Achievement of LDL-C <55 mg/dL among US Adults : Findings from the cvMOBIUS2 Registry.

BACKGROUND: Reflecting clinical trial data showing improved outcomes with lower LDL-C levels, guidelines across the globe are increasingly recommending a goal of LDL-C <55 mg/dL in persons with atherosclerotic cardiovascular disease (ASCVD). What proportion of patients with ASCVD are already meeting those goals in the US remains understudied. METHODS: Using electronic health record data from 8 large US health systems, we evaluated lipid-lowering therapy (LLT), LDL-C levels, and factors associated with an LDL-C <55 mg/dL in persons with ASCVD treated between 1/1/2021-12/31/2021. Multivariable modeling was used to evaluate factors associated with achievement of an LDL-C <55 mg/dL. RESULTS: Among 167,899 eligible patients, 22.6% (38,016) had an LDL-C <55 mg/dL. While 76.1% of individuals overall were on a statin, only 38.2% were on a high-intensity statin,;5.9% were on ezetimibe, and 1.7% were on a PCSK9i monoclonal antibody (mAb). Factors associated with lower likelihood of achieving an LDL-C <55 mg/dL included: younger age (odds ratio [OR] 0.91 per 10y), female sex (OR 0.69), Black race (OR 0.76), and non-coronary artery disease forms of ASCVD including peripheral artery disease (OR 0.72) and cerebrovascular disease (OR 0.85), while high-intensity statin use was associated with increased odds of LDL-C <55 mg/dL (OR 1.55). Combination therapy (statin+ezetimibe or statin+PCSK9i mAb) was rare (4.4% and 0.5%, respectively) and was associated with higher odds of an LDL-C <55 mg/dL (OR 1.39 and 3.13, respectively). CONCLUSION: Less than a quarter of US patients with ASCVD in community practice are already achieving an LDL-C <55 mg/dL. Marked increases in utilization of both high intensity statins and combination therapy with non-statin therapy will be needed to achieve LDL-C levels <55 mg/dL at the population level in secondary prevention.

Association between characteristics of employing healthcare facilities and healthcare worker infection rates and psychosocial experiences during the COVID-19 pandemic.

BACKGROUND: Healthcare facility characteristics, such as ownership, size, and location, have been associated with patient outcomes. However, it is not known whether the outcomes of healthcare workers are associated with the characteristics of their employing healthcare facilities, particularly during the COVID-19 pandemic. METHODS: This was an analysis of a nationwide registry of healthcare workers (the Healthcare Worker Exposure Response and Outcomes (HERO) registry). Participants were surveyed on their personal, employment, and medical characteristics, as well as our primary study outcomes of COVID-19 infection, access to personal protective equipment, and burnout. Participants from healthcare sites with at least ten respondents were included, and these sites were linked to American Hospital Association data to extract information about sites, including number of beds, teaching status, urban/rural location, and for-profit status. Generalized estimating equations were used to estimate linear regression models for the unadjusted and adjusted associations between healthcare facility characteristics and outcomes. RESULTS: A total of 8,941 healthcare workers from 97 clinical sites were included in the study. After adjustment for participant demographics, healthcare role, and medical comorbidities, facility for-profit status was associated with greater odds of COVID-19 diagnosis (aOR 1.76, 95% CI 1.02-3.03, p = .042). Micropolitan location was associated with decreased odds of COVID-19 infection after adjustment (aOR = 0.42, 95% CI 0.24, 0.71, p = .002. For-profit facility status was associated with decreased odds of burnout after adjustment (aOR = 0.53, 95% CI 0.29-0.98), p = .044). CONCLUSIONS: For-profit status of employing healthcare facilities was associated with greater odds of COVID-19 diagnosis but decreased odds of burnout after adjustment for demographics, healthcare role, and medical comorbidities. Future research to understand the relationship between facility ownership status and healthcare outcomes is needed to promote wellbeing in the healthcare workforce. TRIAL REGISTRATION: The registry was prospectively registered: ClinicalTrials.gov Identifier (trial registration number) NCT04342806, submitted April 8, 2020.

Addressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights.

The inverse probability of treatment weighting (IPTW) approach is popular for evaluating causal effects in observational studies, but extreme propensity scores could bias the estimator and induce excessive variance. Recently, the overlap weighting approach has been proposed to alleviate this problem, which smoothly down-weights the subjects with extreme propensity scores. Although advantages of overlap weighting have been extensively demonstrated in literature with continuous and binary outcomes, research on its performance with time-to-event or survival outcomes is limited. In this article, we propose estimators that combine propensity score weighting and inverse probability of censoring weighting to estimate the counterfactual survival functions. These estimators are applicable to the general class of balancing weights, which includes IPTW, trimming, and overlap weighting as special cases. We conduct simulations to examine the empirical performance of these estimators with different propensity score weighting schemes in terms of bias, variance, and 95% confidence interval coverage, under various degrees of covariate overlap between treatment groups and censoring rates. We demonstrate that overlap weighting consistently outperforms IPTW and associated trimming methods in bias, variance, and coverage for time-to-event outcomes, and the advantages increase as the degree of covariate overlap between the treatment groups decreases.

The importance of the design of observational studies in comparative effectiveness research: Lessons from the GARFIELD-AF and ORBIT-AF registries.

Randomized controlled trials (RCTs) are considered the gold standard for estimating the effectiveness of a treatment. However, in many instances they are impractical to conduct because of time limitations, cost restrictions, or ethical reasons. As a consequence, non-randomized observational studies have an important role in comparative effectiveness and safety research since they can address issues that would not be possible using conventional RCT methodology. Observational studies can be strategically designed to reduce the risk of potential sources of bias by emulating the design principles of an equivalent but ideal randomized trial - the target trial - that would answer the research question of interest. In this article, we review some of the necessary components of observational studies required for valid causal inference within the framework of target trial emulation, so as to avoid common methodological pitfalls of study design. We discuss the assumptions of consistency, time-zero specification, exchangeability and positivity. To illustrate these concepts in a context where existing knowledge is well-established through clinical trials, we evaluate and compare the treatment effects of vitamin K antagonists (VKA) against no VKA (No VKA) on the treatment of atrial fibrillation from two real-world observational studies, namely the GARFIELD-AF and ORBIT-AF registries. Results are compared with those of published RCTs.

Treatment Persistence of Renin-Angiotensin-Aldosterone-System Inhibitors Over Time in Heart Failure with Reduced Ejection Fraction.

BACKGROUND: Clinical practice guidelines support sustained use of renin-angiotensin-aldosterone-system (RAAS) inhibitors over time in heart failure with reduced ejection fraction, yet few data are available regarding the frequency, timing or predictors of early treatment discontinuation in clinical practice. METHODS: Among prevalent or new users of angiotensin-converting enzyme inhibitors (ACEis)/angiotensin receptor blockers (ARBs), angiotensin receptor-neprilysin inhibitors (ARNIs), and mineralocorticoid receptor antagonists (MRAs) in the CHAMP-HF (Change the Management of Patients with Heart Failure) registry, we estimated the frequency and independent predictors of treatment discontinuation during follow-up. Among sites with > 5 users of a given RAAS inhibitor, we evaluated practice variation in the proportion of patients with treatment discontinuation. RESULTS: Over median follow-up of 18 months, frequency of drug discontinuation of ACEis/ARBs, ARNIs and MRAs was 12.7% (444 of 3509 users), 10.4% (140 of 1352 users), and 20.4% (435 of 2129 users), respectively. An additional, 149 (11.0%) of ARNI users were switched to ACEis/ARBs, and 447 (12.7%) of ACEi/ARB users were switched to ARNIs during follow-up. Across sites, the median proportion of discontinuation of ACEis/ARBs, ARNIs and MRAs was 12.5% (25th-75th percentiles 6.9%-18.9%), 18.8% (25th-75th percentiles 12.5%-28.6%), and 19.6% (25th-75th percentiles 10.7%-27.0%), respectively. Chronic kidney disease was the only independent predictor of increased risk of discontinuation of each of the RAAS inhibitor classes (P < 0.02 for all). Higher Kansas City Cardiomyopathy Questionnaire overall summary scores independently predicted lower risk of discontinuation of ACEis/ARBs and ARNIs (both P < 0.001) but not of MRAs. Investigator clinical experience was predictive of lower risks of discontinuation of ACEis/ARBs and MRAs (P < 0.02) but not of ARNIs. All other independent predictors of discontinuation were unique to individual therapeutic classes. CONCLUSIONS: One in 10 patients discontinue ACEis/ARBs or ARNIs, and 1 in 5 discontinue MRAs in routine clinical practice of heart failure with reduced ejection fraction. Unique patient-level and clinician/practice-level factors are associated with premature discontinuation of individual RAAS inhibitors, which may help to guide structured efforts to promote treatment persistence in clinical care.

