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.

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.].

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 substantial covariate imbalance with propensity score stratification and balancing weights: connections and recommendations.

Objectives: Propensity score (PS) weighting methods are commonly used to adjust for confounding in observational treatment comparisons. However, in the setting of substantial covariate imbalance, PS values may approach 0 and 1, yielding extreme weights and inflated variance of the estimated treatment effect. Adaptations of the standard inverse probability of treatment weights (IPTW) can reduce the influence of extremes, including trimming methods that exclude people with PS values near 0 or 1. Alternatively, overlap weighting (OW) optimizes criteria related to bias and variance, and performs well compared to other PS weighting and matching methods. However, it has not been compared to propensity score stratification (PSS). PSS has some of the same potential advantages; being insensitive extreme values. We sought to compare these methods in the setting of substantial covariate imbalance to generate practical recommendations. Methods: Analytical derivations were used to establish connections between methods, and simulation studies were conducted to assess bias and variance of alternative methods. Results: We find that OW is generally superior, particularly as covariate imbalance increases. In addition, a common method for implementing PSS based on Mantel-Haenszel weights (PSS-MH) is equivalent to a coarsened version of OW and can perform nearly as well. Finally, trimming methods increase bias across methods (IPTW, PSS and PSS-MH) unless the PS model is re-fit to the trimmed sample and weights or strata are re-derived. After trimming with re-fitting, all methods perform similarly to OW. Conclusions: These results may guide the selection, implementation and reporting of PS methods for observational studies with substantial covariate imbalance.

Propensity score weighting methods for causal subgroup analysis with time-to-event outcomes.

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in comparative effectiveness research. Despite recent advancements in causal subgroup analysis, research on time-to-event outcomes has been lacking. This article investigates the propensity score weighting method for causal subgroup survival analysis. We introduce two causal estimands, the subgroup marginal hazard ratio and subgroup restricted average causal effect, and provide corresponding propensity score weighting estimators. We analytically established that the bias of subgroup-restricted average causal effect is determined by subgroup covariate balance. Using extensive simulations, we compare the performance of various combinations of propensity score models (logistic regression, random forests, least absolute shrinkage and selection operator, and generalized boosted models) and weighting schemes (inverse probability weighting, and overlap weighting) for estimating the causal estimands. We find that the logistic model with subgroup-covariate interactions selected by least absolute shrinkage and selection operator consistently outperforms other propensity score models. Also, overlap weighting generally outperforms inverse probability weighting in terms of balance, bias and variance, and the advantage is particularly pronounced in small subgroups and/or in the presence of poor overlap. We applied the methods to the observational Comparing Options for Management: PAtient-centered REsults for Uterine Fibroids study to evaluate the causal effects of myomectomy versus hysterectomy on the time to disease recurrence in a number of pre-specified subgroups of patients with uterine fibroids.

Dexamethasone for Inpatients With COVID-19 in a National Cohort.

Importance: Limited effective therapeutics are available to hospitalized patients with COVID-19. Clinical trials and observational studies have shown varying effects of systemic corticosteroids, including dexamethasone, in hospitalized patients with COVID-19, with limited descriptions of important patient subgroups. Objective: To examine the clinical use of dexamethasone for hospitalized patients with COVID-19 respiratory illness and to explore the heterogeneity of treatment outcomes across different subgroups. Design, Setting, and Participants: This is a retrospective, propensity score-weighted cohort study of adult patients hospitalized for at least 48 hours for COVID-19 respiratory illness between July 1, 2020, and October 31, 2021, at a large health care network of 156 hospitals across the US. Data analysis was performed from March 2022 to February 2023. Exposures: Systemic dexamethasone administered within 48 hours of either admission or escalation in oxygen support. Main Outcomes and Measures: All-cause in-hospital mortality or discharge to hospice. Results: A total of 80 699 patients who met the eligibility criteria were identified (median [IQR] age, 64 [52-76] years; 37 606 women [46.6%]); 13 230 patients (16.4%) identified as Black, 49 222 (60.9%) as White, 18 247 (22.6%) as other race, and 20 340 (25.2%) as Hispanic ethnicity. Of these patients, 13 040 (16.2%) did not require supplemental oxygen within 48 hours of admission, 56 368 (69.8%) required supplemental oxygen, 7618 (9.4%) required noninvasive positive pressure ventilation (NIPPV), and 3673 (4.6%) required mechanical ventilation (MV) and/or extracorporeal membrane oxygenation (ECMO). After adjustment by propensity score overlap weighting, early use of dexamethasone was associated with reduction in a composite outcome of in-hospital mortality or discharge to hospice for patients receiving supplemental oxygen (aOR, 0.92; 95% CI, 0.86-0.98) and MV and/or ECMO (aOR, 0.82; 95% CI, 0.68-0.99). In contrast, all-cause inpatient mortality or discharge to hospice was not lower for patients who received dexamethasone in the no supplemental oxygen group (aOR, 0.90; 95% CI, 0.78-1.03) and in the NIPPV group (aOR, 0.87; 95% CI, 0.73-1.04). Importantly, patients with more comorbidities had greater benefit from dexamethasone use. Conclusions and Relevance: In this national multicenter cohort study of inpatients with COVID-19, early administration of dexamethasone was associated with significantly reduced odds of mortality or discharge to hospice in those requiring supplemental oxygen or MV and/or ECMO but not in those requiring no supplemental oxygen or NIPPV. These results support the continued use of systemic dexamethasone in patients hospitalized with COVID-19.

