Browsing by Author "Alvir, Jose"

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    Associations of Community and Individual Social Determinants of Health With Medication Adherence in Patients With Atrial Fibrillation: A Retrospective Cohort Study.
    (Journal of the American Heart Association, 2023-04) Boyd, Lisa M; Colavecchia, Anthony Carmine; Townsend, Kevin A; Kmitch, Laura; Broder, Leah; Hegeman-Dingle, Rozelle R; Ateya, Mohammad; Lattimer, Alan; Bosch, Ryan; Alvir, Jose
    Background Despite guideline-recommended use of oral anticoagulation (OAC) for stroke prevention in atrial fibrillation (AF), OAC medication adherence among patients with AF in the United States ranges from 47% to 82%. To characterize potential causes of nonadherence, we analyzed associations between community and individual social risk factors and OAC adherence for stroke prevention in AF. Methods and Results A retrospective cohort analysis of patients with AF was conducted using the IQVIA PharMetrics Plus claims data from January 2016 to June 2020, and 3-digit ZIP code-level social risk scores were calculated using American Community Survey and commercial data. Logistic regression models evaluated associations between community social determinants of health, community social risk scores for 5 domains (economic climate, food landscape, housing environment, transportation network, and health literacy), patient characteristics and comorbidities, and 2 adherence outcomes: persistence on OAC for 180 days and proportion of days covered ≥0.80 at 360 days. Of 28 779 patients with AF included in the study, 70.8% of patients were male, 94.6% were commercially insured, and the average patient age was 59.2 years. Multivariable regression found that greater health literacy risk was negatively associated with 180-day persistence (odds ratio [OR]=0.80 [95% CI, 0.76-0.83]) and 360-day proportion of days covered (OR, 0.81 [95% CI, 0.76-0.87]). Patient age and higher AF stroke risk score and AF bleeding risk scores were positively associated with both 180-day persistence and 360-day proportion of days covered. Conclusions Social risk domains, such as health literacy, may affect OAC adherence among patients with AF. Future studies should explore associations between social risk factors and nonadherence with greater geographic granularity.
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    Transthyretin amyloid cardiomyopathy among patients hospitalized for heart failure and performance of an adapted wild-type ATTR-CM machine learning model: Findings from GWTG-HF.
    (American heart journal, 2023-11) Peters, Anthony E; Solomon, Nicole; Chiswell, Karen; Fonarow, Gregg C; Khouri, Michel G; Baylor, Lori; Alvir, Jose; Bruno, Marianna; Huda, Ahsan; Allen, Larry A; Sharma, Kavita; DeVore, Adam D; Greene, Stephen J

    Background

    An 11-factor random forest model has been developed among ambulatory heart failure (HF) patients for identifying potential wild-type amyloidogenic TTR cardiomyopathy (wtATTR-CM). The model has not been evaluated in a large sample of patients hospitalized for HF.

    Methods

    This study included Medicare beneficiaries aged ≥65 years hospitalized for HF in the Get With The Guidelines-HF® Registry from 2008-2019. Patients with and without a diagnosis of ATTR-CM were compared, as defined by inpatient and outpatient claims data within 6 months pre- or post-index hospitalization. Within a cohort matched 1:1 by age and sex, univariable logistic regression was used to evaluate relationships between ATTR-CM and each of the 11 factors of the established model. Discrimination and calibration of the 11-factor model were assessed.

    Results

    Among 205,545 patients (median age 81 years) hospitalized for HF across 608 hospitals, 627 patients (0.31%) had a diagnosis code for ATTR-CM. Univariable analysis within the 1:1 matched cohort of each of the 11-factors in the ATTR-CM model found pericardial effusion, carpal tunnel syndrome, lumbar spinal stenosis, and elevated serum enzymes (e.g., troponin elevation) to be strongly associated with ATTR-CM. The 11-factor model showed modest discrimination (c-statistic 0.65) and good calibration within the matched cohort.

    Conclusions

    Among US patients hospitalized for HF, the number of patients with ATTR-CM defined by diagnosis codes on an inpatient/outpatient claim within 6 months of admission was low. Most factors within the prior 11-factor model were associated with greater odds of ATTR-CM diagnosis. In this population, the ATTR-CM model demonstrated modest discrimination.