Regularized regression models provided consistent cardiovascular risk prediction in T2D patients with recent ACS, achieving an AUC of 0.69 (95% CI 0.64-0.74) for the primary composite endpoint.
Observational
Do regularized regression methods outperform complex machine learning algorithms for predicting cardiovascular outcomes in T2D patients post-ACS?
Regularized regression methods provide more consistent and generalizable performance than complex machine learning algorithms for predicting cardiovascular risk in T2D patients with recent ACS.
Effect estimate: AUC 0.69 (95% CI 0.64-0.74)
Abstract Identifying type 2 diabetes (T2D) patients at high cardiovascular risk following acute coronary syndrome (ACS) remains challenging despite treatment advances. We systematically compared machine learning and regularized regression methods for predicting cardiovascular outcomes using data from the EXAMINE trial (NCT00968708). Prediction models incorporating over 100 clinical and biomarker variables were developed for three endpoints: cardiovascular death, nonfatal myocardial infarction, or stroke; all-cause mortality; and cardiovascular death or heart failure hospitalization. Eleven algorithms were evaluated using area under the ROC curve (AUC) at 12 and 24 months, with tenfold stratified cross-validation. Regularized regression methods showed the most consistent and generalizable performance, with substantial overlap in confidence intervals between top-performing methods. At 24 months, among regularized models LASSO achieved the highest testing AUC for cardiovascular death or heart failure hospitalization (0.80, 95% CI 0.75–0.85) and all-cause mortality (0.75, 95% CI 0.69–0.81), while the best regularized model for the primary composite endpoint reached an AUC of 0.69 (95% CI 0.64–0.74); the strongest discrimination overall was observed for the 12-month heart failure endpoint (AUC up to 0.83). Adding the proteomic panel to clinical predictors improved or maintained discrimination across all six outcome-by-horizon strata (mean ΔAUC + 0.03). Complex algorithms including neural networks and random forests exhibited overfitting despite cross-validation. These findings indicate that regularized regression methods provide the most consistent and generalizable performance for cardiovascular risk prediction in T2D patients with recent ACS. For the heart failure hospitalization endpoint, the leading regularized model outperformed the best-tuned tree-ensemble method (XGBoost) significantly at 12 months (ΔAUC + 0.065, 95% CI + 0.031 to + 0.100, DeLong p < 0.001) and by a consistent but non-significant margin at 24 months (ΔAUC + 0.037, 95% CI − 0.007 to + 0.082, p = 0.10). The superior performance for heart failure events suggests current clinical and proteomic markers are particularly valuable for heart failure risk stratification in this population.
Barros et al. (Wed,) conducted a observational in Type 2 diabetes following acute coronary syndrome. Regularized regression models vs. Complex machine learning algorithms (e.g., neural networks, random forests, XGBoost) was evaluated on Cardiovascular death, nonfatal myocardial infarction, or stroke (AUC 0.69, 95% CI 0.64-0.74). Regularized regression models provided consistent cardiovascular risk prediction in T2D patients with recent ACS, achieving an AUC of 0.69 (95% CI 0.64-0.74) for the primary composite endpoint.