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.

Angiotensin Receptor Neprilysin Inhibition and Associated Outcomes by Race and Ethnicity in Patients With Heart Failure With Reduced Ejection Fraction: Data From CHAMP-HF.

Background There are limited data on the use of angiotensin receptor neprilysin inhibitors (ARNIs) in minority populations with heart failure (HF) with reduced ejection fraction. We used data from the CHAMP-HF (Change the Management of Patients With Heart Failure) registry to evaluate ARNI initiation and associated changes in health status and clinical outcomes across different races and ethnicities. Methods and Results CHAMP-HF was a prospective, observational registry of US outpatients with chronic HF with reduced ejection fraction. We compared patients starting ARNI with patients not starting ARNI using a propensity-matched analysis. Patients were grouped as Hispanic, non-Hispanic Black, non-Hispanic White, or non-Hispanic other individuals, where "non-Hispanic other" consists of all patients who did not identify as Hispanic, Black, or White. Health status was assessed using the 12-item Kansas City Cardiomyopathy Questionnaire. Outcomes were analyzed with multivariable models that included race and ethnicity, ARNI initiation, and an interaction term between race and ethnicity and ARNI initiation. Cox proportional hazards models were used for death/HF hospitalization, and multiple regression was used for change in Kansas City Cardiomyopathy Questionnaire score. The analysis included 1516 patients, with 758 patients in each group (ARNI and no ARNI). Changes in Kansas City Cardiomyopathy Questionnaire score after ARNI initiation were similar among all race and ethnicity groups (mean [SD], non-Hispanic White individuals, 3.5 [19.0]; non-Hispanic Black individuals, 2.0 [17.0]; non-Hispanic other individuals, 5.5 [20.3]; and Hispanic individuals, 3.2 [20.1]), with no statistically significant interaction between race and ethnicity and ARNI initiation (P=0.21). There was similarly no statistically significant interaction between race and ethnicity and ARNI initiation for HF hospitalization (P=0.82) or all-cause mortality (P=0.92). Conclusions In a large registry of outpatients with HF with reduced ejection fraction, the association between ARNI initiation and outcomes did not differ by race and ethnicity. These data support the use of ARNI therapy for chronic HF with reduced ejection fraction irrespective of race and ethnicity.

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.

Racial/Ethnic Disparities in Healthcare Worker Experiences During the COVID-19 Pandemic: An Analysis of the HERO Registry.

Background: The extent to which healthcare worker (HCWs) experiences during the COVID-19 pandemic vary by race or ethnicity after adjustment for confounding factors is not currently known. Methods: We performed an observational prospective cohort study of 24,769 healthcare workers from 50 U.S. states and the District of Columbia, enrolled between April 10, 2020 and June 30, 2021, and evaluated participant experiences during the COVID-19 pandemic, including testing, diagnosis with COVID-19, emotional experiences, burnout, and interest in vaccines and vaccine clinical trials. Findings: After adjustment for professional role, medical history, and community characteristics, Black and Asian participants were less likely to receive SARS-CoV-2 viral testing (adjusted odds ratio (aOR) 0·82 [0·70, 0·96], p=0·012 and aOR 0·77 [0·67, 0·89], p<0·001 respectively) than White participants. Hispanic participants were more likely to have evidence of COVID-19 infection (aOR 1·23 (1·00, 1·50, p=0·048). Black and Asian participants were less likely to report interest in a COVID-19 vaccine (aOR 0·11 [0·05, 0·25], p<0·001 and aOR 0·48 [0·27, 0·85] p=0·012). Black participants were less likely to report interest in participating in a COVID-19 vaccine trial (aOR = 0·39 [0·28, 0·54], p<0·001). Black participants were also less likely to report 3 or more daily emotional impacts of COVID-19 (aOR = 0·66 [0·53, 0·82], p=<0·001). Black participants were additionally less likely to report burnout (aOR = 0·66 ([0·49, 0·95], p=0·025). Interpretation: In a large, national study of healthcare workers, after adjustment for individual and community characteristics, race/ethnicity disparities in COVID-19 outcomes persist. Future work is urgently needed to understand precise mechanisms behind these disparities and to develop and implement targeted interventions to improve health equity for healthcare workers. Funding: This work was funded by the Patient-Centered Outcomes Research Institute (PCORI), Contract # COVID-19-2020-001.

