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

Anthony E Peters; Nicole Solomon; Karen Chiswell; Gregg C Fonarow; Michel G Khouri; Lori Baylor; Jose Alvir; Marianna Bruno; Ahsan Huda; Larry A Allen; Kavita Sharma; Adam D DeVore; Stephen J Greene

doi:10.1016/j.ahj.2023.06.013

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

Am Heart J. 2023 Nov:265:22-30. doi: 10.1016/j.ahj.2023.06.013. Epub 2023 Jul 1.

Authors

Affiliations

¹ Division of Cardiology, Duke University School of Medicine, Durham, NC; Duke Clinical Research Institute, Durham, NC.
² Duke Clinical Research Institute, Durham, NC.
³ Division of Cardiology, Ahmanson-UCLA Cardiomyopathy Center, University of California Los Angeles Medical Center, Los Angeles, CA.
⁴ Division of Cardiology, Duke University School of Medicine, Durham, NC.
⁵ Pfizer Inc., New York, NY.
⁶ Division of Cardiology & Colorado Cardiovascular Outcomes Research Consortium, University of Colorado School of Medicine, Aurora, CO.
⁷ Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD.
⁸ Division of Cardiology, Duke University School of Medicine, Durham, NC; Duke Clinical Research Institute, Durham, NC. Electronic address: stephen.greene@duke.edu.

PMID: 37400049
DOI: 10.1016/j.ahj.2023.06.013

Abstract

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.