.

While regulations are in place to check improper Related Party Transactions, including their disclosure, it is important that the Board and Audit Committee exercise close oversight over such matters and get satisfactory assurances.

Published on


March 29, 2022

Posted on

Adverse events and risk management in residential aged care | RMHP

Angiotensinogen and Risk of Stroke Events in Patients with Type 2 Diab | DMSO

Introduction

Regarding urbanization and industrialization, population aging is one of the most urgent problems globally, specifically for developing countries with limited resources for old-age care.1 China has the highest number of older adults; it is also one of the countries with the fastest aging population.2 The proportion of adults over 65 years has increased from 13.3% in 2010 to 18.7% in 2020.3 Residential aged care facilities (RACFs), a thriving way of old-age care, have become increasingly important in China, because of the “one-child” policy, increase in life expectancy, and weakening role of family care.4 Currently, less than 200,000 nurses provide nursing care for about 2.146 million older adults in 42,300 RACFs.5 Due to the decline of physical strength and physiological functions of older adults, their chances of suffering from chronic physical diseases, mental disorders, and disabilities have increased.6 Along with the shortage of nursing staff, insufficient professional skills, and other problems,7 older adults living in RACFs are at a high risk of suffering from care accidents and nursing adverse events (eg falls, burns, choking, aspiration, wandering away, and pressure sores),8 which negatively impact their physical and mental wellbeing and can cause disputes, economic and property losses, among others. The role of risk management in effectively reducing nursing adverse events and maintaining older adults’ safety has been widely documented.9,10 Given various risks faced by RACFs, managers urgently need to actively adopt risk management and early warning strategies to reduce the potential mistakes in care delivery for older adults.

Background

Risk management is a complicated process of implementing and maintaining countermeasures to reduce risk impact to an acceptable level.11 In the past decade, governments, researchers, and RACFs have been devoted to exploring nursing risk indicators, establishing nursing risk early warning systems, and formulating nursing risk prevention strategies. For example, the UK and the US have applied a standardized assessment tool, namely the Minimum Data Set (MDS), to systematically assess and review nursing practices, identify nursing home residents at risk of deterioration, and protect residents’ integrity and autonomy.12,13 Australia developed nursing risk assessment indicators for older adults, which involve pressure sores, falls or fractures, use of physical restraints, and psychological and behavioral symptoms.14 Several risk analysis methods such as Failure Mode and Effects Analysis (FMEA),15 Fine–Kinney method,16 SHEL model (software, hardware, environment, and liveware),17 Swiss cheese model,18 are widely applied to identify potential human mistakes in practice. With the wide application of information technology (IT) in healthcare, risk management information systems (RMIS) are often applied for data collection, information analysis, and statistical reports to identify and monitor preventable incidents. In the UK, through the National Early Warning Score (NEWS) system19 as well as the National Reporting and Learning System, a supportive environment for actively reporting and sharing errors is created, and related experiences and lessons are widely disseminated.

Chinese scholars and healthcare professionals tried to conduct preliminary studies of risk management in RACFs. For example, Li et al20 used FMEA to analyze various internal risks regarding infectious diseases, injuries, falling, and accidents in RACFs. Zhou21 tried to establish risk prevention and management systems for RACFs including external, internal, and economic risks. Based on the prevention, preparedness, response, and recovery (PPRR) risk management model, Zhang22 established an emergency practical path for public health emergencies in RACFs. China’s government departments such as the China National Committee on Aging, Ministry of Civil Affairs, China Academy of Social Management have tried to improve RACFs care quality and have promulgated relevant policies and regulations. China’s Ministry of Civil Affairs created requirements for the room environment and older adults’ sanitation as well as safety measures in RACFs, such as barrier-free design and smooth and non-slip floors. The establishment of such facilities should pass the evaluation by local authorities regarding construction, fire protection, sanitation, and epidemic prevention. Additionally, the national mandatory standard-basic specification of service safety for senior care organizations requires each RACF to reduce risks of choking, pressure ulcer, scald, fall, and so on through standardized operation procures and risk management.23

Effective risk management activities usually include the following stages: identification, analysis, evaluation and treatment, and communication and consultation.24 Evaluation and feedback are essential to accumulate long-term evidence and guarantee the care outcomes and older adults’ safety regarding risk management and early warning in RACFs. However, existing studies fail to provide a clear picture of the prevalence status, risk management, and risk early warning of nursing adverse events in RACFs. Thus, this study aims to (1) investigate the prevalence of nursing adverse events, risk management, and risk early warning status and (2) explore the factors associated with risk management in RACFs in China.

Materials and Methods

Design

This study used a cross-sectional design and employed an online questionnaire to investigate RACFs in Hunan Province.

Participants

A convenience sample (a type of non-probability sampling method where the sample is drawn from a group of people easy to contact and obtain their participation)25 of 272 RACFs were recruited in Hunan Province. RACFs’ managers were invited to respond to the survey. The inclusion criteria were 1) license for establishment and registration in the local civil affairs department; 2) operation duration of at least one year; 3) equipped with ten or more beds; 4) consent to participate in the study.

Survey Tools

The self-administered questionnaire used had four sections: prevalence of nursing adverse events, risk management, risk early warning, and general information survey scales.

Prevalence of Nursing Adverse Events

This section is used to inquire about the prevalence of nursing adverse events in RACFs in the past year (2019), including 14 fill-in-the-blank questions. Respondents were required to provide an accurate figure in each blank. The type of adverse events in this study included falls,26 falling out of beds (or chairs), fractures,27 food poisoning,28 burns or scalds,29 choking,30 aspiration or swallowing of foreign bodies,31 wandering away,32 accidental death, pressure sores,33 medication errors,34 adverse events caused by physical restraints,35 and accidental catheter removal (ie cannula, endotracheal tube, nasogastric tube, and urinary catheter).36

Risk Management Scale

The risk management scale was self-developed from our previous research37 to evaluate the status of RACFs. It includes 28 items and four management dimensions: environment (4 items), personnel (4 items), service (10 items), and safety (10 items). Each item is scored on a five-point Likert scale ranging from 1 (completely inappropriate) to 5 (completely appropriate). The scale is scored by summing the numerical ratings for each item and dividing the result by the total number of items. Possible scores ranged from 1 to 5. The higher the score, the better risk management. The overall Cronbach’s α coefficient of this scale was 0.974, and its split-half reliability (used to measure the internal consistency reliability of survey instruments and assessed by splitting the items of survey instrument in half, and then calculating the correlation of the scores for each half)38 was 0.951. Cronbach’s α coefficients for the environment personnel, service, and safety management dimensions were 0.842, 0.905, 0.924, and 0.943, respectively.

Risk Early Warning Scale

The risk early warning scale was self-designed from our previous study39 to evaluate the early warning status of RACFs. The scale comprises four dimensions with 26 items: nursing staff training (six items), a contingency plan for public security incidents (eight items), a contingency plan for age-related safety incidents (seven items), and emergency management (five items). Participants respond “Yes” or “No” to each item (1 = Yes; 0 = No). The total score ranges from 0 to 26, and a higher score signifies better risk early warning. In this study, the Cronbach’s α coefficient of the entire scale was 0.874 and the split-half reliability was 0.786. Cronbach’s α coefficients for the nursing staff training, a contingency plan for public security incidents, contingency plan for age-related safety incidents and emergency management dimensions were 0.720, 0.813, 0.724, and 0.682, respectively.

General Information

This section captured RACFs’ demographic characteristics, such as the ownership and location of the facility, whether it has the Practice Certificate of Social Welfare Facilities, operation duration (year), bed-size, number of residents, number of nursing staff, whether it requires nursing staff to work with certificates, and payment for nursing staff (RMB/month).

Data Collection

From February 1 and March 1, 2020, the primary researchers sent the recruitment invitation and informed consent form to the RACF managers in Hunan Province through email and obtained a copy of the written informed consent from 401 eligible RACFs. Then, we recruited sophomore nursing students as investigators, who were not allowed to return to School because of the isolation policy due to COVID-19. From March 5 to May 10, 2020, potential student investigators received curriculum and instructions on the questionnaire survey guidelines, quantitative research design, and data collection methods. The primary researcher distributed the questionnaire link to WeChat groups (the most popular social media application in China) and ensured all student investigators had the survey link. Finally, 114 students participated in the data collection of RACFs in their county administrative areas (one investigator per county-level administrative region). From May 13 to June 1, 2020, student investigators collected data from nearby RACFs and invited the managers to respond to the survey. In the field survey, the investigators did not conduct the questionnaire survey until they obtained the participants’ oral consent. While interviewing the RACF managers according to the survey scale, the investigators recorded the answers into the online questionnaire (https://www.wenjuan.com/s/F3aaUvT/) on their mobile phones.

Ethical Considerations

Before collecting data, the investigators explained the purpose, process, as well as potential benefits and risks to the RACF managers. The collected data did not contain the identifiable information of the RACFs or managers. This study conforms to the provisions and ethical principles of the 1995 Declaration of Helsinki (revised in Edinburgh in 2000). The Institutional Review Board of the Affiliated Hospital of Xiangnan University approved this study (registration number: KY–201508001).

Data Analysis

We excluded questionnaires with more than 20% data missing, which could not be supplemented by other means. The questionnaires were sorted by the principal researchers, and the data were analyzed using SPSS 25.0 statistical software. The Kolmogorov–Smirnov normality test was performed on continuous data. The normality tests showed that all continuous variables had a non-normal distribution. In this study, continuous variables were described by median (interquartile range, IQR), and categorical variables were described as frequency and percentage and presented in bar charts. The statistical analysis was conducted using the Mann–Whitney U-test and Kruskal–Wallis H-test, and Spearman correlation was used to analyze the association with the main independent variables. The significance level was set as 0.05.

Results

General Characteristics of the Sample

In total, 328 questionnaires were collected; after excluding 56 questionnaires, 272 valid questionnaires were obtained. Figure 1 shows the distribution of the sample RACFs.

Figure 1 Distribution of the research residential aged care facilities in the Hunan Province.

Finally, this study analyzed 152 (55.88%) public and 120 (44.12%) private RACFs. Most were in rural areas (n = 150; 55.15%), obtained the Practice Certificate of Social Welfare Facilities (n = 234; 86.03%), and required nursing staff to work with certificates (n = 239; 87.87%). The operation duration of RACFs was 1 to 62 (11; IQR: 12) years. The number of beds in RACFs ranged from 10 to 10,000 (70; IQR: 97), among which 81 (29.78%) had 10 to 49 beds, 86 (31.62%) had 50 to 99 beds, 60 (22.06%) had 100 to 199 beds, and 45 (16.54%) had 200 or more beds. During the survey period, the number of older adult residents was between four and 7216 (40, IQR: 54), the number of nursing staff was between one and 4000 (7, IQR: 14), and the nursing hours per resident day (HPRD) ranged between 0.16 and 16.84 (1.48, IQR: 1.64; calculated according to nursing staff working 8h per day; Table 1, Figure 2).

Table 1 Demographic Characteristics of the Research RACFs (n=272)

Figure 2 General characteristics of the sample (median, n=272).

Prevalence of Nursing Adverse Events, Risk Management, and Risk Early Warning in RACFs

We found that an average of five (15) adverse events occurred in RACFs in 2019, with falls (2, IQR: 5) and pressure sores (0, IQR: 1.75) at the highest level, and accidental death (0, IQR: 0) and food poisoning (0, IQR: 0) at the lowest level (Figure 3).

Figure 3 The prevalence of nursing adverse events in 2019.

This study revealed that the facility’s risk management score was at an acceptable and relatively desirable level. The total average score of risk management in RACFs was 4.72 (0.98) out of 5. The score of environment management and personnel management dimensions was the highest at 4.75 (1), followed by safety management at 4.70 (1), while the score of service management dimension was the lowest at 4.60 (1; See Table 2).

Table 2 Risk Management in Residential Aged Care Facilities (n=272)

Regarding the nursing staff training of RACFs, as shown in Graph A of Figure 4, more than 90% had trained their nurses on nursing knowledge and rehabilitation skills, but only 72.79% on relevant ethical and legal considerations. In terms of contingency planning for public safety incidents, most RACFs had made contingency plans for fire (96.31%) and food poisoning (92.28%), but only 66.91% and 56.99% had formulated contingency plans for gas poisoning and drowning, respectively, as shown in Graph B of Figure 4. Regarding contingency planning for age-related safety incidents, Graph C of Figure 4 shows that 95.96% and 90.81% of RACFs had made contingency plans for falls and sudden changes of diseases, respectively. However, only 73.53% reported suicide contingency plans. More than 90% regularly organized nursing staff to conduct emergency drills (92.28%), had set up emergency teams (91.91%), established a safety emergency monitoring system (92.65%), and quickly coordinated and distributed emergency materials and equipment according to critical situations (94.12%). However, only 84.56% provided pre-hospital first aid for older adults, as shown in Graph D of Figure 4.

Figure 4 Risk early warning status in residential aged care facilities.

Associated Factors of Risk Management in RACFs

The Mann–Whitney U-test or the Kruskal–Wallis H-test showed that the following factors of RACFs favor risk management: in urban areas, obtaining the Practice Certificate of Social Welfare Facilities, with more than 50 beds, providing higher HRPD, requiring nursing staff to work with certificates, and paying higher salary (all p < 0.05; Table 3).

Table 3 Differences in Risk Management Scores with Participants’ Demographic Characteristics (Score, n = 272)

Relationships Between Risk Management Scores, Frequency of Nursing Adverse Events, and Risk Early Warning Scores

In this study, frequency of adverse events was weakly negatively correlated with the risk management scores (rs = −0.208, p < 0.01), as well as environment (rs =−0.151, p < 0.05), personnel (rs = −0.212, p < 0.01), service (rs = −0. 205, p < 0.01), and safety management (rs = −0.204, p < 0.01). Moreover, risk early warning scores was moderately positively correlated with the risk management scores (rs =0.516, p < 0.01), as well as environment (rs =0.434, p < 0.01), personnel (rs =0.461, p < 0.01), service (rs = 0.497, p < 0.01), safety management (rs =0.511, p < 0.01) scores, as shown in Table 4.

