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

Author

Listed:
  • Ahsan Huda

    (Pfizer, Inc.)

  • Adam Castaño

    (Pfizer, Inc.)

  • Anindita Niyogi

    (Pfizer, Inc.)

  • Jennifer Schumacher

    (Pfizer, Inc.)

  • Michelle Stewart

    (Pfizer, Inc.)

  • Marianna Bruno

    (Pfizer, Inc.)

  • Mo Hu

    (Northwestern University Feinberg School of Medicine)

  • Faraz S. Ahmad

    (Northwestern University Feinberg School of Medicine)

  • Rahul C. Deo

    (Brigham and Women’s Hospital)

  • Sanjiv J. Shah

    (Northwestern University Feinberg School of Medicine)

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.

Suggested Citation

  • Ahsan Huda & Adam Castaño & Anindita Niyogi & Jennifer Schumacher & Michelle Stewart & Marianna Bruno & Mo Hu & Faraz S. Ahmad & Rahul C. Deo & Sanjiv J. Shah, 2021. "A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22876-9
    DOI: 10.1038/s41467-021-22876-9
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    Cited by:

    1. Soroosh Tayebi Arasteh & Tianyu Han & Mahshad Lotfinia & Christiane Kuhl & Jakob Nikolas Kather & Daniel Truhn & Sven Nebelung, 2024. "Large language models streamline automated machine learning for clinical studies," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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