Experiences of U.S. Nurses Compared With Nonnurses in the First Year of COVID-19: Findings From a National Registry.

BACKGROUND: Nursing professional organizations and media sources indicated early in the pandemic that the physical and psychological effects of COVID-19 might be distinct and possibly greater in nurses than in other types of healthcare workers (HCWs). OBJECTIVES: Based on survey data collected in Healthcare Worker Exposure Response and Outcomes (HERO), a national registry of U.S. HCWs, this study compared the self-reported experiences of nurses with other HCWs during the first 13 months of the pandemic. METHODS: Nurse responses were compared to responses of nonnurse HCWs in terms of viral exposure, testing and infection, access to personal protective equipment (PPE), burnout, and well-being. Logistic regression models were used to examine associations between nurse and nonnurse roles for the binary end points of viral testing and test positivity for COVID-19. We also examined differences by race/ethnicity and high-risk versus low-risk practice settings. RESULTS: Of 24,343 HCWs in the registry, one third self-identified as nurses. Nurses were more likely than other HCWs to report exposure to SARS-CoV-2, problems accessing PPE, and decreased personal well-being, including burnout, feeling tired, stress, trouble sleeping, and worry. In adjusted models, nurses were more likely than nonnurse HCWs to report viral testing and test positivity for COVID-19 infection. Nurses in high-risk settings were more likely to report viral exposure and symptoms related to well-being; nurses in low-risk settings were more likely to report viral testing and test positivity. Black or Hispanic nurses were most likely to report test positivity. DISCUSSION: Differences were identified between nurses and nonnurse HCWs in access to PPE, physical and mental well-being measures, and likelihood of reporting exposure and infection. Among nurses, testing and infection differed based on race and ethnicity, and type of work setting. Our findings suggest further research and policy are needed to elucidate and address social and occupational disparities.

Impact of the Early Phase of the COVID-19 Pandemic on US Healthcare Workers: Results from the HERO Registry.