Table 4 Correlations Between Risk Management Scores, Frequency of Adverse Events, and Risk Early Warning Scores (Rs, N = 272)

Discussion

We found RACFs experienced an average of five (15) adverse events in 2019, which is significantly higher than that in Italian nursing homes.40 This may be related to the differences in survey methods (field survey vs online survey), the healthcare environment, and the time period (one year vs six weeks). However, these numbers may be significantly lower than actual incidents. Generally, most RACFs try to underreport any adverse events, which might affect their quality ratings and reputation in the community.41,42 Kapoor43 reported that the prevalence of adverse events in patients transitioning from hospitals to long-term care facilities was 37.3%. According to a study by Health and Human Services Inspector General Office, about 22% of older adults experienced at least one adverse event during their stay in the nursing home, and the total cost leading to rehospitalization was about USD 208 million.44 The trend of adverse events is similar to those in other countries;43,45,46 for instance, falls, pressure sores, psychological adverse events, falling out of bed (or chair), choking, and aspiration or swallowing foreign bodies were common adverse events in RACF settings. These may have potentially negative impacts on residents’ health outcomes, quality of life, and mental health. Thus, RACFs and healthcare professionals need to make effective resident-safety interventions to reduce adverse events.

Although the facility’s risk management score is at an acceptable and relatively desirable level, it might be reasonable to increase it to a more optimal level, specifically in the safety management and service management domains. In RACFs, numerous quality and safety issues, such as minor mistakes, missing and rushed care, and disruptions in care, can negatively impact the physical and mental health of older people over time.47,48 A significant amount of previous research49,50 has shown that RACFs need to establish a safety culture and encourage active and unpunished error reporting of nursing adverse events to reduce potential harm. In China, the safety management and service quality provided by RACFs are highly variable due to weak quality regulation, insufficient inspections, and poor enforcement of rules.51 To ensure quality care in supporting independence, autonomy, dignity, and safety of the older adults, it is imperative to establish an effective long-term care regulatory framework and quality assurance system.

Regarding staff training in RACFs, only about 70% had trained nursing staff regarding relevant ethical and legal considerations. Legal knowledge is often neglected in the training and education of nursing staff.52 Typically, geriatric care training conducted by RACFs focuses on the daily-life care of older individuals and basic nursing knowledge, such as diet care, oral cleaning, pressure ulcer prevention, and use of crutches and wheelchairs. However, nursing staff in RACFs shoulder complex ethical responsibilities, take ethical care actions, and balance moral dilemmas and legal issues related to health and safety.53 Thus, it is important to emphasize nursing staff’s understanding of the legal and ethical framework for geriatric care to provide the better care.

Regarding contingency plans, this survey showed that 30% to 40% of the facilities did not have contingency plans for suicide, electric shock, gas poisoning, and drowning, although China’s Ministry of Civil Affairs issued the Administrative Measures for Residential Aged Care Facilities, which requires RACFs to formulate contingency plans for natural disasters, accidents, public health incidents, and social security incidents.54 Fisher et al55 stated that contingency planning is a continuous process, which demonstrates personnel responsibilities, response time, corresponding strategies as well as resource preparation before, during, and after the emergency. Moreover, evidence has shown that emergency preparedness and disaster response plan is important in the emergency system and guide timely and effective emergency rescue.56,57 Our findings demonstrate the need for administrative authorities (eg the Ministry of Civil Affairs and the Central Politics and Law Commission) to strengthen the monitoring of RACFs’ emergency system to effectively address emergencies and minimize losses.

Regarding emergency management, only 84.56% of RACFs utilized pre-hospital first aid for older adults. In China, to minimize daily operating costs, some small RACFs choose the sharing mode to cooperate with nearby community health service centers and hospitals to address residents’ healthcare treatment.58 However, due to the shortage of qualified employees and equipment, these facilities may experience potential hazards such as missing the prime time for first aid and causing serious life-threatening consequences. Hence, we recommend RACFs make an extensive and sustained effort to develop manpower (eg in-service training, simulation of incident scenes, and operational training)59 and equipment (eg alarm device, upgraded technologies, necessary emergency rescue equipment, and ambulances)60 to ensure residents’ safety and meet the needs of first aid and high-quality medical services.61

The findings revealed that urban RACFs scored significantly higher than those in rural areas. The common assumption is that those in rural areas with higher poverty rates, limited support staff, and lower availability of health promotion and disease projects62–65 experience more difficulty in creating a physical environment suitable for the older adults, recruiting and training nursing staff with sufficient competences, and establishing effective risk management programs. Moreover, RACFs that have obtained the Practice Certificate of Social Welfare Facilities are better at risk management. In China, they can successfully obtain it only when they meet the basic standards of civil affairs departments for living places, and outdoor activity venues, start-up funds, regulations and personnel, as well as the national fire safety, sanitation, and epidemic prevention standards. Such policy promotes managers to address the risk and safety issues in the application process of the certificate and further improves the scores in all dimensions. Similarly, in the US, the Centers for Medicare and Medicaid Services and health authorities in various states have issued a series of policies (eg Five-Star Quality Rating System) to supervise and inspect nursing homes, to ensure that care delivery meets the necessary legal, quality, and safety standards.66 We identified lower risk management scores in small-scale RACFs with 10 to 49 beds than those with more than 50 beds, consistent with the previous study by Liu,67 which found the vast majority of small RACFs are equipped with little emergency medical equipment and tend to recruit rural women with poor professional knowledge and limited emergency nursing skills as nursing aids. Notably, adequate and qualified nurses are important for risk management in RACFs along with nursing HPRD (staffing) and qualified care workers’ recruitment. Typically, nursing staff are crucial human resources in reducing pressure sores, falls, use of immobilization devices, and pain and furthering residents’ better outcomes.68,69 However, the results showed that the average nursing HPRD in RACFs in China was 1.48 (1.64), significantly lower than the international standard.68,70 According to payroll-based journal data, in 2019, nursing homes reported 3.89 nursing HPRD on average (0.68 registered nurse HPRD, 0.88 licensed vocational nurse/licensed practical nurse HPRD, and 2.33 certified nursing assistant HPRD, including all administrative nurses).71 It is well known that RACFs should recruit sufficient qualified nursing staff with appropriate competencies; however, in this study, although 85.45% of the facilities required nursing staff to work with certificates, many did not recruit on-site registered nurses. Given the low nursing HPRD and limited registered nurses in Chinese RACFs, further efforts are needed to attract nursing staff to geriatric care and retain them, thereby addressing the nursing shortage. Interestingly, the risk management score of RACFs is related to nursing staff’s salary. This can be explained as RACFs offering higher salaries for a greater likelihood of better financial resources and higher employee retention. Furthermore, it is well documented that nurses with relatively low incomes are more likely to report higher job burnout,72 lower prestige, less professional autonomy,73 and lower work creativity, which may lead to less empathic care delivery, and missing or rushed care. A national survey of 239,312 employees in China RACF settings also revealed that the labor (taking care of six to seven disabled or semi-disabled older adults on average every day) and payment (<3000 RMB) for nursing staff were significantly unbalanced, which leads to a continual decline in their efforts and attitudes toward nursing development and career retention.74 Recommendations include strengthening national route inspections and enforcement provisions, improving care workers’ performance, establishing a reward and punishment mechanism to help risk management of RACFs, specifically for those in a rural area with limited healthcare resources.

As expected, the lower frequency of adverse events was correlated with better risk management, consistent with the results of Lawati et al75 and Smith et al,76 which suggest that successful risk management programs could facilitate creating and maintaining safe systems of care, improving human performance, and reducing adverse events. However, the correlation was not as high as expected,77,78 with the figure of −0.208 indicating only a weak correlation between adverse event frequency and risk management. It is unclear whether this is related to confounding factors’ effect such as RACF characteristics and the validity of the measurement tool. More evidence is needed linking risk management to the incidence of adverse events. Additionally, RACFs’ risk early warning favors risk management, which is supported by previous studies.79,80 Catalyst11 believed that education and training, contingency plans, response, and mitigation are essential components for all healthcare risk management programs. Prevention beats remediation, with an emphasis on “early warning, risk reduction and managing uncertainties around older residents.”81 With the continuous development of artificial intelligence (AI), applying AI algorithms such as machine learning, and back propagation neural networks in constructing nursing risk early warning models can overcome the shortcomings of traditional integrated prediction methods and provide managers with decision-making information.82 RACFs could consider integrating innovative IT across risk early warning and management to address the quality and safety concerns of stakeholder groups.

The risk management and risk early warning of adverse events affect the interactions, attitudes, and practice of leaders, employees, and residents of RACFs, and can help leaders initiate quality improvement interventions. This study contributes to the existing literature on risk management and related factors. It also provides evidence for policymakers and RACF managers and staff to develop strategies to optimize care delivery in these settings.

Limitations

This study has the following limitations. First, the cross-sectional design and potential selection bias limit the results’ generalizability. Future longitudinal study can include facilities from other provinces. Second, some facilities might choose to underreport adverse events and increase effective preventive measures to protect their reputation, although we required investigators to check the relevant documents and records of the organizations to minimize data biases. Third, the psychometric properties of the questionnaires and participants’ subjective ideas need to be further explored, although the entire scales have good reliability and validity with the Cronbach’s α coefficients between 0.874–0.974 and the split-half reliabilities between 0.786–0.951. Fourth, student investigators may lack an in-depth understanding of the questionnaire items, leading to missing important information and inability to guarantee the data accuracy. Moreover, given their low prestige, respondents could skip the question or choose the best answer provided, resulting in some biases on answers. Finally, this study failed to record some types of adverse events (eg suicide, hospital admissions) and did not consider some potential variables (eg star rating, occupancy ratio of disabled older adults, the proportion of residents aged over 80 years, and profit and loss status). Multi-center and cross-sector research is needed to evaluate other factors that may be related to risk management and early warning.

Conclusion

Adverse events in RACFs are the leading causes of morbidity and mortality among residents; facilities should conduct appropriate risk management and risk early warning. The results of our study showed that the staffing level of nursing staff in RACFs in Hunan, China was low, but the frequency of nursing adverse events (eg falls, pressure sore, psychological adverse events, falling out of bed [or chair] and choking) was high. Overall, the risk management level of the surveyed RACFs was at an acceptable and relatively desirable level. Regarding risk early warning, care workers’ ethical and legal training and some important contingency plans (eg suicide, electric shock, gas poisoning, and drowning) were often overlooked. Besides, large-scale urban RACFs that obtain the Practice Certificate of Social Welfare Facilities, are equipped with adequate and qualified care workers, pay a higher salary to their employees, and gain higher risk management scores. We observed that the lower the frequency of adverse events, the better the risk management, and the better the risk early warning, the higher is the risk management score. RACFs need to adopt multi-dimensional methods and strategies to address the high prevalence of nursing adverse events and advance risk management and risk early warning, to deliver safe and high-quality care for older adults. The results could inform an empirical study of risk management in RACF settings in China to develop a blueprint for the improvement of care outcomes in the context of global rapid aging.

Ethical Approval

The Ethics Review Committee of the Affiliated Hospital of Xiangnan University approved this study (reg. no. KY–201508001).

Acknowledgments

We would like to thank all the investigators and respondents who participated in this study for their contributions and the Affiliated Hospital of Xiangnan University for its approval and support. We are grateful to Hunan Social Science Achievement Evaluation Committee for their financial and material support.

Funding

This study was supported by the Hunan Social Science Achievement Evaluation Committee (grant no. XSP20YBC164), 2018 key scientific research projects of Hunan Provincial Department of Education [grant no. 18A459], the General Project of Hunan Provincial Department of Education [grant no. 21C0723], the General Project of Hunan Philosophy and Social Science Fund [grant no. 17Y3A361], Hunan Clinical Medical Technology Demonstration Base[grant no. 2021sk4046].

Disclosure

The authors report no conflicts of interest in this work.

References

1. Dixon A. The United Nations decade of healthy ageing requires concerted global action. Nat Aging. 2021;1(1):2. doi:10.1038/s43587-020-00011-5

2. Population Reference Bureau (PRB). Countries with the oldest populations in the world. Washington, DC: PRB; 2020. Available from: https://www.prb.org/resources/countries–with–the–oldest–populations–in–the–world/. Accessed January 20, 2022.

3. National Bureau of Statistics. Main data of the seventh national population census; 2021. Available from: http://www.stats.gov.cn/english/PressRelease/202105/t20210510_1817185.html. Accessed February 22, 2022.

4. Han Y, He Y, Lyu J, Yu C, Bian M, Lee L. Aging in China: perspectives on public health. Glob Health J. 2020;4(1):11–17. doi:10.1016/j.glohj.2020.01.002

5. The State Council of the People’s Republic of China. China has 42,300 elderly care institutions; 2020. Available from: http://english.www.gov.cn/statecouncil/ministries/202007/29/content_WS5f215868c6d029c1c2636e90.html. Accessed May 28, 2021.

6. Karppinen H, Pitkälä KH, Kautiainen H, et al. Changes in disability, self–rated health, comorbidities and psychological wellbeing in community–dwelling 75–95–year–old cohorts over two decades in Helsinki. Scand J Prim Health Care. 2017;35(3):279–285. doi:10.1080/02813432.2017.1358855

7. Zeng Y, Hu X, Li Y, et al. The quality of caregivers for the elderly in long–term care institutions in Zhejiang Province, China. Int J Environ Res Public Health. 2019;16(12):2164. doi:10.3390/ijerph16122164

8. Konetzka RT. The challenges of improving nursing home quality. JAMA Netw Open. 2020;3(1):e1920231. doi:10.1001/jamanetworkopen.2019.20231

9. Shi C, Xu Y, Chen Y, et al. Perceptions and experiences of risk management by managers of residential aged care facilities: a qualitative study from Hunan Province, China. Int J Qual Stud Health Well–Being. 2021;16(1):1978724. doi:10.1080/17482631.2021.1978724

10. Simsekler MCE. The link between healthcare risk identification and patient safety culture. Int J Health Care Qual Assur. 2019;32(3):574–587. doi:10.1108/IJHCQA-04-2018-0098

11. Catalyst N. What is risk management in healthcare? NEJM Catal Innov Care Deliv. 2018. Available from: https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0197.

