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Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning

Lusha W Liang1, Michael A Fifer2, Kohei Hasegawa3

  • 1Division of Cardiology, Department of Medicine (L.W.L., M.S.M., M.P.R., Y.J.S.), Columbia University Irving Medical Center, New York, NY.

Circulation. Genomic and Precision Medicine
|April 23, 2021
PubMed

Insights

Machine learning models significantly improve genotype positivity prediction in hypertrophic cardiomyopathy (HCM) patients. These novel algorithms outperform traditional scoring systems, aiding in personalized screening and treatment strategies.

Area of Science:

  • Cardiology
  • Genetics
  • Machine Learning

Background:

  • Genetic testing is crucial for hypertrophic cardiomyopathy (HCM) management but poses psychosocial challenges.
  • Current scoring systems have limited accuracy in predicting genetic mutations.
  • There is a need for improved methods to identify genotype-positive HCM patients.

Purpose of the Study:

  • To develop and validate a novel prediction model for genotype positivity in HCM patients using machine learning (ML).
  • To compare the performance of ML models against existing clinical scoring systems.

Main Methods:

  • Three ML models were developed using clinical and cardiac imaging data from 102 HCM patients (training set).
  • Model performance was validated on a separate set of 76 HCM patients (test set).
  • ML models were compared to the Toronto HCM Genotype Score and Mayo HCM Genotype Predictor using AUROC and net reclassification improvement.

Main Results:

  • The random forest ML model achieved an AUROC of 0.92 in the test set, significantly outperforming the Toronto score (0.77) and Mayo score (0.79).
  • ML models demonstrated higher sensitivity, positive predictive value, and negative predictive value compared to conventional scores.
  • Gradient boosted decision tree ML model also showed significant improvement over existing scores.

Conclusions:

  • ML models offer superior prediction of genotype positivity in HCM patients compared to current scoring systems.
  • These findings support the use of ML for more accurate genetic risk stratification in HCM.
  • The developed models can aid in tailoring family screening and prognostic assessments.
Abstract

Keywords:
cardiomyopathiesgenesgenotypemachine learningmutation

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