BACKGROUND: The HERO registry was established to support research on the impact of the COVID-19 pandemic on US healthcare workers. OBJECTIVE: Describe the COVID-19 pandemic experiences of and effects on individuals participating in the HERO registry. DESIGN: Cross-sectional, self-administered registry enrollment survey conducted from April 10 to July 31, 2020. SETTING: Participants worked in hospitals (74.4%), outpatient clinics (7.4%), and other settings (18.2%) located throughout the nation. PARTICIPANTS: A total of 14,600 healthcare workers. MAIN MEASURES: COVID-19 exposure, viral and antibody testing, diagnosis of COVID-19, job burnout, and physical and emotional distress. KEY RESULTS: Mean age was 42.0 years, 76.4% were female, 78.9% were White, 33.2% were nurses, 18.4% were physicians, and 30.3% worked in settings at high risk for COVID-19 exposure (e.g., ICUs, EDs, COVID-19 units). Overall, 43.7% reported a COVID-19 exposure and 91.3% were exposed at work. Just 3.8% in both high- and low-risk settings experienced COVID-19 illness. In regression analyses controlling for demographics, professional role, and work setting, the risk of COVID-19 illness was higher for Black/African-Americans (aOR 2.32, 99% CI 1.45, 3.70, p < 0.01) and Hispanic/Latinos (aOR 2.19, 99% CI 1.55, 3.08, p < 0.01) compared with Whites. Overall, 41% responded that they were experiencing job burnout. Responding about the day before they completed the survey, 53% of participants reported feeling tired a lot of the day, 51% stress, 41% trouble sleeping, 38% worry, 21% sadness, 19% physical pain, and 15% anger. On average, healthcare workers reported experiencing 2.4 of these 7 distress feelings a lot of the day. CONCLUSIONS: Healthcare workers are at high risk for COVID-19 exposure, but rates of COVID-19 illness were low. The greater risk of COVID-19 infection among race/ethnicity minorities reported in the general population is also seen in healthcare workers. The HERO registry will continue to monitor changes in healthcare worker well-being during the pandemic. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT04342806.

Propensity score weighting for causal subgroup analysis.

A common goal in comparative effectiveness research is to estimate treatment effects on prespecified subpopulations of patients. Though widely used in medical research, causal inference methods for such subgroup analysis (SGA) remain underdeveloped, particularly in observational studies. In this article, we develop a suite of analytical methods and visualization tools for causal SGA. First, we introduce the estimand of subgroup weighted average treatment effect and provide the corresponding propensity score weighting estimator. We show that balancing covariates within a subgroup bounds the bias of the estimator of subgroup causal effects. Second, we propose to use the overlap weighting (OW) method to achieve exact balance within subgroups. We further propose a method that combines OW and LASSO, to balance the bias-variance tradeoff in SGA. Finally, we design a new diagnostic graph-the Connect-S plot-for visualizing the subgroup covariate balance. Extensive simulation studies are presented to compare the proposed method with several existing methods. We apply the proposed methods to the patient-centered results for uterine fibroids (COMPARE-UF) registry data to evaluate alternative management options for uterine fibroids for relief of symptoms and quality of life.

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach.

BACKGROUND: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. METHODS: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate-subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. RESULTS: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. CONCLUSION: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate-subgroup interactions in the propensity score model.

Guideline-directed therapies for comorbidities and clinical outcomes among individuals with atrial fibrillation.

BACKGROUND: Comorbidities are common in patients with atrial fibrillation (AF) and affect prognosis, yet are often undertreated. However, contemporary rates of use of guideline-directed therapies (GDT) for non-AF comorbidities and their association with outcomes are not well described. METHODS: We used the Outcomes Registry for Better Informed Treatment of AF (ORBIT-AF) to test the association between GDT for non-AF comorbidities and major adverse cardiac or neurovascular events (MACNE; cardiovascular death, myocardial infarction, stroke/thromboembolism, or new-onset heart failure), all-cause mortality, new-onset heart failure, and AF progression. Adjustment was performed using Cox proportional hazards models and logistic regression. RESULTS: Only 6,782 (33%) of the 20,434 patients eligible for 1 or more GDT for non-AF comorbidities received all indicated therapies. Use of all comorbidity-specific GDT was highest for patients with hyperlipidemia (75.6%) and lowest for those with diabetes mellitus (43.1%). Use of "all eligible" GDT was associated with a nonsignificant trend toward lower rates of MACNE (HR 0.90 [0.79-1.02]) and all-cause mortality (HR 0.90 [0.80-1.01]). Use of GDT for heart failure was associated with a lower risk of all-cause mortality (HR 0.77 [0.67-0.89]), and treatment of obstructive sleep apnea was associated with a lower risk of AF progression (OR 0.75 [0.62-0.90]). CONCLUSIONS: In AF patients, there is underuse of GDT for non-AF comorbidities. The association between GDT use and outcomes was strongest in heart failure and obstructive sleep apnea patients where use of GDT was associated with lower mortality and less AF progression.

