Relationship Between Social Vulnerability Indicators and Trial Participant Attrition: Findings From the HYVALUE Trial.

BACKGROUND: Social vulnerability indicators are associated with health care inequities and may similarly impede ongoing participation in research studies. We evaluated the association of social vulnerability indicators and research participant attrition in a trial focused on reducing health disparities. METHODS: Self-identified White or Black adults enrolled in the HYVALUE trial (Hypertension and VALUEs), a randomized trial testing a values-affirmation intervention on medication adherence, from February 2017 to September 2019 were included. The self-reported measures of social vulnerability indicators included: (1) Black race; (2) female gender; (3) no health insurance; (4) unemployment; (5) a high school diploma or less; and (6) financial-resource strain. Full attrition was defined as not completing at least one 3- or 6-month follow-up study visit. Log-binomial regression models adjusted for age, gender, race, medical comorbidities, and the other social vulnerability indicators to estimate the relative risk of each social vulnerability indicator with study attrition. RESULTS: Among 825 participants, the mean age was 63.3 years (±11.7 years), 60% were women, 54% were Black, and 97% reported at least one social vulnerability. Overall, 21% participants had full attrition after study enrollment. After adjustment for all other social vulnerabilities, only financial-resource strain remained consistently associated with full attrition (relative risk, 1.71 [95% CI, 1.28-2.29]). In a secondary analysis of partial attrition (completed only one follow-up visit), financial-resource strain (relative risk, 1.40 [95% CI, 1.09-1.81]) and being uninsured (relative risk, 1.54 [95% CI, 1.01-2.34]) were associated with partial attrition. CONCLUSIONS: In a trial aimed at reducing disparities in medication adherence, participants who reported financial-resource strain had a higher risk of participant attrition independent of race or gender. Our findings suggest that efforts to retain diverse populations in clinical trials should extend beyond race and gender to consider other social vulnerability indicators. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03028597.

Predictive Accuracy of Heart Failure-Specific Risk Equations in an Electronic Health Record-Based Cohort.

BACKGROUND: Guidelines recommend identification of individuals at risk for heart failure (HF). However, implementation of risk-based prevention strategies requires validation of HF-specific risk scores in diverse, real-world cohorts. Therefore, our objective was to assess the predictive accuracy of the Pooled Cohort Equations to Prevent HF within a primary prevention cohort derived from the electronic health record. METHODS: We retrospectively identified patients between the ages of 30 to 79 years in a multi-center integrated healthcare system, free of cardiovascular disease, with available data on HF risk factors, and at least 5 years of follow-up. We applied the Pooled Cohort Equations to Prevent HF tool to calculate sex and race-specific 5-year HF risk estimates. Incident HF was defined by the International Classification of Diseases codes. We assessed model discrimination and calibration, comparing predicted and observed rates for incident HF. RESULTS: Among 31 256 eligible adults, mean age was 51.4 years, 57% were women and 11% Black. Incident HF occurred in 568 patients (1.8%) over 5-year follow-up. The modified Pooled Cohort Equations to Prevent HF model for 5-year risk prediction of HF had excellent discrimination in White men (C-statistic 0.82 [95% CI, 0.79-0.86]) and women (0.82 [0.78-0.87]) and adequate discrimination in Black men (0.69 [0.60-0.78]) and women (0.69 [0.52-0.76]). Calibration was fair in all race-sex subgroups (χ2<20). CONCLUSIONS: A novel sex- and race-specific risk score predicts incident HF in a real-world, electronic health record-based cohort. Integration of HF risk into the electronic health record may allow for risk-based discussion, enhanced surveillance, and targeted preventive interventions to reduce the public health burden of HF.