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Predicting cardiovascular disease in patients with mental illness using machine learning.

Martin Bernstorff1,2,3, Lasse Hansen1,2,3, Kevin Kris Warnakula Olesen4

  • 1Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark.

European Psychiatry : the Journal of the Association of European Psychiatrists
|January 8, 2025
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Summary

Machine learning accurately predicts cardiovascular disease (CVD) risk in individuals with mental illness using electronic health records. This tool can aid in early CVD prevention for this high-risk population.

Keywords:
artificial intelligencecardiovascular diseasesprecision medicinepsychiatry

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Area of Science:

  • Computational epidemiology
  • Clinical informatics
  • Public health

Background:

  • Individuals with mental illness have double the prevalence of cardiovascular disease (CVD).
  • Accurate CVD risk prediction is crucial for implementing effective prevention strategies in this population.
  • Routine clinical data from electronic health records (EHRs) offer a valuable resource for risk prediction.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting incident CVD.
  • To utilize routine clinical data from EHRs for risk prediction in patients with mental illness.
  • To assess the model's performance in identifying high-risk individuals for early intervention.

Main Methods:

  • A cohort study included 74,880 patients with 1.6 million psychiatric service contacts (2013-2021).
  • Two ML models (XGBoost, regularised logistic regression) were trained on 234 predictors from EHR data.
  • External validation was performed on a separate patient cohort.

Main Results:

  • The best-performing XGBoost model achieved an area under the receiver operating characteristic curve of 0.84 (training) and 0.74 (validation).
  • The model identified high-risk individuals approximately 2.5 years prior to CVD events.
  • For the top 5% predicted risk, positive predictive value was 5% and negative predictive value was 99%.

Conclusions:

  • A ML model can effectively predict CVD risk in patients with mental illness using EHR data.
  • This predictive capability can support primary CVD prevention efforts.
  • Integration into a decision support system could enhance clinical practice for this vulnerable group.