Matching with time-dependent treatments: A review and look forward.

Observational studies of treatment effects attempt to mimic a randomized experiment by balancing the covariate distribution in treated and control groups, thus removing biases related to measured confounders. Methods such as weighting, matching, and stratification, with or without a propensity score, are common in cross-sectional data. When treatments are initiated over longitudinal follow-up, a target pragmatic trial can be emulated using appropriate matching methods. The ideal experiment of interest is simple; patients would be enrolled sequentially, randomized to one or more treatments and followed subsequently. This tutorial defines a class of longitudinal matching methods that emulate this experiment and provides a review of existing variations, with guidance regarding study design, execution, and analysis. These principles are illustrated in application to the study of statins on cardiovascular outcomes in the Framingham Offspring cohort. We identify avenues for future research and highlight the relevance of this methodology to high-quality comparative effectiveness studies in the era of big data.

Application of net reclassification index to non-nested and point-based risk prediction models: a review.

Much of medical risk prediction involves externally derived prediction equations, nomograms, and point-based risk scores. These settings are vulnerable to misleading findings of incremental value based on versions of the net reclassification index (NRI) in common use. By applying non-nested models and point-based risk scores in the setting of stroke risk prediction in patients with atrial fibrillation (AF), we demonstrate current recommendations for presentation and interpretation of the NRI. We emphasize pitfalls that are likely to occur with point-based risk scores that are easy to neglect when statistical methodology is focused on continuous models. In order to make appropriate decisions about risk prediction and personalized medicine, physicians, researchers, and policy makers need to understand the strengths and limitations of the NRI.

Prognostic Significance of Nuisance Bleeding in Anticoagulated Patients With Atrial Fibrillation.

BACKGROUND: Bleeding is commonly cited as a reason for stopping oral anticoagulants (OACs). Whether minor bleeding events (nuisance bleeding, NB) in patients with atrial fibrillation on OACs are associated with OAC discontinuation, major bleeding, and stroke/systemic embolism (SSE) is unknown. METHODS: Within the ORBIT-AF prospective, outpatient registry (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation), we identified 6771 patients ≥18 years of age at 172 sites with atrial fibrillation and eligible follow-up visits. NB was ascertained from the medical record and was defined as minor bleeding that did not require medical attention (eg, bruising, hemorrhoidal bleeding). We used multivariable pooled logistic regression modeling to evaluate the associations between NB and major bleeding and SSE in the 180 days after documentation of NB. Our unit of analysis was the patient visit, occurring at ≈6-month intervals for a median of 1.5 years following enrollment. Changes in anticoagulation treatment satisfaction after NB were examined descriptively in a subset of patients. RESULTS: The median age of the overall population was 75.0 (interquartile range, 67.0-81.0); 90.0% were white and 42.5% were female. Among 6771 patients (18 560 visits), n=1357 (20.0%) had documented NB, for an incidence rate of 14.8 events per 100 person-years. Over 96.4% of patients remained on OAC therapy after the NB event. Overall, 287 (4.3%) patients experienced major bleeding and 64 (0.96%) had a SSE event during follow-up. NB was not associated with a significant increased risk of major bleeding over 6 months in models adjusting for the ATRIA bleeding score (Anticoagulation and Risk Factors in Atrial Fibrillation) (odds ratio, 1.04; 95% confidence interval, 0.68-1.60; P=0.86). NB was also not associated with increased SSE risk over 6 months in models adjusting for the CHA2DS2-VASc risk score (odds ratio, 1.24; 95% confidence interval, 0.53-2.91; P=0.62). CONCLUSIONS: NB is common among patients with atrial fibrillation on OACs. However, NB was not associated with a higher risk of major bleeding or SSE over the next 6 months, suggesting its occurrence should not lead to changes in anticoagulation treatment strategies in OAC-treated patients. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01165710.