12. Wei YJ, Solberg L, Chen C, et al. Agreement of minimum data set 3.0 depression and behavioral symptoms with clinical diagnosis in a nursing home. Aging Ment Health. 2021;25(10):1897–1902. doi:10.1080/13607863.2020.1758921

13. Musa MK, Akdur G, Hanratty B, et al. Uptake and use of a minimum data set (MDS) for older people living and dying in care homes in England: a realist review protocol. BMJ Open. 2020;10(11):e040397. doi:10.1136/bmjopen-2020-040397

14. Caughey GE, Lang CE, Bray SC, et al. International and national quality and safety indicators for aged care. report for the royal commission into aged care quality and safety. Adelaide, South Australia: South Australian Health and Medical Research Institute; 2020. Available from: https://apo.org.au/sites/default/files/resource-files/2020-08/apo-nid307751.pdf. Accessed March 18, 2022.

15. Liu HC, Zhang LJ, Ping YJ, Wang L. Failure mode and effects analysis for proactive healthcare risk evaluation: a systematic literature review. J Eval Clin Pract. 2020;26(4):1320–1337. doi:10.1111/jep.13317

16. Çalış Boyacı A, Selim A. Assessment of occupational health and safety risks in a Turkish public hospital using a two-stage hesitant fuzzy linguistic approach. Environ Sci Pollut Res. 2022:1–13. doi:10.1007/s11356-021-18191-x

17. Song W, Li J, Li H, et al. Human factors risk assessment: an integrated method for improving safety in clinical use of medical devices. Appl Soft Comput. 2020;86:105918. doi:10.1016/j.asoc.2019.105918

18. Wiegmann DA, Wood JL, Cohen NT, Shappell SA. Understanding the “Swiss Cheese Model” and its application to patient safety. J Patient Saf. 2022;18(2):119–123. doi:10.1097/PTS.0000000000000810

19. Barker RO, Stocker R, Russell S, et al. Distribution of the National Early Warning Score (NEWS) in care home residents. Age Ageing. 2019;49(1):141–145. doi:10.1093/ageing/afz130

20. Li J, Li W, Liu J, Sang X. Internal operation risk analysis of private elderly care institutions based on FMEA. Soft Sci Health. 2021;35(5):50–54. doi:10.3969/j.issn.1003-2800.2021.05.012

21. Zhou X. Research on operational risk prevention mechanism of the pension institutions [Master’s thesis]. Shanghai: Shanghai University of Engineering Science; 2020.

22. Zhang X. Research on emergency management mechanism of public health emergencies in elderly care institutions –– from the perspective of PPRR. Jianghuai Trib. 2020;302(4):21. doi:10.16064/j.cnki.cn34-1003/g0.2020.04.003

23. Standardization Administration. Basic specification of service safety for senior care organization; 2019. Available from: http://xxgk.mca.gov.cn:8011/gdnps/n164/n230/n240/c12959/attr/87811.pdf. Accessed April 28, 2020.

24. Akkiyat I, Souissi N. Modelling risk management process according to ISO standard. Int J Recent Technol Eng. 2019;8:5830–5835. doi:10.35940/ijrte.B3751.078219

25. Edgar T, Manz D Research methods for cyber security. Cambridge, MA: Syngress; 2017: 106.

26. Castaldo A, Giordano A, Incalzi RA, Lusignani M. Risk factors associated with accidental falls among Italian nursing home residents: a longitudinal study (FRAILS). Geriatr Nurs. 2020;41(2):75–80. doi:10.1016/j.gerinurse.2019.06.003

27. Benzinger P, Riem S, Bauer J, et al. Risk of institutionalization following fragility fractures in older people. Osteoporosis Int. 2019;30(7):1363–1370. doi:10.1007/s00198-019-04922-x

28. Choi J-H, Kim D-H, Choi E-H, et al. Assessment of foodservice management practices according to types of elderly foodservice facilities. J Korean Soc Food Sci Nutr. 2019;48(4):469–481. doi:10.3746/jkfn.2019.48.4.469

29. Tracy LM, Singer Y, Schrale R, et al. Epidemiology of burn injury in older adults: an Australian and New Zealand perspective. Scars Burn Health. 2020;6:2059513120952336. doi:10.1177/2059513120952336

30. Hamasaki T, Hagihara A. Medical malpractice litigation related to choking accidents in older people in Japan. Gerodontology. 2021;38(1):104–112. doi:10.1111/ger.12506

31. Pu D, Yiu EML, Chan KMK. Factors associated with signs of aspiration in older adults: a prospective study. Geriatr Nurs. 2020;41(5):635–640. doi:10.1016/j.gerinurse.2020.03.019

32. Adekoya AA, Guse L. Wandering behavior from the perspectives of older adults with mild to moderate dementia in long-term care. Res Gerontol Nurs. 2019;12(5):239–247. doi:10.3928/19404921-20190522-01

33. Lavallée JF, Gray TA, Dumville J, Cullum N. Barriers and facilitators to preventing pressure ulcers in nursing home residents: a qualitative analysis informed by the theoretical domains framework. Int J Nurs Stud. 2018;82:79–89. doi:10.1016/j.ijnurstu.2017.12.015

34. Assiri GA, Shebl NA, Mahmoud MA, et al. What is the epidemiology of medication errors, error-related adverse events and risk factors for errors in adults managed in community care contexts? A systematic review of the international literature. BMJ Open. 2018;8(5):e019101. doi:10.1136/bmjopen-2017-019101

35. Abraham J, Kupfer R, Behncke A, et al. Implementation of a multicomponent intervention to prevent physical restraints in nursing homes (IMPRINT): a pragmatic cluster randomized controlled trial. Int J Nurs Stud. 2019;96:27–34. doi:10.1016/j.ijnurstu.2019.03.017

36. Moureau N. Impact and safety associated with accidental dislodgement of vascular access devices: a survey of professions, settings, and devices. J Assoc Vasc Access. 2018;23(4):203–215. doi:10.1016/j.java.2018.07.002

37. Shi C, Zhang Y, Li C, Li P, Zhu H. Using the delphi method to identify risk factors contributing to adverse events in residential aged care facilities. Risk Manag Healthc Policy. 2020;13:523–537. doi:10.2147/RMHP.S243929

38. Heale R, Twycross A. Validity and reliability in quantitative studies. Evid Based Nurs. 2015;18(3):66–67. doi:10.1136/eb-2015-102129

39. Shi C, Li C, Wang Y, Li T, Wang L, Lu C. Research progress of nursing risk early warning system in residential aged care facilities. J Xiangnan Univ. 2018;20(02):75–79. doi:10.16500/j.cnki.1673-498x.2018.02.025

40. Lombardo FL, Salvi E, Lacorte E, et al. Adverse events in Italian nursing homes during the COVID-19 epidemic: a national survey. Front Psychiatry. 2020;11:578465. doi:10.3389/fpsyt.2020.578465

41. Mascarenhas FA, Anders JC, Gelbcke FL, Lanzoni GM, Ilha P. Facilities and difficulties of health professionals regarding the adverse event reporting process. Texto Contexto Enfer. 2019;28:e20180040. doi:10.1590/1980-265X-TCE-2018-0040

42. Martin B, Reneau K. Evaluating the adverse event decision pathway: a survey of Canadian nursing leaders. J Nurs Regul. 2021;12(1):71–77. doi:10.1016/S2155-8256(21)00020-X

43. Kapoor A, Field T, Handler S, et al. Adverse events in long–term care residents transitioning from hospital back to nursing home. JAMA Intern Med. 2019;179(9):1254–1261. doi:10.1001/jamainternmed.2019.2005

44. Levinson DR. Adverse Events in Skilled Nursing Facilities: National Incidence Among Medicare Beneficiaries. Washington DC: Department of Health Human Services; 2014. Available from: https://oig.hhs.gov/oei/reports/oei–06–11–00370.pdf.

45. Ogletree AM, Mangrum R, Harris Y, et al. Omissions of care in nursing home settings: a narrative review. J Am Med Dir Assoc. 2020;21(5):604–614. doi:10.1016/j.jamda.2020.02.016

46. Trinks A, Hägglin C, Nordvall D, Rothenberg E, Wijk H. The impact of a national quality register in the analysis of risks and adverse events among older adults in nursing homes and hospital wards—a Swedish Senior Alert survey. Saf Health. 2018;4(1):1–11. doi:10.1186/s40886-018-0077-x

47. Ludlow K, Churruca K, Mumford V, et al. Unfinished care in residential aged care facilities: an integrative review. Gerontologist. 2021;61(3):e61–e74. doi:10.1093/geront/gnz145

48. Jones T, Drach-Zahavy A, Sermeus W, Willis E, Zelenikova R. Understanding missed care: definitions, measures, conceptualizations, evidence, prevalence, and challenges. In: Impacts of Rationing and Missed Nursing Care: Challenges and Solutions. Springer. 2021:9–47.

49. Yount N, Zebrak KA, Famolaro T, Sorra J, Birch R. Linking patient safety culture to quality ratings in the nursing home setting. J Appl Gerontol. 2020;1–9. doi:10.1177/0733464820969283

50. Quach ED, Kazis LE, Zhao S, et al. Safety climate associated with adverse events in nursing homes: a national VA study. J Am Med Dir Assoc. 2021;22(2):388–392. doi:10.1016/j.jamda.2020.05.028

51. Feng Z, Glinskaya E, Chen H, et al. Long-term care system for older adults in China: policy landscape, challenges, and future prospects. Lancet. 2020;396(10259):1362–1372. doi:10.1016/S0140-6736(20)32136-X

52. Xavier KF, Mmusi–Phetoe R, Thupayagale–Tshweneagae G. Nurses’ perception of ethics and legal training of nurses in Ghana. Int J Nurs Educ. 2019;11(4):191. doi:10.5958/0974-9357.2019.00117.X

53. Podgorica N, Flatscher–Thoeni M, Deufert D, Siebert U, Ganner M. A systematic review of ethical and legal issues in elder care. Nurs Ethics. 2021;28(6):895–910. doi:10.1177/0969733020921488

54. Ministry of Civil Affairs of the People’s Republic of China. Managerial approach of residential aged care facilities; 2020. Available from: http://xxgk.mca.gov.cn:8011/gdnps/pc/content.jsp?id=14589&mtype=1. Accessed November 3, 2021.

55. Fischer RJ, Halibozek EP, Walters DC. Contingency planning emergency response and safety. In: Introduction to Security. 2019:249–268. doi:10.1016/B978-0-12-805310-2.00011-1

56. Veenema TG. Disaster Nursing and Emergency Preparedness. Springer Publishing Company; 2018:561.

57. Labrague L, Hammad K, Gloe D, et al. Disaster preparedness among nurses: a systematic review of literature. Int Nurs Rev. 2018;65(1):41–53. doi:10.1111/inr.12369

58. Tong C, Wang S. On the cooperation path between community health service center and elderly care institutions –– from the perspective of simple innovation and partnership theory. Acad. 2017;6:78–87. doi:10.3969/j.issn.1002-1698.2017.06.008

59. Fatoni F, Panduragan SL, Sansuwito T, Pusporini LS. Community first aid training for disaster preparedness: a review of education content. KnE Life Sci. 2022;7(2):549–558. doi:10.18502/kls.v7i2.10356

60. Lyng J, Adelgais K, Alter R, et al. Recommended essential equipment for basic life support and advanced life support ground ambulances 2020: a joint position statement. Pediatrics. 2021;147(6):e2021051508. doi:10.1542/peds.2021-051508

61. Wei S. Emergency medicine: past, present, and future challenges. Emerg Crit Care Med. 2021;1(2):49–52. doi:10.1097/EC9.0000000000000017

62. Bouldin ED, Shaull L, Andresen EM, Edwards VJ, McGuire LC. Financial and health barriers and caregiving-related difficulties among rural and urban caregivers. J Rural Health. 2018;34(3):263–274. doi:10.1111/jrh.12273

63. Hamiduzzaman M, Kuot A, Greenhill J, Strivens E, Isaac V. Towards personalized care: factors associated with the quality of life of residents with dementia in Australian rural aged care homes. PLoS One. 2020;15(5):e0233450. doi:10.1371/journal.pone.0233450

64. probst J, Eberth JM, Crouch E. Structural urbanism contributes to poorer health outcomes for rural America. Health Aff. 2019;38(12):1976–1984. doi:10.1377/hlthaff.2019.00914

65. Henning–Smith C, Cross D, Rahman A. Challenges to admitting residents: perspectives from rural nursing home administrators and staff. Inquiry. 2021;58:1–8. doi:10.1177/00469580211005191

66. Wadhera RK, Figueroa JF, Joynt Maddox KE, Rosenbaum LS, Kazi DS, Yeh RW. Quality measure development and associated spending by the centers for medicare & medicaid services. JAMA. 2020;323(16):1614–1616. doi:10.1001/jama.2020.1816

67. Liu Y Study on living situation and influence factors of elderly people living in private and small-sized pension institutions [Master’s thesis]. Jilin: College of Humanities, Jilin Agricultural University; 2018.

