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

Abstract

An 11-factor random forest model has been previously developed among ambulatory heart failure (HF) patients for identifying potential wild-type amyloidogenic TTR cardiomyopathy (wtATTR-CM), but model performance in a large sample of patients hospitalized for HF has not been evaluated. 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 ATTR-CM model. Discrimination and calibration of the 11-factor model were assessed. Among 205,545 patients (median age 81 years) hospitalized for HF across 608 US hospitals, 627 patients (0.31%) had a diagnosis code for ATTR-CM. ATTR-CM patients were more likely to be male (69% vs 47%), Black race (23% vs 9%), and had lower systolic blood pressure (median 124 vs 139 mmHg). 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 with c-statistic of 0.65 and good calibration within the matched cohort. Among patients hospitalized for HF in US practice, the number of patients with ATTR-CM as defined by diagnosis codes on an inpatient/outpatient claim within +/- 6 months of admission was low. Patients with a diagnosis of ATTR-CM have a distinct clinical profile, and 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.