Antithrombotic Therapy in Patients With Atrial Fibrillation After Acute Coronary Syndromes or Percutaneous Intervention.

BACKGROUND: The use of apixaban instead of vitamin K antagonists (VKA) as well as dropping aspirin results in less bleeding and comparable ischemic events in patients with atrial fibrillation and acute coronary syndrome and/or percutaneous coronary intervention treated with a P2Y12 inhibitor. OBJECTIVES: The authors assessed the safety and efficacy of antithrombotic regimens according to HAS-BLED and CHA2DS2-VASc scores in AUGUSTUS (The Open-Label, 2 × 2 Factorial, Randomized, Controlled Clinical Trial to Evaluate the Safety of Apixaban vs. Vitamin K Antagonist and Aspirin vs. Placebo in Patients with Atrial Fibrillation and Acute Coronary Syndrome and/or Percutaneous Coronary Intervention). METHODS: In AUGUSTUS, 4,614 patients were randomized in a 2-by-2 factorial design to open-label apixaban or VKA and blinded aspirin or placebo. The primary endpoint was major or clinically relevant nonmajor bleeding over 6 months of follow-up. Cox proportional hazards models were used to assess treatment effects by baseline HAS-BLED (≤2 vs ≥3) and CHA2DS2-VASc (≤2 vs ≥3) scores. RESULTS: Of 4,386 (95.1%) patients with calculable scores, 66.8% had HAS-BLED ≥3 and 81.7% had CHA2DS2-VASc ≥3. Bleeding rates were lower with apixaban than VKA irrespective of baseline risk (HR: 0.57; 95% CI: 0.41-0.78 [HAS-BLED ≤2]; HR: 0.72; 95% CI: 0.59-0.88 [HAS-BLED ≥3]; interaction P = 0.23). Aspirin increased bleeding irrespective of baseline risk (HR: 1.86; 95% CI: 1.36-2.56 [HAS-BLED ≤2]; HR: 1.81; 95% CI: 1.47-2.23 [HAS-BLED ≥3]; interaction P = 0.88). Apixaban resulted in a lower risk of death or hospitalization than VKA without a significant interaction with baseline stroke risk (HR: 0.92; 95% CI: 0.67-1.25 [CHA2DS2-VASc ≤2]; HR: 0.82; 95% CI: 0.73-0.94 [CHA2DS2-VASc ≥3]; interaction P = 0.53). CONCLUSIONS: Our findings support the use of apixaban and a P2Y12 inhibitor without aspirin for most patients with atrial fibrillation and acute coronary syndrome and/or percutaneous coronary intervention, irrespective of a patient's baseline bleeding and stroke risk (NCT02415400).

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.

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.

Identifying patients at increased risk for poor outcomes from heart failure with reduced ejection fraction: the PROMPT-HF risk model.

AIMS: We aimed to develop a risk prediction tool that incorporated both clinical events and worsening health status for patients with heart failure (HF) with reduced ejection fraction (HFrEF). Identifying patients with HFrEF at increased risk of a poor outcome may enable proactive interventions that improve outcomes. METHODS AND RESULTS: We used data from a longitudinal HF registry, CHAMP-HF, to develop a risk prediction tool for poor outcomes over the next 6 months. A poor outcome was defined as death, an HF hospitalization, or a ≥20-point decrease (or decrease below 25) in 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12) overall summary score. Among 4546 patients in CHAMP-HF, 1066 (23%) experienced a poor outcome within 6 months (1.3% death, 11% HF hospitalization, and 11% change in KCCQ-12). The model demonstrated moderate discrimination (c-index = 0.65) and excellent calibration with observed data. The following variables were associated with a poor outcome: age, race, education, New York Heart Association class, baseline KCCQ-12, atrial fibrillation, coronary disease, diabetes, chronic kidney disease, smoking, prior HF hospitalization, and systolic blood pressure. We also created a simplified model with a 0-10 score using six variables (New York Heart Association class, KCCQ-12, coronary disease, chronic kidney disease, prior HF hospitalization, and systolic blood pressure) with similar discrimination (c-index = 0.63). Patients scoring 0-3 were considered low risk (event rate <20%), 4-6 were considered intermediate risk (event rate 20-40%), and 7-10 were considered high risk (event rate >40%). CONCLUSIONS: The PROMPT-HF risk model can identify outpatients with HFrEF at increased risk of poor outcomes, including clinical events and health status deterioration. With further validation, this model may help inform therapeutic decision making.