68. Shin JH, Renaut RA, Reiser M, Lee JY, Tang TY. Increasing registered nurse hours per resident day for improved nursing home residents’ outcomes using a longitudinal study. Int J Environ Res Public Health. 2021;18(2):402. doi:10.3390/ijerph18020402

69. Boscart VM, Sidani S, Poss J, et al. The associations between staffing hours and quality of care indicators in long-term care. BMC Health Serv Res. 2018;18(1):750. doi:10.1186/s12913-018-3552-5

70. Harrington C, Chapman S, Halifax E, Dellefield ME, Montgomery A. Time to ensure sufficient nursing home staffing and eliminate inequities in care. J Gerontol Geriatr Med. 2021;7:099. doi:10.24966/GGM-8662/100099

71. Harrington C, Dellefield ME, Halifax E, Fleming ML, Bakerjian D. Appropriate nurse staffing levels for U.S. nursing homes. Health Serv Insights. 2020;13:1178632920934785. doi:10.1177/1178632920934785

72. Zhang W, Miao R, Tang J, et al. Burnout in nurses working in China: a national questionnaire survey. Int J Nurs Pract. 2021;27(6):e12908. doi:10.1111/ijn.12908

73. ko YK, Jeong SH, Yu S. Job autonomy, perceptions of organizational policy, and the safety performance of nurses. Int J Nurs Pract. 2018;24(6):e12696. doi:10.1111/ijn.12696

74. Ministry of Civil Affairs of the People’s Republic of China. Research on the current situation, problems and Countermeasures of employees in pension institutions in China –– based on the data analysis of 239312 employees in the national pension institution data direct reporting system. Chin Civ Aff. 2015;(17):29–31. doi:10.3969/j.issn.1002-4441.2015.17.010

75. Lawati MHA, Dennis S, Short SD, Abdulhadi NN. Patient safety and safety culture in primary health care: a systematic review. BMC Fam Pract. 2018;19(1):104. doi:10.1186/s12875-018-0793-7

76. Smith AF, Plunkett E. People, systems and safety: resilience and excellence in healthcare practice. Anaesthesia. 2019;74(4):508–517. doi:10.1111/anae.14519

77. Kakemam E, Sheikhy-Chaman M. The relationship between patient safety culture and adverse events among nurses in Tehran teaching hospitals in 2019. Avicenna J Nurs Midwifery Care. 2020;28(4):20–31. doi:10.30699/ajnmc.28.4.20

78. Najjar S, Nafouri N, Vanhaecht K, Euwema M. The relationship between patient safety culture and adverse events: a study in Palestinian hospitals. Saf Health. 2015;1(1):1–9. doi:10.1186/s40886-015-0008-z

79. Chen Y, Zheng W, Li W, Huang Y. Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recognit Lett. 2021;144:1–5. doi:10.1016/j.patrec.2021.01.008

80. McGowan J, Wojahn A, Nicolini JR. Risk management event evaluation and responsibilities. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2022. Available from https://www.ncbi.nlm.nih.gov/books/NBK559326/. Accessed March 18, 2022.

81. Choi SJ, Johnson ME, Lehmann CU. Data breach remediation efforts and their implications for hospital quality. Health Serv Res. 2019;54(5):971–980. doi:10.1111/1475-6773.13203

82. Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform. 2020;8(7):e18599. doi:10.2196/18599

Posted on

People with elevated blood pressure upon standing more likely to have risk for cardiovascular events

People with elevated blood pressure upon standing more likely to have risk for cardiovascular events

Among young and middle-aged adults with high blood pressure, a substantial rise in blood pressure upon standing may identify those with a higher risk of serious cardiovascular events, such as heart attack and stroke, according to new research published today in the American Heart Association’s peer-reviewed journal Hypertension.

This finding may warrant starting blood-pressure-lowering treatment including medicines earlier in patients with exaggerated blood pressure response to standing.”


Paolo Palatini, M.D., lead author of the study and professor of internal medicine at the University of Padova in Padova, Italy

Nearly half of Americans and about 40% of people worldwide have high blood pressure, considered to be the world’s leading preventable cause of death. According to the American Heart Association’s 2022 heart disease statistics, people with hypertension in mid-life are five times more likely to have impaired cognitive function and twice as likely to experience reduced executive function, dementia and Alzheimer’s disease.

Typically, systolic (top number) blood pressure falls slightly upon standing up. In this study, researchers assessed whether the opposite response – a significant rise in systolic blood pressure upon standing – is a risk factor for heart attack and other serious cardiovascular events.

The investigators evaluated 1,207 people who were part of the HARVEST study, a prospective study that began in Italy in 1990 and included adults ages 18-45 years old with untreated stage 1 hypertension. Stage 1 hypertension was defined as systolic blood pressure of 140-159 mm Hg and/or diastolic BP 90-100 mm Hg. None had taken blood pressure-lowering medication prior to the study, and all were initially estimated at low risk for major cardiovascular events based on their lifestyle and medical history (no diabetes, renal impairment or other cardiovascular diseases). At enrollment, participants were an average age of 33 years, 72% were men, and all were white.

At enrollment, six blood pressure measurements for each participant were taken in various physical positions, including when lying down and after standing up. The 120 participants with the highest rise (top 10%) in blood pressure upon standing averaged an 11.4 mm Hg increase; all increases in this group were greater than 6.5 mm Hg. The remaining participants averaged a 3.8 mm Hg fall in systolic blood pressure upon standing.

The researchers compared heart disease risk factors, laboratory measures and the occurrence of major cardiovascular events (heart attack, heart-related chest pain, stroke, aneurysm of the aortic artery, clogged peripheral arteries) and chronic kidney disease among participants in the two groups. In some analyses, the development of atrial fibrillation, an arrhythmia that is a major risk factor for stroke, was also noted. Results were adjusted for age, gender, parental history of heart disease, and several lifestyle factors and measurements taken during study enrollment.

During an average 17-year follow-up 105 major cardiovascular events occurred. The most common were heart attack, heart-related chest pain and stroke.

People in the group with top 10% rise in blood pressure:

  • were almost twice as likely as other participants to experience a major cardiovascular event;
  • did not generally have a higher risk profile for cardiovascular events during their initial evaluation (outside of the exaggerated blood pressure response to standing);
  • were more likely to be smokers (32.1% vs. 19.9% in the non-rising group), yet physical activity levels were comparable, and they were not more likely to be overweight or obese, and no more likely to have a family history of cardiovascular events;
  • had more favorable cholesterol levels (lower total cholesterol and higher high-density-lipoprotein cholesterol);
  • had lower systolic blood pressure when lying down than the other group (140.5 mm Hg vs. 146.0 mm Hg, respectively), yet blood pressure measures were higher when taken over 24 hours.

After adjusting for average blood pressure taken over 24 hours, an exaggerated blood pressure response to standing remained an independent predictor of adverse heart events or stroke.

“The results of the study confirmed our initial hypothesis – a pronounced increase in blood pressure from lying to standing could be prognostically important in young people with high blood pressure. We were rather surprised that even a relatively small increase in standing blood pressure (6-7 mm Hg) was predictive of major cardiac events in the long run,” said Palatini.

In a subset of 630 participants who had stress hormones measured from 24-hour urine samples, the epinephrine/creatinine ratio was higher in the people with a rise in standing blood pressure compared to those whose standing blood pressure did not rise (118.4 nmol/mol vs. 77.0 nmol/mol, respectively).

“Epinephrine levels are an estimate of the global effect of stressful stimuli over the 24 hours. This suggests that those with the highest blood pressure when standing may have an increased sympathetic response [the fight-or-flight response] to stressors,” said Palatini. “Overall, this causes an increase in average blood pressure.”

“The findings suggest that blood pressure upon standing should be measured in order to tailor treatment for patients with high blood pressure, and potentially, a more aggressive approach to lifestyle changes and blood-pressure-lowering therapy may be considered for people with an elevated [hyperreactor] blood pressure response to standing,” he said.

Results from this study may not be generalizable to people from other ethnic or racial groups since all study participants reported white race/ethnicity. In addition, there were not enough women in the sample to analyze whether the association between rising standing blood pressure and adverse heart events was different among men and women. Because of the relatively small number of major adverse cardiac events in this sample of young people, the results need to be confirmed in larger studies.

Source:

Journal reference:

Palatini, P., et al. (2022) Blood Pressure Hyperreactivity to Standing: a Predictor of Adverse Outcome in Young Hypertensive Patients. Hypertension. doi.org/10.1161/HYPERTENSIONAHA121.18579.

Posted on

New cancer diagnosis associated with risk for fatal, nonfatal cardiovascular events

hemonc today logo

March 15, 2022

3 min read

Disclosures:
Paterson reports no relevant financial disclosures. Please see the study for all other authors’ relevant disclosures. Ohtsu and colleagues report no relevant financial disclosures.


We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com.

New cancer diagnosis appeared associated with increased risk for cardiovascular death, as well as incident heart failure, stroke or pulmonary embolism, according to a retrospective cohort study published in JACC: CardioOncology.

“This risk persisted to at least 7 years from cancer diagnosis and appeared most pronounced in patients with hematologic, gastrointestinal, genitourinary and thoracic malignancies,” D. Ian Paterson, MD, FRCPC, professor of medicine in the division of cardiology, director of the Edmonton Cardio-Oncology Program and director of academic and research cardiac MRI at University of Alberta, told Healio.

HRs among patients with vs. without new cancer diagnosis
Data derived from Paterson DI, et al. JACC CardioOncol. 2022;doi:10.1016/j.jaccao.2022.01.100.

Background and methodology

Paterson and colleagues pursued the research because, despite the knowledge that patients with cancer and cancer survivors are at increased risk for heart failure, previous data conflicted regarding long-term risk for other cardiovascular events, as well as risk according to cancer site.

“Population studies to date have largely evaluated the risk [for] cardiovascular disease — and usually only heart failure — in patients with breast cancer,” Paterson said. “We performed a comprehensive analysis of the risk [for] incident cardiovascular disease in patients with a new cancer diagnosis of any type.”

D. Ian Paterson, MD, FRCPC

D. Ian Paterson

The analysis included 4,519,243 adults who resided in Alberta, Canada, from April 2007 to December 2018. Among them, 224,016 (median age, 56 years; range, 43-67; 56.8% women) had a new cancer diagnosis and 4,295,227 (median age, 34 years; range, 23-49; 48.5% women) comprised the control population.

Paterson and colleagues used time-to-event survival models, after adjusting for comorbidities and sociodemographic factors, to compare the two cohorts with respect to risk for subsequent cardiovascular events, which included cardiovascular mortality, myocardial infarction, stroke, heart failure and pulmonary embolism.

Determining the impact of new cancer diagnosis on risk for fatal and nonfatal cardiovascular events served as the primary outcome.

Key findings

At median follow-up of 11.8 years, results showed 73,360 cardiovascular deaths and 470,481 nonfatal cardiovascular events. After adjustment, researchers reported participants with cancer demonstrated the following HRs compared with participants without cancer:

1.33 (95% CI, 1.29-1.37) for cardiovascular mortality;

1.01 (95% CI, 0.97-1.05) for myocardial infarction;

1.44 (95% CI, 1.41-1.47) for stroke;

1.62 (95% CI, 1.59-1.65) for heart failure; and

3.43 (95% CI, 3.37-3.5) for pulmonary embolism.

Additionally, patients with genitourinary, gastrointestinal, thoracic, neurologic and hematologic malignancies demonstrated the highest cardiovascular risk.

“We were surprised that the risk [for] incident cardiovascular disease remained elevated in patients with cancer, even after fully adjusted risk modeling,” Paterson told Healio. “This suggests that the cancer itself, cancer therapies and/or other less traditional risk factors, such as physical activity and body composition, may have also contributed to cardiovascular risk.”

Implications

Paterson and colleagues wrote that future studies should investigate other potential contributors to cardiovascular risk, including cancer therapies and emerging risk factors for cardiotoxicity.

“We would like to identify effective intervention strategies to mitigate cardiovascular risk in patients with cancer, especially in the higher-risk cancer types (eg, hematologic),” Paterson said.

Paterson noted that as life expectancy of patients with cancer increases, so does their likelihood of developing other illnesses after diagnosis, necessitating a more collaborative approach to their health care. The authors of a corresponding editorial concurred.

“Perhaps the lesson we need to learn from [this study] is that it is time for cardiology and oncology to collaborate in order to travel upstream and build a powerhouse to generate information from the new flow of data efficiently,” Hiroshi Ohtsu, MS, manager of clinical epidemiology and director of JCRAC data center at National Center for Global Health and Medicine, Center for Clinical Sciences in Japan, and colleagues wrote.

“Cardiology and oncology need to collaborate to launch and successfully execute projects to establish new techniques to use real-world data for real-world evidence,” they added.

Reference s :

For more information:

D. Ian Paterson, MD, FRCPC, can be reached at Division of Cardiology, University of Alberta, 8440 112 St., 2C2.43 WCM, Edmonton, Alberta T6G2B7, Canada; email: ip3@ualberta.ca.

Posted on

Trajectories of prescription opioid dose and risk of opioid-related adverse events among older Medicare beneficiaries in the United States: A nested case–control study

Trajectories of prescription opioid dose and risk of opioid-related adverse events among older Medicare beneficiaries in the United States: A nested case–control study

Abstract

Methods and findings

We conducted a nested case–control study within a cohort of older (≥65 years) patients diagnosed with CNCP who were new users of prescription opioids, assembled using a 5% national random sample of Medicare beneficiaries from 2011 to 2018. From the cohort with a mean follow-up of 2.3 years, we identified 3,103 incident ORAE cases with ≥1 opioid prescription in 6 months preceding the event, and 3,103 controls matched on sex, age, and time since opioid initiation. Key exposure was trajectories of prescribed opioid morphine milligram equivalent (MME) daily dosage over 6 months before the incident ORAE or matched controls. Among the cases and controls, 2,192 (70.6%) were women, and the mean (SD) age was 77.1 (7.1) years. Four prescribed opioid trajectories before the incident ORAE diagnosis or matched date emerged: gradual dose discontinuation (from ≤3 to 0 daily MME, 1,456 [23.5%]), gradual dose increase (from 0 to >3 daily MME, 1,878 [30.3%]), consistent low dose (between 3 and 5 daily MME, 1,510 [24.3%]), and consistent moderate dose (>20 daily MME, 1,362 [22.0%]). Few older patients (<5%) were prescribed a mean daily dose of ≥90 daily MME during 6 months before diagnosis or matched date. Patients with gradual dose discontinuation versus those with a consistent low dose, moderate dose, and increase dose were more likely to be younger (65 to 74 years), Midwest US residents, and receiving no low-income subsidy. Compared to patients with gradual dose discontinuation, those with gradual dose increase (adjusted odds ratio [aOR] = 3.4; 95% confidence interval (CI) 2.8 to 4.0; P < 0.001), consistent low dose (aOR = 3.8; 95% CI 3.2 to 4.6; P < 0.001), and consistent moderate dose (aOR = 8.5; 95% CI 6.8 to 10.7; P < 0.001) had a higher risk of ORAE, after adjustment for covariates. Our main findings remained robust in the sensitivity analysis using a cohort study with inverse probability of treatment weighting analyses. Major limitations include the limited generalizability of the study findings and lack of information on illicit opioid use, which prevents understanding the clinical dose threshold level that increases the risk of ORAE in older adults.