Addressing Extreme Propensity Scores via the Overlap Weights.

The popular inverse probability weighting method in causal inference is often hampered by extreme propensity scores, resulting in biased estimates and excessive variance. A common remedy is to trim patients with extreme scores (i.e., remove them from the weighted analysis). However, such methods are often sensitive to the choice of cutoff points and discard a large proportion of the sample. The implications for bias and the precision of the treatment effect estimate are unclear. These problems are mitigated by a newly developed method, the overlap weighting method. Overlap weights emphasize the target population with the most overlap in observed characteristics between treatments, by continuously down-weighting the units in the tails of the propensity score distribution. Here we use simulations to compare overlap weights to standard inverse probability weighting with trimming, in terms of bias, variance, and 95% confidence interval coverage. A range of propensity score distributions are considered, including settings with substantial nonoverlap and extreme values. To facilitate practical implementation, we further provide a consistent estimator for the standard error of the treatment effect estimated using overlap weighting.

Hospital participation in clinical trials for patients with acute myocardial infarction: Results from the National Cardiovascular Data Registry.

BACKGROUND: Little is known about the proportion of hospitals in the United States that offer clinical trial enrollment opportunities and how patient outcomes differ between hospitals that do and do not participate in clinical trials. METHODS: In the nationwide Chest Pain-MI registry, we described the proportion of hospitals that enrolled patients with acute myocardial infarction (MI) in clinical trials from 2009 to 2014. Hospital-level adherence to every eligible MI performance measure was compared between hospitals that did and did not enroll patients in clinical trials. Using linked Medicare data, we also compared 1-year major adverse cardiovascular events (MACE: death, MI, heart failure, or stroke) among patients ≥65 years old treated at trial versus nontrial hospitals. RESULTS: Among 766 hospitals, 430 (56.1%) enrolled ≥1 MI patient in a clinical trial during the study period, but the proportion of hospitals enrolling patients in clinical trials declined from 36.8% in 2009 to 26.6% in 2014. Complete adherence to performance measures was delivered to a greater proportion of patients at trial hospitals than nontrial hospitals (72.6% vs 64.9%, P < .001; adjusted OR 1.07, 95% CI 1.03-1.12). One-year MACE rates were also lower for trial hospitals (adjusted HR 0.96, 95% CI 0.93-0.99). CONCLUSIONS: Hospitals are becoming less likely to engage in clinical trials for patients with MI. Patients admitted to hospitals that participated in clinical trials more often received guideline-adherent care and had better long-term outcomes.

The ORBIT bleeding score: a simple bedside score to assess bleeding risk in atrial fibrillation.

BACKGROUND: Therapeutic decisions in atrial fibrillation (AF) are often influenced by assessment of bleeding risk. However, existing bleeding risk scores have limitations. OBJECTIVES: We sought to develop and validate a novel bleeding risk score using routinely available clinical information to predict major bleeding in a large, community-based AF population. METHODS: We analysed data from Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF), a prospective registry that enrolled incident and prevalent AF patients at 176 US sites. Using Cox proportional hazards regression, we identified factors independently associated with major bleeding among patients taking oral anticoagulation (OAC) over a median follow-up of 2 years (interquartile range = 1.6-2.5). We also created a numerical bedside risk score that included the five most predictive risk factors weighted according to their strength of association with major bleeding. The predictive performance of the full model, the simple five-item score, and two existing risk scores (hypertension, abnormal renal/liver function, stroke, bleeding history or predisposition, labile INR, elderly, drugs/alcohol concomitantly, HAS-BLED, and anticoagulation and risk factors in atrial fibrillation, ATRIA) were then assessed in both the ORBIT-AF cohort and a separate clinical trial population, Rivaroxaban Once-daily oral direct factor Xa inhibition compared with vitamin K antagonism for prevention of stroke and embolism trial in atrial fibrillation (ROCKET-AF). RESULTS: Among 7411 ORBIT-AF patients taking OAC, the rate of major bleeding was 4.0/100 person-years. The full continuous model (12 variables) and five-factor ORBIT risk score (older age [75+ years], reduced haemoglobin/haematocrit/history of anaemia, bleeding history, insufficient kidney function, and treatment with antiplatelet) both had good ability to identify those who bled vs. not (C-index 0.69 and 0.67, respectively). These scores both had similar discrimination, but markedly better calibration when compared with the HAS-BLED and ATRIA scores in an external validation population from the ROCKET-AF trial. CONCLUSIONS: The five-element ORBIT bleeding risk score had better ability to predict major bleeding in AF patients when compared with HAS-BLED and ATRIA risk scores. The ORBIT risk score can provide a simple, easily remembered tool to support clinical decision making.

