A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy

Ahsan Huda, Adam Castaño, Anindita Niyogi, Jennifer Schumacher, Michelle Stewart, Marianna Bruno, Mo Hu, Faraz S. Ahmad, Rahul C. Deo, Sanjiv J. Shah*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.

Original languageEnglish (US)
Article number2725
JournalNature communications
Volume12
Issue number1
DOIs
StatePublished - Dec 1 2021

Funding

S.J.S. has received grants from Actelion, AstraZeneca, Corvia, Novartis, and Pfizer; and has received consulting fees from Actelion, Amgen, AstraZeneca, Bayer, Boehringer-Ingelheim, Cardiora, Eisai, Ionis, Ironwood, Merck, Novartis, Pfizer, Sanofi, and United Therapeutics. R.C.D. has received a grant from GE Healthcare and has received consulting fees from Novartis and Pfizer. A.C., M.B., A.H., A.N., M.S., and J.S. are full-time employees of Pfizer. All other authors report no competing interests. This study was supported by Pfizer. This work was also supported by grants from the National Institutes of Health (R01 HL140731, R01 HL120728, R01 HL107577, and R01 HL149423); the American Heart Association (#16SFRN28780016, #15CVGPSD27260148, One Brave Idea, Apple Heart and Movement Study); the Agency for Healthcare Research and Quality (K12 HS026385); and the Patient-Centered Outcomes Research Institute and a contract from the People-Centered Research Foundation (#1236). Tracking of co-author contributions and data checks were provided by Donna McGuire and Joshua Fink, PhD, of Engage Scientific Solutions and funded by Pfizer; no contribution was made to editorial content.

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

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