aceA Predictive Model Integrating Coagulation Profiles for Preoperative Risk Stratification of Difficult Single-Incision Laparoscopic Cholecystectomy: A Retrospective Cohort Study Based on Explainable Machine Learning
Background Accurate preoperative prediction of surgical difficulty in single-incision laparoscopic cholecystectomy (SILC) remains challenging, as models for multiport surgery are not directly applicable. Methods This retrospective single-center study included 520 patients, who were randomly divided into a training cohort (n=364) and an internal validation cohort (n=156). Six predictors were identified, guiding 8 machine learning models. Performance was assessed using the area under the curve (AUC), calibration,…
A neural network built on six predictors including coagulation parameters discriminated difficult from non-difficult single-incision cholecystectomy cases in internal validation, potentially aiding patient selection and surgical planning.
The study is a retrospective, single-center analysis validated on an internal cohort drawn from the same population, so generalizability to other settings is untested.
Evidence
- Peer-reviewedSurgical Laparoscopy, Endoscopy & Percutaneous Techniques2026-10-01
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Truvace Impact Record TRV-2026-1469, v1: “A Predictive Model Integrating Coagulation Profiles for Preoperative Risk Stratification of Difficult Single-Incision Laparoscopic Cholecystectomy: A Retrospective Cohort Study Based on Explainable Machine Learning.” Truvace, 2026-10-15. /record/TRV-2026-1469 (accessed at citation time). sha256 fb147166465ff806…
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