aceTargeting delirium in gastric surgery: An interpretable predictive model and statistical analysis in critical care
BackgroundPostoperative delirium is a serious complication in elderly ICU patients after gastric surgery, increasing mortality and healthcare burden. Early identification of high-risk patients may support timely prevention and optimise perioperative care.ObjectiveTo develop and externally validate an interpretable machine learning model for predicting postoperative delirium in elderly ICU patients undergoing gastric surgery.MethodsThis retrospective multicohort study used three perioperative and critical care da…
Low outcome prevalence and class imbalance produced low precision and a substantial false-positive burden, so the model is not ready for clinical implementation.
The model showed low precision and a substantial false-positive burden due to class imbalance and low prevalence, and the retrospective design requires prospective validation and clinical utility assessment before implementation.
Evidence
- Peer-reviewedTechnology and Health Care2026-10-08
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Truvace Impact Record TRV-2026-1733, v1: “Targeting delirium in gastric surgery: An interpretable predictive model and statistical analysis in critical care.” Truvace, 2026-10-17. /record/TRV-2026-1733 (accessed at citation time). sha256 a53c08d5af47d4e2…
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