Introduction

The number of older adults who had medical encounters for treatment of opioid misuse, dependence, and poisoning has increased disproportionately over the past decade [1]. The opioid-related adverse events (ORAEs) defined by the United States (US) government agencies [13] contain diagnostic codes commonly used for opioid use disorder (OUD) and overdose from use of illicit opioids (i.e., heroin) or incorrect use of prescribed opioids, as well as E codes for severe adverse effects from use of heroin or correct use of prescribed opioids that lead to hospital or emergency department visits. The rate of hospital stays and emergency department visits due to ORAEs rose by 34% (from 199.3 stays to 267.6 stays per 100,000 persons) and 74% (from 44.7 visits to 77.9 visits per 100,000 persons), respectively, among older patients between 2010 and 2015 [1]. A study of a commercially insured population also indicated a marked increase (14.2-fold from 2.05 to 31.12 per 10,000 persons) in the incidence of OUD or overdose among older adults aged 65 and older between 2006 and 2016 [4]. These alarming statistics have prompted questions of what might have predisposed older patients to be at risk for ORAEs [2].

Of the known risk factors, prescription opioid dose is one of the strong predictors of ORAEs [59]. Studies of nonelderly or mixed populations of young and older populations showed that use of prescription opioids at a dose of 90 morphine milligram equivalent (MME) or above per day was associated with an increased risk for opioid overdose and deaths [1013]. Built on this evidence, the 2016 Centers for Disease Control and Prevention (CDC)’s Guidance for Opioid Prescribing for Chronic Pain recommends avoidance of prescribing daily opioid doses at 90 mg MME or greater [14]. Since then, some medical societies’ guidelines [15], state regulations [16], and health insurance payers [17] have adapted the CDC-recommended dose threshold and limited prescribing doses of opioids to 90 mg MME per day [18,19].

The Centers for Medicare and Medicaid Services (CMS), the largest insurer of older adults in the US, has utilized 90 daily MME as one of the criteria for flagging high-risk beneficiaries for OUD or overdose and required its Part D plan sponsors to adjudicate the appropriateness of opioid prescribing of these high-risk patients [20]. However, our prior study has shown that the CMS’s opioid overutilization criteria missed the majority of patients with OUD or overdose and flagged more than half of opioid prescription users as high risk who were not diagnosed with OUD or overdose [21]. The finding challenges the use of 90 daily MME as a risky threshold for Medicare beneficiaries, the vast majority of whom are aged 65 years or older. Literature has primarily focused on establishing the high-risk prescription opioid dose thresholds using healthcare data among young (aged 18 to 64) individuals, which may not be applicable to older individuals who may have different thresholds for adverse opioid outcomes due to declined renal and hepatic function, multiple comorbidities, and polypharmacy.

Because of the time-varying nature of prescription opioid use, assessing the progression of opioid dose toward ORAEs is important to understand whether there are typical and atypical opioid dose patterns emerged before the adverse events. Thus, the present study aims to (1) examine trajectories of prescription opioid dose preceding the incident medical encounter for ORAEs; and (2) quantify the association between identified trajectories of prescribed opioid dose and risk of ORAEs among Medicare older adults with chronic noncancer pain (CNCP).

Methods

Study setting and cohort

Using the pharmacy and medical claims data from a 5% national random sample of Medicare beneficiaries from the US CMS [22], we conducted a nested case–control study design within a cohort of older (≥65 years) beneficiaries enrolled in US Medicare who were new opioid users and had a diagnosis of CNCP between January 1, 2011 and December 31, 2018. We chose a nested case–control study design because such design allows for examination of prescription opioid use in a time window (i.e., 6 months in this study) preceding ORAE outcome as a risk factor [23]. Studying prescription opioid use before ORAE is important because of the time-varying nature of opioid use, with a larger effect expected from opioid exposure preceding an ORAE compared to distant opioid exposure during the early months of opioid initiation.

Cohort members were older adults aged 65 or older and naïve to opioids for 12 months prior to the date of their first dispensed opioid prescription (i.e., cohort entry). During the 12-month pre-cohort entry, patients were also required to have the following: (1) continuous enrollment in Medicare Parts A (inpatient), B (outpatient provider), and D (prescription drug) without insurance coverage from Health Maintenance Organization or employer-sponsored plans; and (2) primary or secondary diagnosis of a chronic pain condition (S1 Table) to ensure a relatively homogeneous cohort regarding pain conditions. We excluded patients who received a cancer diagnosis, hospice care, or palliative care, as well as those with a history of an ORAE encounter during the year before cohort entry. Patients were followed until an ORAE event, a cancer diagnosis, receiving palliative or hospice care, death, Medicare disenrollment, or study end (i.e., December 31, 2018), whichever came first. The University of Florida Institutional Review Board approved the study with a waiver of informed consent and HIPAA authorization because of minimal risk and lack of feasibility to contact Medicare patients. Data analyses were performed as per a prespecified protocol between January and December 2020 (S1 Text). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines (S1 STROBE Checklist).

Selection of cases and controls

In the cohort of opioid initiators, we identified cases of ORAE using the International Classification of Diseases, Ninth or Tenth Revision, Clinical Modification (ICD-9 or ICD-10 CM) Codes recorded in inpatient or outpatient encounter claims during follow-up. These codes have been used by the CDC and Agency for Healthcare Research and Quality (AHRQ) to define ORAEs, including opioid misuse (ICD-9 codes: 305.50–305.52), opioid dependence and unspecified use (304.00–304.02, 304.70–304.72), opioid poisoning (965.00–965.02, 965.09, 970.1, E850.0-E850.2), and adverse effects of opioids (E-codes: E935.0-E935.2, E940.1) [1,3]. We also used the ICD-9-CM to ICD-10-CM code conversion of ORAEs provided by AHRQ (S1 Table) [24]. Opioid misuse and dependence are commonly grouped as OUD, and opioid poisoning is also known as OD. The adverse effects of opioids defined by the E codes include any severe reactions to illicit opioids (i.e., heroin) or correct use of prescribed opioids (i.e., methadone, opioid antagonists, and other opioids) that lead to an emergency department or hospital visit. Consistent with prior studies [4,25], when identifying patients with incident ORAE encounter, we excluded ICD-10-CM codes that indicated “in remission” or “subsequent encounter.” The date of the first ORAE encounter represented the “index” date.

To emulate clinical practices where a limited time window of patient history is often available for routine clinical assessment, we focused on trajectories of opioid dose during the 6 months before the incident ORAE encounter for cases. To measure opioid dose trajectories, cases were required to have at least one prescription opioid fill in the 6 months before the incident ORAE. This requirement excluded cases who had an incident ORAE within 6 months after opioid initiation and who had no prescription opioid dispensed in the 6 months before an incident ORAE. The rationale for requiring one or more opioid prescriptions is to (1) focus on cases who visit clinics to obtain prescription opioids and have a chance of being evaluated by doctors for risk of ORAE before its onset; and (2) to reduce confounding by illicit opioid use, which likely occurs among cases who had no prescription opioid use before ORAE [4]. For each case, we used an incidence density sampling approach to randomly select one control person prescribed opioids who was at risk but had not experienced an ORAE encounter by the index date of the case event. In other words, controls also had at least one dispensed opioid prescription in the 6 months before their matched date. We matched controls to cases on age, sex, and time (in days) since cohort entry because these 3 matching variables provided a sufficient number of controls for matching. We adjusted for other nonmatched confounders later in multivariable regression models.

Prescription opioid and its dose conversion

Prescription opioids approved for use in the US between 2011 and 2018 were captured from the Medicare Part D Prescription Event files based on the National Drug Code (S2 Table). We excluded (1) injectable opioids because they are primarily used in inpatient settings where prescription dispensing data are not available; and (2) buprenorphine sublingual tablets and buprenorphine–naloxone combinations because they are indicated for treatment of OUD or OD.

The dose of each prescription opioid filled during 6 months before the index date was converted to an MME dose based on a standard formula—the quantity of opioids dispensed per day multiplied by the strength and the MME conversion factor [26]. We then calculated the mean daily MME dose in each month by adding the MMEs of all days with prescribed opioids dispensed during the month and then dividing by 30 days. Sensitivity analysis was conducted with the mean daily MME dose calculated at biweekly intervals.

Statistical analysis

We used a group-based trajectory model (GBTM) to identify clusters of patients who followed a similar longitudinal pattern for prescribed opioid dose during the 6 months preceding an incident ORAE encounter for cases and matched controls. Because the monthly mean MME measure had a nonnormal distribution, to enable model convergence while retaining all MME data points, we applied natural log transformation to the MME measure and modeled log-MME as a censored normal distribution [27]. We fitted the GBTMs with 1 to 5 classes and found that a model with 4 trajectories was optimal within the recommended criteria (S3 Table) [27,28]. Characteristics, as well as the use of cautionary high-dose (defined as 50 daily MME) and risky high-dose (defined as 90 daily MME) in any given month, were described and compared across the 4 trajectory groups using the chi-squared test. Sensitivity analysis was performed by examining prescription opioid dose trajectories among cases with specific types of opioid encounters and their matched controls.

We used a multivariate conditional logistic regression to examine the association between the identified 4 trajectories of prescribed opioid dose and risk for ORAE in the study sample of cases and controls, adjusting for several potential confounders measured between 12 and 6 months before the index date. These confounders included demographics (race/ethnicity [defined based on Research Triangle Institute race code available in the Medicare claims database and grouped into 3 groups: White, Black, and other (including Hispanic, Asian, Pacific Islander, and Native American individuals), each with a sample size sufficient enough to ensure statistically reliable estimates], low-income subsidy status [yes/no], region [Northeast, Midwest, South, and West]), diagnosis of alcohol or tobacco use disorder, types of chronic pain conditions (musculoskeletal, neuropathic, and idiopathic pain), polypharmacy (defined as having ≥5 different medications, excluding opioids), select clinical comorbid conditions (including mental health disorders, diabetes, cardiovascular diseases, hypertension, pulmonary condition, kidney disease, gastrointestinal disorder, respiratory infections, injuries, and infections from nonsterile opioid injection, identified based on ICD codes defined in the Clinical Classifications Software (CCS) of the Healthcare Cost and Utilization Project) [29], and overall healthcare utilization (including any hospital stay, any emergency department visit, and any skilled nursing facility stay, identified based on medical claims). To account for opioid exposure time, we also calculated the duration of opioid use between opioid initiation and the day before the 6-month exposure measurement period for each individual. To account for the secular trend in national opioid prescribing, we also included the year of index date (2011 to 2018) as a linear variable in the model. S1 Table details diagnostic or procedure codes of the aforementioned confounders. We reported the odds ratios (ORs) and 95% confidence intervals (CIs) from the model.

We performed additional analysis by assessing trajectories of prescribed opioid doses in relation to specific types of opioid encounters (i.e., opioid misuse or dependence and opioid poisoning). We conducted a sensitivity analysis using a cohort study design with inverse probability of treatment weighting (IPTW) analysis to test the association of trajectories of prescribed opioid dose with risk of ORAEs (see details of method in S2 Text). S4 Fig showed 4 trajectories of prescribed opioid dose identified in the sensitivity analysis of a cohort design, which resemble the shapes of the 4 groups identified in the main analysis using a nested case–control design. S5 and S6 Tables showed the baseline characteristics of the 4 identified trajectory groups before and after IPTW weighting, respectively, in a cohort design. After the weighting, all characteristics were balanced between the target and reference trajectory group, except for the duration of opioid use since opioid initiation, for which statistical adjustment was performed in the final weighted Cox hazard models as a sensitivity analysis. All analyses were performed using SAS 9.4, and all tests were two-sided with statistical significance set as P < 0.05.

Results

A cohort of 380,272 Medicare older patients with CNCP (mean [SD] age, 76.2 [7.9] years; 65.4% female; and 81.4% White) were new users of prescription opioids between 2011 and 2018 (Table 1). Fig 1 describes the cohort inclusion and exclusion criteria. During the year before cohort entry, 9.0% had a diagnosis of tobacco or alcohol use disorder, 28.3% had mental health disorders, 44.7% had diabetes, and 59.3% had cardiovascular diseases. The majority (80.9%) had polypharmacy. Musculoskeletal pain was the most prevalent pain condition experienced in these older adults.

thumbnail

Table 1. Characteristics of the total cohort, cases who had an incident ORAE encounter, and matched controls of older adults with CNCP who were new users of prescription opioids between 2011 and 2018.


https://doi.org/10.1371/journal.pmed.1003947.t001

From this opioid new user cohort, we identified 6,176 patients who had an incident ORAE encounter during follow-up, yielding an incidence rate of 7.17 per 1,000 person-years. Of the 6,176 ORAE cases, 1,800 (29.1%) had the encounter within the first 6 months after prescription opioid initiation, and 1,273 (20.6%) had no prescription opioid fill in the 6 months preceding the ORAE diagnosis. This resulted in 3,103 cases with a 6-month follow-up preceding the ORAE, during which at least one opioid prescription was dispensed for dose trajectory analysis. Of 3,103 cases, 55.5% had a diagnosis of opioid misuse or dependence, 45.1% had a diagnosis of opioid poisoning, and only 0.06% had a diagnosis of adverse effects of opioids. Table 1 gives the characteristics of the 3,103 cases and 3,103 matched control patients.

We identified distinct opioid dose trajectories before an incident ORAE encounter or matched date in controls (Fig 2). Four trajectories, categorized based on their mean daily MME use of prescription opioids per month, included patients with gradual dose discontinuation (from ≤3 to 0 daily MME, consisting of 23.5% of the study sample), gradual dose increase (from 0 to >3 daily MME, 30.3%), consistent low-dose use (between 3 and 5 daily MME, 24.3%), and consistent moderate-dose use (>20 daily MME, 22.0%). The dose trajectory groups differed significantly for most demographics as well as select pain and clinical conditions (Table 2).

thumbnail

Fig 2. Trajectories of mean daily MME dose prescribed in each month for the 6 months preceding an incident encounter of ORAE for cases or matched controls of older adults.

Lines represent types of dose trajectory group, and for each line, each point represents the mean daily MME of prescription opioids per month. The scale on the left and right side of the figure is the natural logarithm of MME and actual MME, respectively. The error bar represents the standard deviation of the natural logarithm transformed MME. MME, morphine milligram equivalent; ORAE, opioid-related adverse event.


https://doi.org/10.1371/journal.pmed.1003947.g002

Compared with controls, cases had a lower proportion of patients with gradual dose discontinuation (11.8% versus 35.2%, P < 0.001) but a higher proportion with consistent moderate dose (31.6% versus 12.3%, P < 0.001) (Table 3). Overall, only 2.4% of older patients were prescribed a mean daily dose of 90 mg MME or more during any month of the 6 months before the index date. Sensitivity analysis with mean daily MME use of prescription opioids calculated at the biweekly interval showed similar dose trajectories (S1 Fig). We found similar prescription opioid dose trajectories among cases with opioid misuse or dependence encounters and their controls (S2 Fig) and among cases with opioid poisoning encounters and their controls (S3 Fig).

