Machine learning (ML) algorithms developed to predict in-hospital mortality after acute MI offered more meaningful gains in model calibration than in accuracy, researchers found. Parsing through data on 29 variables from the American College of Cardiology (ACC) Chest Pain-MI Registry, extreme gradient descent boosting (XGBoost) and meta-classifier models offered no substantive improvement in discrimination compared with standard logistic regression modeling (C-statistics 0.90 for both vs 0.89), reported Harlan Krumholz, MD, SM, of Yale School of Medicine, and colleagues. However, the two ML models showed nearly perfect agreement between observed and predicted risk across the risk spectrum. Of the people deemed moderate-to-high risk in logistic regression, 27% were more accurately reclassified as low risk by the XGBoost model and 25% by the meta-classifier model -- both more consistent with the observed event rates. "These findings suggest that ML models are not associated with substantially better prediction of risk of death after acute MI but may offer greater resolution of risk, which can better clarify the individual risk for adverse outcomes," Krumholz's group reported in a paper published online in JAMA Cardiology.