TY - JOUR
T1 - EstimATTR
T2 - A Simplified, Machine-Learning-Based Tool to Predict the Risk of Wild-Type Transthyretin Amyloid Cardiomyopathy
AU - CASTAÑO, A. D.A.M.
AU - HEITNER, STEPHEN B.
AU - MASRI, AHMAD
AU - HUDA, AHSAN
AU - CALAMBUR, VEENA
AU - BRUNO, MARIANNA
AU - SCHUMACHER, JENNIFER
AU - EMIR, BIROL
AU - ISHERWOOD, CATHERINE
AU - SHAH, SANJIV J.
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2024
Y1 - 2024
N2 - 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).
AB - 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).
KW - Wild-type transthyretin amyloidosis
KW - cardiomyopathy
KW - heart failure
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85184072016&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184072016&partnerID=8YFLogxK
U2 - 10.1016/j.cardfail.2023.11.017
DO - 10.1016/j.cardfail.2023.11.017
M3 - Article
C2 - 38065306
AN - SCOPUS:85184072016
SN - 1071-9164
JO - Journal of Cardiac Failure
JF - Journal of Cardiac Failure
ER -