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Background: COVID-positive outpatients may benefit from remote monitoring, but such a program often relies on smartphone apps. This may introduce racial and socio-economic barriers to participation. Offering multiple methods for participation may address these barriers. Objectives: (1) To examine associations of race and neighborhood disadvantage with patient retention in a monitoring program offering two participation methods. (2) To measure the association of the program with emergency department visits and hospital admissions. Design: Retrospective propensity-matched cohort study. Participants: COVID-positive outpatients at a single university-affiliated healthcare system and propensity-matched controls. Interventions: A home monitoring program providing daily symptom tracking via patient portal app or telephone calls. Main Measures: Among program enrollees, retention (until 14 days, symptom resolution, or hospital admission) by race and neighborhood disadvantage, with stratification by program arm. In enrollees versus matched controls, emergency department utilization and hospital admission within 30 days. Key Results: There were 7592 enrolled patients and 9710 matched controls. Black enrollees chose the telephone arm more frequently than White enrollees (68% versus 44%, p = 0.009), as did those from more versus less disadvantaged neighborhoods (59% versus 43%, p = 0.02). Retention was similar in Black enrollees and White enrollees (63% versus 62%, p = 0.76) and in more versus less disadvantaged neighborhoods (63% versus 62%, p = 0.44). When stratified by program arm, Black enrollees had lower retention than White enrollees in the app arm (49% versus 55%, p = 0.01), but not in the telephone arm (69% versus 71%, p = 0.12). Compared to controls, enrollees more frequently visited the emergency department (HR 1.71 [95% CI 1.56–1.87]) and were admitted to the hospital (HR 1.16 [95% CI 1.02–1.31]). Conclusions: In a COVID-19 remote patient monitoring program, Black enrollees preferentially selected, and had higher retention in, telephone- over app-based monitoring. As a result, overall retention was similar between races. Remote monitoring programs with multiple modes may reduce barriers to participation. [ABSTRACT FROM AUTHOR]

Background: The relationship between risk factor or biomarker trajectories and contemporaneous short-term clinical outcomes is poorly understood. In diabetes patients, it is unknown whether hemoglobin A1c (HbA1c) trajectories are associated with clinical outcomes and can inform care in scenarios in which a single HbA1c is uninformative, for example, after a diagnosis of coronary artery disease (CAD). Objective: To compare associations of HbA1c trajectories and single HbA1c values with short-term mortality in diabetes patients evaluated for CAD Design: Retrospective observational cohort study Participants: Diabetes patients (n = 7780) with and without angiographically defined CAD Main Measures: We used joint latent class mixed models to simultaneously fit HbA1c trajectories and estimate association with 2-year mortality after cardiac catheterization, adjusting for clinical and demographic covariates. Key Results: Three HBA1c trajectory classes were identified: individuals with stable glycemia (class A; n = 6934 [89%]; mean baseline HbA1c 6.9%), with declining HbA1c (class B; n = 364 [4.7%]; mean baseline HbA1c 11.6%), and with increasing HbA1c (class C; n = 482 [6.2%]; mean baseline HbA1c 8.5%). HbA1c trajectory class was associated with adjusted 2-year mortality (3.0% [95% CI 2.8, 3.2] for class A, 3.1% [2.1, 4.2] for class B, and 4.2% [3.4, 4.9] for class C; global P = 0.047, P = 0.03 comparing classes A and C, P > 0.05 for other pairwise comparisons). Baseline HbA1c was not associated with 2-year mortality (P = 0.85; hazard ratios 1.01 [0.96, 1.06] and 1.02 [0.95, 1.10] for HbA1c 7–9% and ≥ 9%, respectively, relative to HbA1c < 7%). The association between HbA1c trajectories and mortality did not differ between those with and without CAD (interaction P = 0.1). Conclusions: In clinical settings where single HbA1c measurements provide limited information, HbA1c trajectories may help stratify risk of complications in diabetes patients. Joint latent class modeling provides a generalizable approach to examining relationships between biomarker trajectories and clinical outcomes in the era of near-universal adoption of electronic health records. [ABSTRACT FROM AUTHOR]