In adjusted multivariable conditional logistic regression analysis, prescription opioid dose trajectories were independently associated with risk for an ORAE encounter (Table 4). Compared to patients with gradual dose discontinuation, those with graduate dose increase had a 3.4-fold (95% CI 2.8 to 4.0; P < 0.001), those with consistent low dose had a 3.8-fold (95% CI 3.2 to 4.6; P < 0.001), and those with consistent moderate dose had an 8.5-fold (95% CI 6.8 to 10.7; P < 0.001) increased risk for having an incident ORAE encounter, after adjustment for covariates. Stratification analysis by specific types of opioid encounters showed similar results, with the gradual dose increase, consistent low dose, and consistent moderate dose group having an increased risk for opioid misuse/dependence or poisoning when compared to the group with decreasing dose trajectory (S4 Table).

Our main findings remained robust in the sensitivity analysis using a cohort study design. The increased risk of ORAE among patients with the gradual dose increase (adjusted hazard ratio [HR] = 4.4; 95% CI 3.8 to 5.1; P < 0.001), consistent low dose (aHR = 1.9; 95% CI 1.6 to 2.1; P < 0.001), and consistent moderate dose (aHR = 5.7; 95% CI 5.0 to 6.5; P < 0.001), as compared to those with the gradual dose discontinuation, persisted in a cohort design (S7 Table). Further stratification by the duration of follow-up revealed a higher risk of ORAE in the earlier months (i.e., first or second month) of the follow-up defined in the cohort design as a sensitivity analysis (S7 Table).

Discussion

In this sample of older Medicare beneficiaries, we found that 4 trajectories of opioid dose prescribed during 6 months before the incident ORAE diagnosis or matched date emerged: gradual dose discontinuation (from ≤3 to 0 daily MME), gradual dose increase (from 0 to >3 daily MME), consistent low dose (between 3 and 5 daily MME), and consistent moderate dose (>20 daily MME). Overall, few older patients (<5%) were prescribed a mean daily dose of ≥90 daily MME before diagnosis or matched date. Compared to older patients with gradual dose discontinuation, those with gradual dose increase, consistent low dose, and consistent moderate dose use group had a higher risk of ORAE. The findings were consistent in a sensitivity analysis using a cohort design.

Across the 4 identified groups, we observed a low dose range (mean daily dose between 0 to 20 daily MME) of prescribed opioids and less than 5% of older patients, the majority of whom were among the cases, receiving doses at or above the dose threshold of 90 daily MME. Compared to evidence observed in younger adults with ORAE [25], a lower dose range (0 to 20 daily MME versus 3 to 150 daily MME) and a lower proportion with ≥90 daily MME (2.4% versus 28.8%) was observed among older adults with a similar diagnosis of ORAE, suggesting that there is a unique opioid dosage pattern preceding the incident ORAE encounter in the older population.

The mechanisms by which low to moderate doses of prescribed opioids were associated with increased risk of ORAEs among older patients may be complex and can possibly be explained by 2 major pathways. First, older patients may be more susceptible to opioid side effects at lower doses. While no empirical data or clinical consensus exists on a dose threshold above which opioids are considered harmful for older patients, evidence from the general population has suggested that increased risk of OD may occur at a dose as low as 20 mg/day MME [30]. The other pathway that may explain our observed associations is the use of illicit opioids, which cannot be captured with our data sources, to supplement the low-to-moderate dose of prescribed opioids. Recent reports suggest an emerging transition from prescription opioids to illicit opioids owing to increasingly restricted access to prescription opioids [3133]. It is possible that the gradual dose discontinuation group identified in this study might have sought illicit opioids to achieve pain control or to enhance euphoric effects, putting them at higher risk for ORAEs, compared to other groups of prescription opioid dose. Yet, this assumption was not supported by our data where a lower risk of ORAEs was observed in the group with gradual dose discontinuation versus other identified prescription opioid dose groups. Further studies that examine whether illicit opioid use varies across the 4 identified prescribed opioid dose trajectories are needed to assist interpretations of our study findings.

Of note is our finding that over 1 in 4 (28.3%) older patients gradually discontinued their prescription opioids in 6 months before an ORAE or matched date for controls. The opioid discontinuation was much higher in the matched controls (35.2%) than in cases (11.8%). Reasons for more controls undergoing opioid discontinuation are unclear but could be because of improved pain control, opioid ineffectiveness, or adverse opioid side effects [34]. Opioid discontinuation or dose reduction, particularly among long-term users, is among the recommendations from the 2016 CDC opioid guidance [14]. While the CDC guideline did not support abrupt tapering and sudden discontinuation of prescription opioid dose, these clinical practices were reported for patients with chronic pain and have been implicated as a contributor to unintended consequences, including ORAEs [2], OD and deaths [35], and suicidal ideation or attempts [35,36]. The US Department of Health and Human Services in 2019 issued a new clinical guide on how to appropriately reduce dose or discontinue long-term opioid analgesics, emphasizing the importance of assessing the risks and benefits of such practices [37]. In the present study, we focused on opioid-naïve older adults and found that those with gradual dose discontinuation (versus those with gradual dose increase or those with low-to-moderate dose use) had decreased risk for ORAE. It is worth noting that the gradual dose discontinuation group identified in the present study was named based on the trajectory shape, and the definition of our gradual dose discontinuation is not the same as that defined by the CDC, which involves opioid dose reduction of 10% per week after opioid use for weeks to months and 10% per month following opioid use for >1 year [37]. Whether our association findings can be seen for older adults with long-term opioid therapy requires further investigations to understand the benefits and harms of discontinuation of long-term opioid analgesics.

The present study has several noteworthy strengths. The use of a nationally representative sample of older adults who are Medicare beneficiaries from 2011 to 2018 provides population-based data that reflect current opioid prescribing practices and supplements current literature on prescription opioid dose patterns relevant to older adults at risk for ORAEs. The national data also provide a sufficient number of older adults with an incident ORAE, allowing adequate power to detect the association between trajectories of prescription opioid dose and risk for ORAE.

There are also several limitations to note. First, this study allows for establishing an association but not causation between prescription opioid dose trajectories and risk for ORAEs. Second, illicit opioid use, a growing concern in the opioid epidemic, was not captured in our data, limiting our ability to clarify the safe dose threshold of prescribed opioids for older adults. Third, our analysis of prescription dispensing data confirms receipt of medications and not medication use. Fourth, Medicare administrative claims data lack information on pain severity, which is the key factor associated with selection into opioid treatment. Fifth, while several opioid endpoints defined in claims data have been validated against medical chart review [3840], the validity of ORAE is unclear and warrants further research. Sixth, the present study did not measure the risk of adverse events associated with opioid tapering such as increased pain, insomnia, mental and physical function, and suicide. Seventh, our findings can only be generalized to Medicare fee-for-service beneficiaries with CNCP. Finally, our study excluded patients who had an incident diagnosis of ORAEs but had no prescription opioid fill during the 6 months before the diagnosis. This group may present different opioid risk profiles, and understanding risk factors beyond prescription opioid use is important to identify this high-risk subgroup of older patients.

Our findings have important clinical implications. The CDC-recommended 90 mg/day MME as the high-risk opioid dose threshold may be impractical to detect older adults at risk for ORAE. Only 5% of cases received prescribed opioid doses at or above 90 daily MME before diagnosis, leaving most cases undetected. Since 2013, the CMS required its Medicare Part D sponsors to closely monitor high-risk beneficiaries whose prescribed opioid dose was at or above 120 daily MME, and in recent years, they aligned the risky dose threshold to be consistent with the CDC-recommended 90 daily MME [41]. Prior studies have questioned the utility of using 90 daily MME in detecting patients at risk of ORAEs [21]. Echoing this finding, our study suggests that additional clinical markers to predict illicit opioid use are needed to identify older adult patients at high risk for ORAEs, particularly during the new era of increasingly restricted access to prescription opioids.

Supporting information

S1 Fig. Trajectories of mean daily MME dose prescribed in biweekly within 6 months preceding the incident diagnosis of ORAEs for cases and matched controls of older patients.

MME, morphine milligram equivalent; ORAE, opioid-related adverse event.

https://doi.org/10.1371/journal.pmed.1003947.s011

(TIFF)

S2 Fig. Trajectories of mean daily MME dose prescribed in each month within 6 months preceding an incident diagnosis of opioid misuse or dependence and matched controls of older patients.

MME, morphine milligram equivalent; ORAE, opioid-related adverse event.

https://doi.org/10.1371/journal.pmed.1003947.s012

(TIFF)

S3 Fig. Trajectories of mean daily MME dose prescribed in each month within 6 months preceding an incident diagnosis of opioid poisoning and matched controls of older patients.

MME, morphine milligram equivalent; ORAE, opioid-related adverse event.

https://doi.org/10.1371/journal.pmed.1003947.s013

(TIFF)

