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Machine Learning Reveals How Depression Influences Chest Pain Localisation and Its Predictive Value for Coronary

Mohsyn Imran Malik1, Wendy Lou2, Gianluigi Bisleri3

  • 1Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; Department of Cardiac Surgery, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada.

The Canadian Journal of Cardiology
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Summary

Depression influences how chest pain predicts heart disease risk. Nontraditional pain locations are key indicators in depressed individuals, while typical chest pain is more predictive in those without depression.

Keywords:
anginachest paincoronary artery diseasedepressionmachine learning

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Area of Science:

  • Cardiology
  • Psychiatry
  • Public Health

Background:

  • Depression is linked to chest pain and cardiovascular risk, irrespective of coronary artery disease (CAD).
  • Limited research exists on how chest pain characteristics vary between depressed and non-depressed individuals.
  • Understanding these differences is crucial for accurate CAD risk assessment.

Purpose of the Study:

  • To evaluate the relationship between depression and chest pain localization.
  • To determine how chest pain patterns differ in individuals with and without depression.
  • To inform cardiovascular disease (CVD) risk stratification strategies.

Main Methods:

  • Analysis of National Health and Nutrition Examination Survey data (2005-2020).
  • Propensity score matching created depression-stratified cohorts.
  • Comparison of chest pain localization and predictive value for CAD using random forest models, stratified by depression status.

Main Results:

  • Depressed individuals reported chest pain more often in the lower sternum, left chest, and epigastrium.
  • Atypical pain regions (epigastrium, lower sternum, neck, arms) were more predictive of CAD in depressed individuals.
  • Typical pain regions (chest, upper sternum) were stronger CAD predictors in non-depressed individuals.

Conclusions:

  • Depression status modifies the predictive significance of chest pain localization for CAD.
  • A "depressed chest pain profile" (nontraditional locations) is more strongly associated with CAD in depressed individuals.
  • Integrating mental health status is vital for comprehensive chest pain evaluation and risk assessment.