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

BACKGROUND

Depression is independently associated with chest pain and increased cardiovascular risk, regardless of coronary artery disease (CAD) status. However, limited research has examined how chest pain characteristics differ in individuals with and without depression. This study evaluated the relationship between depression and chest pain localisation to inform CAD risk assessment.

METHODS

Data from the National Health and Nutrition Examination Survey from 2005 to 2020 were analysed for adults completing the chest pain questionnaire. Survey-weighted propensity score matching created depression-stratified cohorts. Chest pain localisation and inter-regional correlations were compared using survey design-adjusted methods. Random forest models with survey-weighted bootstrap replicates were used to estimate the relative importance of pain locations in predicting CAD, stratified by depression status.

RESULTS

A total of 2208 individuals were matched (1104 per cohort). Depressed individuals more frequently reported chest pain in the lower sternum (P = 0.045), left chest (P = 0.002), and epigastrium (P = 0.039). In depressed individuals, atypical pain regions (epigastrium, lower sternum, neck, arms) were more predictive of CAD, whereas in nondepressed individuals, typical regions (chest, upper sternum) were stronger predictors. These findings were robust to temporal validation and stricter definitions of depression.

CONCLUSIONS

Depression modifies the predictive value of chest pain localisation for CAD. A "depressed chest pain profile" involving nontraditional locations was more strongly associated with CAD in those with depression, and a more central "nondepressed pain profile" was more predictive in those without. These findings underscore the importance of integrating mental health context into chest pain evaluation.