References

  1. 1.
    Weiss AJ, Heslin KC, Barrett ML, Izar R, Bierman AS. Opioid-Related Inpatient Stays and Emergency Department Visits Among Patients Aged 65 Years and Older, 2010 and 2015. 2018. Available from: https://www.hcup-us.ahrq.gov/reports/statbriefs/sb244-Opioid-Inpatient-Stays-ED-Visits-Older-Adults.jsp.
  2. 2.
    Agency for Healthcare Research and Quality. Prevention, Diagnosis, and Management of Opioids, Opioid Misuse and Opioid Use Disorder in Older Adults 2019. Available from: https://effectivehealthcare.ahrq.gov/products/opioids-older-adults/protocol.
  3. 3.
    Rajbhandari-Thapa J, Zhang D, Padilla HM, Chung SR. Opioid-Related Hospitalization and Its Association With Chronic Diseases: Findings From the National Inpatient Sample, 2011–2015. Prev Chronic Dis. 2019;16:E157. pmid:31775008
  4. 4.
    Wei YJ, Chen C, Schmidt SO, LoCiganic WH, Winterstein AG. Trends in prior receipt of prescription opioid or adjuvant analgesics among patients with incident opioid use disorder or opioid-related overdose from 2006 to 2016. Drug Alcohol Depend. 2019;204:107600. pmid:31586806
  5. 5.
    Fredheim OM, Borchgrevink PC, Mahic M, Skurtveit S. A pharmacoepidemiological cohort study of subjects starting strong opioids for nonmalignant pain: a study from the Norwegian Prescription Database. Pain. 2013;154(11):2487–93. pmid:24075311
  6. 6.
    Kaplovitch E, Gomes T, Camacho X, Dhalla IA, Mamdani MM, Juurlink DN. Sex Differences in Dose Escalation and Overdose Death during Chronic Opioid Therapy: A Population-Based Cohort Study. PLoS ONE. 2015;10(8):e0134550. pmid:26291716
  7. 7.
    Henry SG, Wilsey BL, Melnikow J, Iosif AM. Dose escalation during the first year of long-term opioid therapy for chronic pain. Pain Med. 2015;16(4):733–44. pmid:25529548
  8. 8.
    Dunn KM, Saunders KW, Rutter CM, Banta-Green CJ, Merrill JO, Sullivan MD, et al. Opioid prescriptions for chronic pain and overdose: a cohort study. Ann Intern Med. 2010;152(2):85–92. pmid:20083827
  9. 9.
    Klimas J, Gorfinkel L, Fairbairn N, Amato L, Ahamad K, Nolan S, et al. Strategies to Identify Patient Risks of Prescription Opioid Addiction When Initiating Opioids for Pain: A Systematic Review. JAMA Netw Open. 2019;2(5):e193365. pmid:31050783
  10. 10.
    Gomes T, Mamdani MM, Dhalla IA, Paterson JM, Juurlink DN. Opioid dose and drug-related mortality in patients with nonmalignant pain. Arch Intern Med. 2011;171(7):686–91. pmid:21482846
  11. 11.
    Edlund MJ, Martin BC, Russo JE, DeVries A, Braden JB, Sullivan MD. The role of opioid prescription in incident opioid abuse and dependence among individuals with chronic noncancer pain: the role of opioid prescription. Clin J Pain. 2014;30(7):557–64. pmid:24281273
  12. 12.
    Garg RK, Fulton-Kehoe D, Franklin GM. Patterns of Opioid Use and Risk of Opioid Overdose Death Among Medicaid Patients. Med Care. 2017;55(7):661–8. pmid:28614178
  13. 13.
    Carey CM, Jena AB, Barnett ML. Patterns of Potential Opioid Misuse and Subsequent Adverse Outcomes in Medicare, 2008 to 2012. Ann Intern Med. 2018;168(12):837–45. pmid:29800019
  14. 14.
    Dowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing Opioids for Chronic Pain—United States, 2016. JAMA. 2016;315(15):1624–45. pmid:26977696
  15. 15.
    Kroenke K, Alford DP, Argoff C, Canlas B, Covington E, Frank JW, et al. Challenges with Implementing the Centers for Disease Control and Prevention Opioid Guideline: A Consensus Panel Report. Pain Med. 2019;20(4):724–35. pmid:30690556
  16. 16.
    Haffajee RL, Mello MM, Zhang F, Zaslavsky AM, Larochelle MR, Wharam JF. Four States With Robust Prescription Drug Monitoring Programs Reduced Opioid Dosages. Health Aff (Millwood). 2018;37(6):964–74. pmid:29863921
  17. 17.
    Kertesz SG, Gordon AJ. A crisis of opioids and the limits of prescription control: United States. Addiction. 2019;114(1):169–80. pmid:30039595
  18. 18.
    Dowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing Opioids for Chronic Pain—United States, 2016. MMWR Recomm Rep. 2016;65(1):1–49. pmid:26987082
  19. 19.
    Rubin R. CMS to Improve Drug Programs and Opioid Overuse Oversight. JAMA. 2018;319(12):1189. pmid:29584828
  20. 20.
    The Centers for Medicare and Medicaid Services. Additional Guidance on CY 2017 Formulary-Level Cumulative Morphine Equivalent Dose (MED) Opioid Point-of-Sale (POS) Edit 2017. Available from: https://mopa.memberclicks.net/assets/docs/071517%20opioid%20med%20safety%20edit%20memo.pdf.
  21. 21.
    Wei YJ, Chen C, Sarayani A, Winterstein AG. Performance of the Centers for Medicare & Medicaid Services’ Opioid Overutilization Criteria for Classifying Opioid Use Disorder or Overdose. JAMA. 2019;321(6):609–11. pmid:30747958
  22. 22.
    The Centers for Medicare and Medicaid Services. Research Data Assistance Center [cited 2021 Sep 26]. Available from: https://resdac.org/#find-cms-data-file.
  23. 23.
    Etminan M. Pharmacoepidemiology II: the nested case-control study—a novel approach in pharmacoepidemiologic research. Pharmacotherapy. 2004;24(9):1105–9. pmid:15460170
  24. 24.
    Weiss AJ, Elixhauser A, Barrett ML, Steiner CA, Bailey MK, O’Malley L. Opioid-Related Inpatient Stays and Emergency Department Visits by State, 2009–2014: Statistical Brief #219. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville(MD); 2017. pmid:28682575
  25. 25.
    Wei YJ, Chen C, Fillingim R, Schmidt SO, Winterstein AG. Trends in prescription opioid use and dose trajectories before opioid use disorder or overdose in US adults from 2006 to 2016: A cross-sectional study. PLoS Med. 2019;16(11):e1002941. pmid:31689302
  26. 26.
    The Centers for Medicare and Medicaid Services. Opioid Oral Morphine Milligram Equivalent (MME) Conversion Factors1. 2016. Available from: https://www.cms.gov/Medicare/Prescription-Drug-coverage/PrescriptionDrugCovContra/Downloads/Opioid-Morphine-EQ-Conversion-Factors-Aug-2017.pdf.
  27. 27.
    Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38. pmid:20192788
  28. 28.
    Andruff H, Carraro N, Thompson A, Gaudreau P, B. L.. Latent Class Growth Modelling: A Tutorial. Tutor Quant Methods Psychol. 2009;5(1):11–24.
  29. 29.
    Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project. HCUP Clinical Classifications Software (CCS). Available from: https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp.
  30. 30.
    Adewumi AD, Hollingworth SA, Maravilla JC, Connor JP, Alati R. Prescribed Dose of Opioids and Overdose: A Systematic Review and Meta-Analysis of Unintentional Prescription Opioid Overdose. CNS Drugs. 2018;32(2):101–16. pmid:29498021
  31. 31.
    Scholl L, Seth P, Kariisa M, Wilson N, Baldwin G. Drug and Opioid-Involved Overdose Deaths—United States, 2013–2017. MMWR Morb Mortal Wkly Rep. 2018;67(5152):1419–27. pmid:30605448
  32. 32.
    Seth P, Rudd RA, Noonan RK, Haegerich TM. Quantifying the Epidemic of Prescription Opioid Overdose Deaths. Am J Public Health. 2018;108(4):500–2. pmid:29513577
  33. 33.
    Compton WM, Jones CM, Baldwin GT. Relationship between Nonmedical Prescription-Opioid Use and Heroin Use. N Engl J Med. 2016;374(2):154–63. pmid:26760086
  34. 34.
    Husain JM, LaRochelle M, Keosaian J, Xuan Z, Lasser KE, Liebschutz JM. Reasons for Opioid Discontinuation and Unintended Consequences Following Opioid Discontinuation Within the TOPCARE Trial. Pain Med. 2019;20(7):1330–7. pmid:29955866
  35. 35.
    Oliva EM, Bowe T, Manhapra A, Kertesz S, Hah JM, Henderson P, et al. Associations between stopping prescriptions for opioids, length of opioid treatment, and overdose or suicide deaths in US veterans: observational evaluation. BMJ. 2020;368:m283. pmid:32131996
  36. 36.
    Demidenko MI, Dobscha SK, Morasco BJ, Meath THA, Ilgen MA, Lovejoy TI. Suicidal ideation and suicidal self-directed violence following clinician-initiated prescription opioid discontinuation among long-term opioid users. Gen Hosp Psychiatry. 2017;47:29–35. pmid:28807135
  37. 37.
    Dowell D, Haegerich T, Chou R. No Shortcuts to Safer Opioid Prescribing. N Engl J Med. 2019;380(24):2285–7. pmid:31018066
  38. 38.
    Green CA, Hazlehurst B, Brandes J, Sapp DS, Janoff SL, Coplan PM, et al. Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data. Pharmacoepidemiol Drug Saf. 2019;28(8):1138–42. pmid:31095831
  39. 39.
    Green CA, Perrin NA, Janoff SL, Campbell CI, Chilcoat HD, Coplan PM. Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records. Pharmacoepidemiol Drug Saf. 2017;26(5):509–17. pmid:28074520
  40. 40.
    Green CA, Perrin NA, Hazlehurst B, Janoff SL, DeVeaugh-Geiss A, Carrell DS, et al. Identifying and classifying opioid-related overdoses: A validation study. Pharmacoepidemiol Drug Saf. 2019;28(8):1127–37. pmid:31020755
  41. 41.
    The Centers for Medicare and Medicaid Services. Opioid Misuse Strategy. 2016. Available from: https://www.cms.gov/Outreach-and-Education/Outreach/Partnerships/Downloads/CMS-Opioid-Misuse-Strategy-2016.pdf.
Posted on

Moderate Physical Activity Reduces Risk for Atrial Fibrillation Events

Interval Walking Boosts Glycemic Control in Type 2 Diabetes

Moderate physical activity was found to reduce risk for atrial fibrillation (AF) events, according to a study published in Open Heart.

The Tromsø Study was a single-center, population-based cohort study conducted in Norway between 1994 and 2016, during which time all Tromsø inhabitants aged 25 years or older were invited to participate. Individuals who were free from known cardiac pathology were followed for AF outcomes for a median of 20.2 years. Physical activity status was defined as inactive (0 h/wk), low (0-1 h/wk), moderate (1-2 h/wk), and vigorous (³3 h/wk) activity levels.

Participants (N=2479) had a mean age of 58.6 (standard deviation [SD], 10.7) years at baseline, 52.4% were women, and BMI was 25.8 (SD, 3.8). Individuals had inactive (n=1502), low (n=383), moderate (n=391), and vigorous (n=203) physical activity statuses.


Continue Reading

AF events occurred among 17.9% of the inactive, 13.1% of the low, 12.3% of the moderate, and 15.8% of the vigorous physical activity cohorts. Compared with the inactive group, the moderate physical activity cohort was at decreased risk for AF events (adjusted hazard ratio [aHR], 0.68; 95% CI, 0.50-0.93; P =.017) but not the low (aHR, 0.80; 95% CI, 0.59-1.09; P =.150) or vigorous (aHR, 0.87; 95% CI, 0.60-1.27; P =.473) physical activity cohorts.

Further stratifying participants by left atrial (LA) status, found no protective effect due to physical activity among the cohort of individuals with normal LA. For the subset of individuals with enlarged LA, individuals with low physical activity were at decreased risk for AF events (aHR, 0.41; 95% CI, 0.22-0.77; P =.005), and there was a decreased trend for those with moderate physical activity (aHR, 0.62; 95% CI, 0.38-1.02; P =.061).

In a cumulative model that grouped all non-inactive physical activity cohorts together, compared with individuals who had enlarged LA and were inactive, all other individuals were at decreased risk for AF events. The lowest risk for AF events was among the enlarged LA, active cohort (aHR, 0.55; 95% CI, 0.39-0.79), followed by normal LA, active (aHR, 0.56; 95% CI, 0.42-0.75), and normal LA, inactive (aHR, 0.61; 95% CI, 0.48-0.78) groups.

Stratified by age, patterns were similar among both the younger (<65 years) and older (³65 years) participants. Stratified by sex, men were observed to have similar patterns. For women, patterns were also similar, except that those with enlarged LA, active status were not at decreased risk for AF events compared with enlarged LA, inactive status women (aHR, 0.74; 95% CI, 0.44-1.23).

This study may have been biased by evaluating LA size using an M-mode anteroposterior diameter approach, which is less accurate than biplane calculated LA volume.

“In conclusion, our prospective study of participants free from known cardiac pathology suggests a U-shaped relationship between [physical activity] and AF,” the investigators noted. “Moderate [physical activity] was associated with reduced risk [for] AF, whereas vigorous [physical activity] attenuated the protective effect of moderate [physical activity].”

Reference

Heitmann KA, Løchen M-L, Stylidis M, Hopstock LA, Schirmer H, Morseth B. Associations between physical activity, left atrial size and incident atrial fibrillation: the Tromsø Study 1994–2016. Open Heart. Published online January 24, 2022. doi:10.1136/openhrt-2021-001823

Posted on

Angiotensinogen and Risk of Stroke Events in Patients with Type 2 Diab | DMSO

Angiotensinogen and Risk of Stroke Events in Patients with Type 2 Diab | DMSO

Introduction

Type 2 diabetes has been considered a vital risk factor for promoting the occurrence and/or development of cardiovascular disease (CVD), such as stroke and coronary heart disease (CHD), and CVD mortality.1–3 Previous studies have shown that individuals with type 2 diabetes may have different severities of the disease, which depends on the presence of comorbidities or other risk factors.4 Well-understood risk heterogeneity and identifying individuals at long-term risk could help improve and personalize cardiovascular risk management for these individuals with type 2 diabetes.

Activation of the renin-angiotensin system (RAS) is a vital pathophysiological mechanism of CVD and renal insufficiency in diabetic patients.5 Many previous observational studies have demonstrated that inhibiting the RAS, which is currently the front-line treatment for diabetic nephropathy,5 could delay worsening renal function and reduce the risk of CVD morbidity and mortality in patients with diabetes.6–8 Importantly, although RAS inhibition has shown many beneficial effects, not all patients showed significant improvements in the prognosis of CVD complications. Hence, accurately estimating the active state of the intrarenal RAS might provide a good opportunity to help identify whether diabetic patients are at high risk of poor prognosis. The kidney has all parts of the RAS pathway that can produce angiotensinogen, which further promotes the production of angiotensin II (angII).9 AngII, produced by the kidney, has been reported to pose a key role in renal function and hemodynamics, affecting the development of cardiovascular pathology.10

Recently, several cross-sectional studies have reported that urinary angiotensinogen may be considered a potential biomarker of renal dysfunction in hypertensive patients.11,12 However, whether urinary and/or serum levels of angiotensinogen can be considered potential biomarkers for predicting stroke risk is still unclear. In this study, we measured urinary and serum levels of angiotensinogen in patients with type 2 diabetes. We aimed to assess whether angiotensinogen levels are associated with stroke prognosis.

Methods

Study Sample

We studied 488 hospitalized patients with type 2 diabetes from Tianjin Nankai Hospital in China between January 2009 and December 2015. None of the included patients had other serious chronic diseases, such as cancers, liver diseases, or respiratory diseases, before admission. After discharge, the patients with type 2 disease were contacted by telephone. A total of 21 patients with type 2 diabetes were excluded from this cohort study due to the diagnosis of serious chronic diseases within three months before admission, including neoplastic diseases (N=10), liver diseases (N=7) and other serious diseases (N=4). The diagnostic criteria for type 2 diabetes were determined by 3 endocrinologists.13 For the purposes of this study, during a mean follow-up of 5 years, ischemic or hemorrhagic stroke requiring hospitalization was defined as an endpoint event. The endpoint event was diagnosed by two neurologists. The Ethics Committee of Tianjin Nankai Hospital approved this study. This was a retrospective study, so this study applied for patients informed exemption according to the Declaration of Helsinki guidelines.

Follow-Up

The included diabetic patients were followed up by telephone and/or reviewing, until the occurrence of endpoint events. The endpoint events of this study were defined as ischemic stroke requiring rehospitalization, hemorrhagic stroke requiring rehospitalization and death caused by stroke. During the 5-year follow-up period, 7 patients with diabetes were lost to follow-up.

Measurement of Serum and Urinary Angiotensinogen Levels

Fasting venous blood samples were obtained from the diabetic patients in the first morning after inclusion. The concentrations of angiotensinogen in urine and serum samples were measured by using enzyme-linked immunosorbent assays (ELISA) at baseline.14 Angiotensinogen concentrations were tested three times in each patient, and the average value of the three results was used for statistical analysis. The interassay and intra-assay coefficients of variability for the serum and urine angiotensinogen assays were 6.5% and 4.5%, respectively.

The blood samples were also measured for serum albumin (ABL), glycosylated hemoglobin (HbA1c), hemoglobin (Hb), low density lipoprotein (LDL), high sensitivity C-reactive protein (hs-CRP) and high density lipoprotein (HDL) levels and were tested at the same time by using immunoassay on an ELECSYS2010 instrument (Roche Diagnostics, Germany). Serum levels of the estimated glomerular filtration rate (eGFR) were calculated by using the Chronic Kidney Disease (CKD) Epidemiology Collaboration equation.15 For research purposes, an eGFR<60 mL/min/1.73 m2 was considered renal insufficiency.

Statistical Analysis

All of the data were analyzed by using SPSS 22.0, and a P ≤ 0.05 was considered to be statistically significant. The Kolmogorov–Smirnov test was used to analyze the normality of the data. t-tests or chi-square tests were performed to compare the two groups (eGFR≥60 mL/min/1.73 m2 and eGFR<60 mL/min/1.73 m2). In the multivariate analysis, Cox regression analysis was performed to identify the independent values of serum and urinary angiotensinogen levels at baseline on predicting the risk of stroke events in patients with type 2 diabetes. To further evaluate the independent association, we further excluded the effect of “duration of diabetes” by sensitivity analysis. Moreover, we also analyzed the association between serum and urinary angiotensinogen levels at baseline and the risk of stroke events during the follow-up period using stratified analysis by adding “taking RAS inhibitors” and “an eGFR≥60 mL/min/1.73 m2”. Additionally, an endpoint (stroke event)-free curve was constructed by the Kaplan–Meier method, and the Log rank test was performed.

