Diagnostic value of triglyceride-rich lipoprotein cholesterol for coronary artery lesions and its predictive significance for long-term prognosis in elderly patients with coronary heart disease

TL;DR

This study shows that triglyceride-rich lipoprotein cholesterol can predict coronary artery disease severity and long-term mortality in elderly patients.

Contribution

TRL-C is shown to be a novel predictor for coronary lesion severity and mortality in elderly coronary heart disease patients.

Findings

TRL-C models predicted coronary lesion severity with AUCs of 0.76 and 0.77 in training and validation sets.

TRL-C models predicted all-cause mortality with AUCs of 0.94 and 0.92 in training and validation sets.

TRL-C is a strong predictor for both lesion severity and long-term mortality in elderly coronary heart disease patients.

Abstract

To assess the diagnostic and prognostic value of triglyceride-rich lipoprotein cholesterol (TRL-C) in patients with coronary artery disease in the elderly.

This study enrolled 220 patients with coronary artery disease in the elderly undergoing coronary angiography at Tianjin Chest Hospital (January–December 2020) with a four-year follow-up. Coronary lesions were classified as mild (SYNTAX score 1–22) or moderate-to-severe (score ≥23). Logistic regression analyzed TRL-C's association with lesion severity, while Cox regression evaluated its predictive capacity for all-cause mortality.

For coronary lesion severity prediction, TRL-C-based models achieved AUCs of 0.76 (95% CI: 0.68–0.85) and 0.77 (95% CI: 0.64–0.90) in training and validation sets, respectively, with decision curve analysis (DCA) confirming clinical utility. For all-cause mortality prediction, models demonstrated higher discriminative performance, with training and validation AUCs of 0.94 (95% CI: 0.89–0.99) and 0.92 (95% CI: 0.86–0.99), respectively, supported by favorable DCA results.

TRL-C strongly predicts both coronary lesion severity and long-term mortality in patients with coronary artery disease in the elderly, offering a potential biomarker for risk stratification and personalized management.

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