Reasons for warfarin discontinuation in the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF).

BACKGROUND: Warfarin reduces thromboembolic risks in atrial fibrillation (AF), but therapeutic durability remains a concern. METHODS: We used clinical data from ORBIT-AF, a nationwide outpatient AF registry conducted at 176 sites with follow-up data at 6 and 12 months, to examine longitudinal patterns of warfarin discontinuation. We estimated associations between patient and provider characteristics and report of any warfarin discontinuation using discrete time proportional odds models. RESULTS: Of 10,132 AF patients enrolled in ORBIT-AF from June 2010 to August 2011, 6,110 (60.3%) were prescribed warfarin, had follow-up data, and were not switched to an alternative oral anticoagulant enrolled from June 2010 to August 2011. Over 1 year, 617 patients (10.1% of baseline warfarin users) discontinued warfarin therapy. Among incident warfarin users (starting therapy within 1 year of baseline survey), warfarin discontinuation rates rose to 17.1%. The most commonly reported reasons for warfarin discontinuation were physician preference (47.7%), patient refusal/preference (21.1%), bleeding event (20.2%), frequent falls/frailty (10.8%), high bleeding risk (9.8%), and patient inability to adhere to/monitor therapy (4.7%). In multivariable analysis, the factors most strongly associated with warfarin discontinuation were bleeding hospitalization during follow-up (odds ratio 10.91, 95% CI 7.91-15.03), prior catheter ablation (1.83, 1.37-2.45), noncardiovascular/nonbleeding hospitalization (1.77, 1.40-2.24), cardiovascular hospitalization (1.64, 1.33-2.03), and permanent AF (0.25, 0.17-0.36). CONCLUSIONS: Discontinuation of warfarin is common among patients with AF, particularly among incident users. Warfarin is most commonly discontinued because of physician preference, patient refusal, and bleeding events.

Nursing Home Workers' Experience During the COVID-19 Pandemic.

PURPOSE: To understand nursing home workers' experience during the coronavirus disease 2019 (COVID-19) pandemic and investigate the prevalence of health-related quality of life, emotional distress, job satisfaction, and the impact of the pandemic. METHOD: The Healthcare Worker Exposure Response and Outcomes (HERO) Registry served as the data source for this descriptive cross-sectional analysis. Recruitment was conducted nationally. Eligible nursing home workers (N = 1,409) enrolled in the study online, self-reported demographic and employment characteristics, and completed electronic surveys. RESULTS: Nursing home workers reported overall good physical health, frequent depressive symptoms, burnout, and a high prevalence of feeling tired, stressed, having trouble sleeping, and feeling worried. Age and race were found to be positively associated with the impact of the pandemic. CONCLUSION: Findings demonstrate the difficulties and challenges nursing home workers faced during the COVID-19 pandemic. Future research needs to evaluate the relationships among nursing home workers' roles, mental health, depressive symptoms, and prevalence of burnout with a larger, more diverse sample. [Research in Gerontological Nursing, 17(3), 131-140.].