Results

Clinical Characteristics of the 467 Patients with Type 2 Diabetes at Baseline

The clinical characteristics of the patients with type 2 diabetes at baseline are presented in Table 1. According to the median value of the eGFR (57 mL/min/1.73 m2), all the patients were divided into two groups. The patients with low eGFRs (<57 mL/min/1.73 m2) tended to have longer durations of diabetes, higher systolic and diastolic blood pressures, and higher rates of ever being a smoker, ever being a drinker, taking RAS inhibitors and having a CVD history, compared with patients with high eGFRs (≥57 mL/min/1.73 m2, all P<0.05). For laboratory measurements, the patients with low eGFRs had higher levels of urinary angiotensinogen, LDL, HbA1c and Hs-CRP and lower levels of ALB and Hb than those with high eGFRs (all P<0.05). Interestingly, serum angiotensinogen was not significantly different between the two groups (P>0.05).

Table 1 Clinical Characteristics in 467 Patients with Type 2 Diabetes at Baseline

Cox Proportional Hazard Analysis for the Associations Between Serum and Urinary Angiotensinogen Levels and Stroke Events in Patients with Type 2 Diabetes

All included patients were prospectively followed up for a median period of 5 years, and 47 patients had stroke events (including ischemic and hemorrhagic stroke). Kaplan–Meier analysis showed that patients with low eGFRs (<57 mL/min/1.73 m2) had a significantly higher rate of stroke events than those with high eGFRs (Figure 1, P=0.040). To further investigate the potential risk for stroke events, a multivariate Cox proportional hazard regression model was used. Urinary angiotensinogen levels (HR=2.78, 95% CI 1.54–5.94, P=<0.001) were associated with an increased risk of stroke events when adjustments for age, sex, BMI, ever smoking and ever drinking were made, which was similar to serum angiotensinogen levels (HR=1.54, 95% CI 1.10–3.27, P=0.037) in Model 1 (Table 2). The significant associations changed slightly after adding systolic and diastolic blood pressures and CVD history in Model 1. After continuing to add the laboratory measurements into Model 2, our results suggested that urinary angiotensinogen levels (HR=2.74, 95% CI 1.50–5.88, P=<0.001, Model 3) were an independent predictor for the risk of stroke events in patients with type 2 diabetes, but not serum angiotensinogen levels (HR=1.42, 95% CI 0.95–2.65, P=0.071, Model 3).

Table 2 Cox Proportional Hazard Analysis for the Association Between Urinary and Serum Angiotensinogen Levels and Stroke Events in Patients with Type 2 Diabetes

Figure 1 Kaplan–Meier analysis of the endpoint-free curve stratified into 2 groups by median level of the eGFR.

We performed an additional sensitivity analysis to evaluate the associations of urinary and serum angiotensinogen levels with the risk of stroke events in patients with type 2 diabetes by adding “duration of diabetes” as a covariate (Table 3). Similarly, the results also suggested that higher urinary angiotensinogen levels still contributed to an increased risk of stroke events (HR=2.71, 95% CI 1.48–5.82, P<0.001, Model 3), but not serum angiotensinogen levels (HR=1.37, 95% CI 0.89–2.21, P=0.104, Model 3), after adjusting for the confounding factors.

Table 3 Sensitivity Analysis for the Association Between Urinary and Serum Angiotensinogen Levels and Stroke Events in Patients with Type 2 Diabetes

Stratified Analysis of the Associations Between Urinary and Serum Angiotensinogen Levels and Stroke Events in Patients with Type 2 Diabetes by “Taking RAS Inhibitors” and “an eGFR≥60 mL/Min/1.73 M2

Stratified analysis was performed by adding “taking RAS inhibitors” and “an eGFR≥60 mL/min/1.73 m2” as covariates. Our results still showed that the association between urinary angiotensinogen levels and the risk of stroke events in patients with type 2 diabetes was significant (HR=2.64, 95% CI 1.45–5.78, P<0.001, Model 3) and was not affected by “taking RAS inhibitors”, as shown in Table 4. Importantly, the significant association was affected by “an eGFR≥60 mL/min/1.73 m2”, as shown in Table 5. We found that a significant association existed only in patients with eGFRs<60 mL/min/1.73 m2 (HR=2.78, 95% CI 1.59–6.30, P<0.001, Model 3) and not in patients with eGFRs≥60 mL/min/1.73 m2 (HR=1.39, 95% CI 0.95–3.53, P=0.054, Model 3). In addition, serum angiotensinogen levels still had no association with the risk of stroke events in the stratified analysis.

Table 4 Stratified Analysis for the Association Between Urinary and Serum Angiotensinogen Levels and Stroke Events in Patients with Type 2 Diabetes by “Taking RAS Inhibitors”

Table 5 Stratified Analysis for the Association Between Urinary and Serum Angiotensinogen Levels and Stroke Events in Patients with Type 2 Diabetes by “Egfr≥60 mL/Min/1.73 M2

Discussion

In the present study, our baseline data suggested that patients with higher urinary levels of angiotensinogen had lower eGFRs. However, serum levels of angiotensinogen were not associated with the eGFR. Moreover, Cox regression analysis suggested that diabetic patients with high levels of urinary angiotensinogen had a high rate of stroke events. Our results documented that increasing urinary angiotensinogen levels were associated with a higher risk for stroke events in diabetic patients. Furthermore, the significant relationship of urinary angiotensinogen levels with stroke risk can be affected by renal function.

Although previous studies have documented that angiotensinogen can be produced and secreted from both the liver and kidneys,10 serum and urinary levels of angiotensinogen originating from different sources pose different impacts on renal function.16,17 Existing evidence suggests that human angiotensinogen cannot be detected in urine obtained from hypertensive and nonhypertensive rats that were injected with human angiotensinogen, which may be explained by the limited glomerular permeability of circulating angiotensinogen and/or degrading angiotensinogen in tubules.18 Under normal renal structure and function, it has been reported that angiotensinogen is expressed in proximal tubular cells and released into the ureter.10 However, in the case of hyperglycemia, the expression of angiotensinogen is significantly increased in proximal tubular cells.19,20 Furthermore, some clinical investigations have reported that diabetic patients have higher urinary angiotensinogen levels,21 whereas there was no difference in serum angiotensinogen levels between diabetic patients and control individuals.21 This previous evidence may suggest that blood angiotensinogen is not a direct source of urinary angiotensinogen. Consistently, our results also suggested that urinary angiotensinogen originates locally from the kidney instead of serum.

Increased angiotensinogen expression in tubules can promote the activation of the intrarenal RAS. Consistent with our study, diabetic patients with low eGFRs had a greater increase in urinary angiotensinogen levels than serum angiotensinogen levels. One similar finding showed that CKD patients with low eGFRs documented higher urinary angiotensinogen levels, suggesting a negative relationship between urinary angiotensinogen levels and renal function.22 Studies have long confirmed the correlation between abnormal renal function and CVDs.23 Our multivariate correlational analysis reported that elevated levels of urinary angiotensinogen contributed to high stroke risk. Hence, the increased levels of urinary angiotensinogen might contribute to the pathological development of stroke, which may be explained by renal dysfunction and/or the incidence of CVD caused by diabetes mellitus.

Our results have several obvious strengths. On the one hand, in the first morning after admission, blood and urinary tests can better reflect the levels of serum and urinary angiotensinogen in diabetic patients. We are the first to find that urinary angiotensinogen can be considered a valuable predictor for endpoints (stroke events requiring rehospitalization) in diabetic patients. On the other hand, our study confirmed complete follow-up and standardized adjudication of the endpoint, so our results are very reliable. Certainly, this study also has several limitations, including a small sample size in a single center. First, although many various confounding factors, including renal function, were adjusted in our study, which may be the most important factor influencing urinary angiotensinogen levels, some other potential confounding factors were not eliminated due to other unknown determinants of urinary angiotensinogen levels. Second, because urinary levels of angiotensinogen were measured in the first morning after admission, the time-dependent variables after discharge were not assessed, which might cause survivorship biases. Third, using many covariates in our Cox regression analysis may have caused overfitting of the model, leading to bias in the results. Finally, we did not further investigate the mechanisms underlying the association of urinary angiotensinogen levels with stroke. Additionally, our study only included the Asian race and limits the generalizability of our results to other races, such as white and black races. In summary, these limitations should be considered in future studies to elaborate on this work.

Conclusions

Our results suggested that elevated urinary levels of angiotensinogen contributed to higher stroke risk in diabetic patients. Reducing urinary levels of angiotensinogen might be a new biomarker to reduce stroke risk.

Funding

There is no funding to report.

Disclosure

The authors report no conflicts of interest in this work.

References

1. Roger VL, Go AS, Lloyd-Jones DM, et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics–2011 update: a report from the American Heart Association. Circulation. 2011;123(4):e18–e209. doi:10.1161/CIR.0b013e3182009701

2. Selvin E, Juraschek SP, Coresh J. Kidney disease in people with diabetes: the expanding epidemic. Am J Kidney Dis. 2012;59(3):340–342. doi:10.1053/j.ajkd.2011.11.016

3. Foster MC, Rawlings AM, Marrett E, et al. Cardiovascular risk factor burden, treatment, and control among adults with chronic kidney disease in the United States. Am Heart J. 2013;166(1):150–156. doi:10.1016/j.ahj.2013.03.016

4. Di Angelantonio E, Kaptoge S, Wormser D, et al.; Emerging Risk Factors Collaboration. Association of cardiometabolic multimorbidity with mortality. JAMA. 2015;314(1):52–60. doi:10.1001/jama.2015.7008

5. Koya D, Araki S, Haneda M. Therapeutic management of diabetic kidney disease. J Diabetes Investig. 2011;2(4):248–254. doi:10.1111/j.2040-1124.2011.00112.x

6. Lewis EJ, Hunsicker LG, Clarke WR, et al.; Collaborative Study Group. Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes. N Engl J Med. 2001;345(12):851–860. doi:10.1056/NEJMoa011303

7. Brenner BM, Cooper ME, de Zeeuw D, et al.; RENAAL Study Investigators. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med. 2001;345(12):861–869. doi:10.1056/NEJMoa011161

8. Fried LF, Emanuele N, Zhang JH, et al.; VA NEPHRON-D Investigators. Combined angiotensin inhibition for the treatment of diabetic nephropathy. N Engl J Med. 2013;369(20):1892–1903. doi:10.1056/NEJMoa1303154

9. Ingelfinger JR, Zuo WM, Fon EA, Ellison KE, Dzau VJ. In situ hybridization evidence for angiotensinogen messenger RNA in the rat proximal tubule. An hypothesis for the intrarenal renin angiotensin system. J Clin Invest. 1990;85(2):417–423. doi:10.1172/JCI114454

10. Kobori H, Nangaku M, Navar LG, Nishiyama A. The intrarenal renin-angiotensin system: from physiology to the pathobiology of hypertension and kidney disease. Pharmacol Rev. 2007;59(3):251–287. doi:10.1124/pr.59.3.3

11. Kobori H, Alper AB Jr, Shenava R, et al. Urinary angiotensinogen as a novel biomarker of the intrarenal renin-angiotensin system status in hypertensive patients. Hypertension. 2009;53(2):344–350. doi:10.1161/HYPERTENSIONAHA.108.123802

12. Kobori H, Ohashi N, Katsurada A, et al. Urinary angiotensinogen as a potential biomarker of severity of chronic kidney diseases. J Am Soc Hypertens. 2008;2(5):349–354. doi:10.1016/j.jash.2008.04.008

13. Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;389(10085):2239–2251. doi:10.1016/S0140-6736(17)30058-2

14. Katsurada A, Hagiwara Y, Miyashita K, et al. Novel sandwich ELISA for human angiotensinogen. Am J Physiol Renal Physiol. 2007;293(3):F956–60. doi:10.1152/ajprenal.00090.2007

15. Krogh J, Benros ME, Martin Balslev J, et al. The association between depressive symptoms, cognitive function, and inflammation in major depression[J]. Brain Behav Immun. 2014;35(1):70–76. doi:10.1016/j.bbi.2013.08.014

16. Sawaguchi M, Araki SI, Kobori H, et al. Association between urinary angiotensinogen levels and renal and cardiovascular prognoses in patients with type 2 diabetes mellitus. J Diabetes Investig. 2012;3(3):318–324. doi:10.1111/j.2040-1124.2011.00172.x

17. Yasue S, Masuzaki H, Okada S, et al. Adipose tissue-specific regulation of angiotensinogen in obese humans and mice: impact of nutritional status and adipocyte hypertrophy. Am J Hypertens. 2010;23(4):425–431. doi:10.1038/ajh.2009.263

18. Kobori H, Nishiyama A, Harrison-Bernard LM, Navar LG. Urinary angiotensinogen as an indicator of intrarenal Angiotensin status in hypertension. Hypertension. 2003;41(1):42–49. doi:10.1161/01.HYP.0000050102.90932.CF

19. Ohashi N, Urushihara M, Satou R, Kobori H. Glomerular angiotensinogen is induced in mesangial cells in diabetic rats via reactive oxygen species–ERK/JNK pathways. Hypertens Res. 2010;33(11):1174–1181. doi:10.1038/hr.2010.143

20. Hsieh TJ, Zhang SL, Filep JG, Tang SS, Ingelfinger JR, Chan JS. High glucose stimulates angiotensinogen gene expression via reactive oxygen species generation in rat kidney proximal tubular cells. Endocrinology. 2002;143(8):2975–2985. doi:10.1210/endo.143.8.8931

21. Saito T, Urushihara M, Kotani Y, Kagami S, Kobori H. Increased urinary angiotensinogen is precedent to increased urinary albumin in patients with type 1 diabetes. Am J Med Sci. 2009;338(6):478–480. doi:10.1097/MAJ.0b013e3181b90c25

22. Yamamoto T, Nakagawa T, Suzuki H, et al. Urinary angiotensinogen as a marker of intrarenal angiotensin II activity associated with deterioration of renal function in patients with chronic kidney disease. J Am Soc Nephrol. 2007;18(5):1558–1565. doi:10.1681/ASN.2006060554

23. Annon CP, Perkovic V, Agarwal R, et al. Evaluating the Effects of Canagliflozin on Cardiovascular and Renal Events in Patients With Type 2 Diabetes Mellitus and Chronic Kidney Disease According to Baseline HbA1c, Including Those With HbA1c <7%: results from the credence trial. Circulation. 2020;141(5):407–410. doi:10.1161/CIRCULATIONAHA.119.044359