Key result
Simplified 11-phenotype machine learning model identifies ATTRwt-CM versus nonamyloid HF with ~74% accuracy.
Why the study?
Wild-type transthyretin amyloid cardiomyopathy is an increasingly recognized cause of heart failure that often remains undiagnosed until later stages of the disease.
Does a simplified machine learning model based on 11 phenotypes accurately predict the risk of ATTRwt-CM in patients with heart failure?
Observational
Yes
Does a simplified machine learning model based on 11 phenotypes accurately predict the risk of ATTRwt-CM in patients with heart failure?
Effect estimate: AUC 0.82
A simplified 11-phenotype machine learning model can accurately identify patients at risk for wild-type transthyretin amyloid cardiomyopathy using administrative data, potentially aiding earlier diagnosis.
May aid ATTRwt-CM screening in HF; leaves open prospective validation and outcome impact before clinical use.
BACKGROUND: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM), an increasingly recognized cause of heart failure (HF), often remains undiagnosed until later stages of the disease. METHODS AND RESULTS: A previously developed machine learning algorithm was simplified to create a random forest model based on 11 selected phenotypes predictive of ATTRwt-CM to estimate ATTRwt-CM risk in hypothetical patient scenarios. Using U.S. medical claims datasets (IQVIA), International Classification of Diseases codes were extracted to identify a training cohort of patients with ATTRwt-CM (cases) or nonamyloid HF (controls). After assessment in a 20% test sample of the training cohort, model performance was validated in cohorts of patients with International Classification of Diseases codes for ATTRwt-CM or cardiac amyloidosis vs nonamyloid HF derived from medical claims (IQVIA) or electronic health records (Optum). The simplified model performed well in identifying patients with ATTRwt-CM vs nonamyloid HF in the test sample, with an accuracy of 74%, sensitivity of 77%, specificity of 72%, and area under the curve of 0.82; robust performance was also observed in the validation cohorts. CONCLUSIONS: This simplified machine learning model accurately estimated the empirical probability of ATTRwt-CM in administrative datasets, suggesting it may serve as an easily implementable tool for clinical assessment of patient risk for ATTRwt-CM in the clinical setting. BRIEF LAY SUMMARY: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM for short) is a frequently overlooked cause of heart failure. Finding ATTRwt-CM early is important because the disease can worsen rapidly without treatment. Researchers developed a computer program that predicts the risk of ATTRwt-CM in patients with heart failure. In this study, the program was used to check for 11 medical conditions linked to ATTRwt-CM in the medical claims records of patients with heart failure. The program was 74% accurate in identifying ATTRwt-CM in patients with heart failure and was then used to develop an educational online tool for doctors (the wtATTR-CM estimATTR).
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Castaño et al. (2023) conducted an observational in Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM). Simplified machine learning model (EstimATTR) vs. Nonamyloid heart failure was evaluated on Identification of patients with ATTRwt-CM vs nonamyloid HF (AUC 0.82). A simplified machine learning model based on 11 phenotypes identified patients with ATTRwt-CM versus nonamyloid heart failure with 74% accuracy, 77% sensitivity, and an AUC of 0.82.
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