Annals of surgery
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The Risk Analysis Index (RAI) predicts 30-, 180-, and 365-day mortality based on variables constitutive of frailty. Initially validated, in a single-center Veteran hospital, we sought to improve model performance by recalibrating the RAI in a large, veteran surgical registry, and to externally validate it in both a national surgical registry and a cohort of surgical patients for whom RAI was measured prospectively before surgery. ⋯ The RAI-rev has improved discrimination and calibration as a frailty-screening tool in surgical patients. It has robust external validity in men and women across a wide range of surgical settings and available for immediate implementation for risk assessment and counseling in preoperative patients.
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Comparative Study
Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.
To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours. ⋯ Machine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.
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To explore contemporary drain management practices and examine the impact of early removal following distal pancreatectomy (DP). ⋯ Although not yet widely implemented, early drain removal after distal pancreatectomy is associated with better outcomes. This study demonstrates the potential benefits of early removal and provides a substrate to define best practices and improve the quality of care for DP.