TL;DR
ML models trained on conventional echocardiographic parameters retained moderate predictive ability for reduced GLS upon external validation, and performance was generally preserved in the external validation cohort.
STE-derived GLS is a highly sensitive, relatively load-independent marker of early myocardial dysfunction in sepsis, significantly outperforming LVEF in predicting 28-day mortality and validated GLS-SOFA nomogram and decision curve models provide a practical, high-yield tool to enhance risk stratification and guide ear...
An interpretable XGBoost model based on six clinical, laboratory, and CGM-derived indicators showed acceptable discrimination, calibration, and clinical net benefit for estimating ultrasound-defined plaque vulnerability among patients with T2DM and established carotid plaque.
In this site-separated external validation, ML models showed metric-dependent performance but no discrimination advantage over the AUB-HAS2 index, and flexible-model results are hypothesis-generating.
This study successfully established a comprehensive prediction model incorporating clinical and imaging variables, which can accurately identify DMD patients at risk for short-term LVEF decline, and demonstrated excellent discrimination.
An interpretable XGBoost-based ML model that facilitates early risk stratification and targeted interventions for CSVD is validated, readily transferable to resource-limited settings and on embedding the nomogram into electronic-health-record decision support.
CMR-derived phenotypes provide incremental prognostic value beyond traditional risk factors and may improve early identification of individuals at elevated cardiovascular risk and may improve early identification of individuals at elevated cardiovascular risk.
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