Related Experiment Video
Updated: Aug 12, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Prospective multicenter external validation of postoperative mortality prediction tools in patients undergoing
Stamatios Kokkinakis1, Evangelos I Kritsotakis, Konstantinos Paterakis
1From the Department of General Surgery (S.K., K.P., G.-A.K., V.M., A.K., M.P., E.C., K.L.), University Hospital of Heraklion, University of Crete, School of Medicine; Laboratory of Biostatistics, University of Crete, School of Medicine (E.I.K.); Department of Surgical Oncology, University Hospital of Heraklion, University of Crete, School of Medicine (C.S.A., O.Z.), Heraklion; Department of Surgery, University General Hospital of Patras, School of Medicine (N.D., I.K., D.K.), University of Patras, Patras, Greece; Department of Surgery, General Hospital of Nicosia, School of Medicine (N.G., G.K., I.P., P.P., K.F.), University of Cyprus, Nicosia, Cyprus; First Department of Surgery (D.S., A.S.) and Second Propaedeutic Department of Surgery (I.M.P.), Laikon General Hospital, National and Kapodistrian University of Athens; Department of Surgery, University General Hospital Attikon, School of Medicine (K.N., M.P., N.V.M., I.M.), University of Athens, Athens; Department of Surgery (E.L., G.D.), General Hospital of Volos, Volos, Greece; Department of Surgery (D.P., V.N.), General Hospital of Trikala, Trikala; Department of Surgery (G.K.G., G.P.-G., K.T.), University Hospital of Ioannina, Ioannina, Greece; Department of Surgery, Ippokrateion General Hospital of Thessaloniki, School of Medicine (G.Z., S.T., I.P.), Aristotle University of Thessaloniki, Thessaloniki; Second Department of Surgery (G.S., G.G.), Evangelismos General Hospital, Athens; and Department of Surgery, University General Hospital of Alexandroupolis, School of Medicine (M.K., K.K., M.M.), University of Thrace, Alexandroupolis, Greece.
Background:
Accurate preoperative risk assessment in emergency laparotomy (EL) is valuable for informed decision making and rational use of resources. Available risk prediction tools have not been validated adequately across diverse health care settings. Herein, we report a comparative external validation of four widely cited prognostic models.
Methods:
A multicenter cohort was prospectively composed of consecutive patients undergoing EL in 11 Greek hospitals from January 2020 to May 2021 using the National Emergency Laparotomy Audit (NELA) inclusion criteria. Thirty-day mortality risk predictions were calculated using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), NELA, Portsmouth Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (P-POSSUM), and Predictive Optimal Trees in Emergency Surgery Risk tools. Surgeons' assessment of postoperative mortality using predefined cutoffs was recorded, and a surgeon-adjusted ACS-NSQIP prediction was calculated when the original model's prediction was relatively low. Predictive performances were compared using scaled Brier scores, discrimination and calibration measures and plots, and decision curve analysis. Heterogeneity across hospitals was assessed by random-effects meta-analysis.
Results:
A total of 631 patients were included, and 30-day mortality was 16.3%. The ACS-NSQIP and its surgeon-adjusted version had the highest scaled Brier scores. All models presented high discriminative ability, with concordance statistics ranging from 0.79 for P-POSSUM to 0.85 for NELA. However, except the surgeon-adjusted ACS-NSQIP (Hosmer-Lemeshow test, p = 0.742), all other models were poorly calibrated ( p < 0.001). Decision curve analysis revealed superior clinical utility of the ACS-NSQIP. Following recalibrations, predictive accuracy improved for all models, but ACS-NSQIP retained the lead. Between-hospital heterogeneity was minimum for the ACS-NSQIP model and maximum for P-POSSUM.
Conclusion:
The ACS-NSQIP tool was most accurate for mortality predictions after EL in a broad external validation cohort, demonstrating utility for facilitating preoperative risk management in the Greek health care system. Subjective surgeon assessments of patient prognosis may optimize ACS-NSQIP predictions.
Level Of Evidence:
Diagnostic Test/Criteria; Level II.
More Related Videos
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025