Diuretic Changes, Health Care Resource Utilization, and Clinical Outcomes for Heart Failure With Reduced Ejection Fraction: From the Change the Management of Patients With Heart Failure Registry.

BACKGROUND: Diuretics are a mainstay therapy for the symptomatic treatment of heart failure. However, in contemporary US outpatient practice, the degree to which diuretic dosing changes over time and the associations with clinical outcomes and health care resource utilization are unknown. METHODS: Among 3426 US outpatients with chronic heart failure with reduced ejection fraction in the Change the Management of Patients with Heart Failure registry with complete medication data and who were prescribed a loop diuretic, diuretic dose increase was defined as: (1) change to a total daily dose higher than their previous total daily dose, (2) addition of metolazone to the regimen, (3) change from furosemide to either bumetanide or torsemide, and the change persists for at least 7 days. Adjusted hazard ratios or rate ratios along with 95% CIs were reported for clinical outcomes among patients with an increase in oral diuretic dose versus no increase in diuretic dose. RESULTS: Overall, 796 (23%) had a diuretic dose increase (18 episodes per 100 patient-years). The proportion of patients with dyspnea at rest (38% versus 26%), dyspnea at exertion (79% versus 67%), orthopnea (32% versus 21%), edema (60% versus 43%), and weight gain (40% versus 23%) were significantly (all P <0.001) higher in the diuretic increase group. Baseline angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (hazard ratio, 0.75 [95% CI, 0.65-0.87]) use were associated with lower likelihood of diuretic increase over time. Patients with a diuretic dose increase had a significantly higher number of heart failure hospitalizations (rate ratio, 2.53 [95% CI, 2.10-3.05]), emergency department visits (rate ratio, 1.84 [95% CI, 1.56-2.17]), and home health visits (rate ratio, 1.88 [95% CI, 1.39-2.54]), but not all-cause mortality (hazard ratio, 1.10 [95% CI, 0.89-1.36]). Similarly, greater furosemide dose equivalent increases were associated with greater resource utilization but not with mortality, compared with smaller increases. CONCLUSIONS: In this contemporary US registry, 1 in 4 patients with heart failure with reduced ejection fraction had outpatient escalation of diuretic therapy over longitudinal follow-up, and these patients were more likely to have sign/symptoms of congestion. Outpatient diuretic dose escalation of any magnitude was associated with heart failure hospitalizations and resource utilization, but not all-cause mortality.

Risk Adjustment Model for Preserved Health Status in Patients With Heart Failure and Reduced Ejection Fraction: The CHAMP-HF Registry.

BACKGROUND: Health status outcomes are increasingly being promoted as measures of health care quality, given their importance to patients. In heart failure (HF), an American College of Cardiology/American Heart Association Task Force proposed using the proportion of patients with preserved health status as a quality measure but not as a performance measure because risk adjustment methods were not available. METHODS: We built risk adjustment models for alive with preserved health status and for preserved health status alone in a prospective registry of outpatients with HF with reduced ejection fraction across 146 US centers between December 2015 and October 2017. Preserved health status was defined as not having a ≥5-point decrease in the Kansas City Cardiomyopathy Questionnaire Overall Summary score at 1 year. Using only patient-level characteristics, hierarchical multivariable logistic regression models were developed for 1-year outcomes and validated using data from 1 to 2 years. We examined model calibration, discrimination, and variability in sites' unadjusted and adjusted rates. RESULTS: Among 3932 participants (median age [interquartile range] 68 years [59-75], 29.7% female, 75.4% White), 2703 (68.7%) were alive with preserved health status, 902 (22.9%) were alive without preserved health status, and 327 (8.3%) had died by 1 year. The final risk adjustment model for alive with preserved health status included baseline Kansas City Cardiomyopathy Questionnaire Overall Summary, age, race, employment status, annual income, body mass index, depression, atrial fibrillation, renal function, number of hospitalizations in the past 1 year, and duration of HF (optimism-corrected C statistic=0.62 with excellent calibration). Similar results were observed when deaths were ignored. The risk standardized proportion of patients alive with preserved health status across the 146 sites ranged from 62% at the 10th percentile to 75% at the 90th percentile. Variability across sites was modest and changed minimally with risk adjustment. CONCLUSIONS: Through leveraging data from a large, outpatient, observational registry, we identified key factors to risk adjust sites' proportions of patients with preserved health status. These data lay the foundation for building quality measures that quantify treatment outcomes from patients' perspectives.

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.

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.

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.