Prospective multicenter external validation of postoperative mortality prediction tools in patients undergoing emergency laparotomy

Abstract

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.

Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality After Emergency Laparotomy

“…The validation of POTTER in patients aged over 65 undergoing any emergency surgery showed AUC of 0.80 28 . The POTTER has demonstrated an AUC of between 0.77 and 0.84 in predicting 30-day mortality in patients undergoing EL 30,31 . The HAS model has the limitation of being linear in nature and does not have the advantages of artificial intelligence-based predictive tools.…”

“…28 The POTTER has demonstrated an AUC of between 0.77 and 0.84 in predicting 30-day mortality in patients undergoing EL. 30,31 The HAS model has the limitation of being linear in nature and does not have the advantages of artificial intelligence-based predictive tools. However, the predictive performance of the HAS model shown in this study was better than nonlinear models in patients undergoing EL.…”

Development and internal validation of a clinical prediction model for serious complications after emergency laparotomy

“…To our knowledge, there is limited research on predictive analytics for EL complications and the only rival model for SEAL is the ACS-NSQIP, which is largely endorsed by the surgeons' community to drive surgical decision making and informed consent. In a previous investigation, we showed that ACS-NSQIP produced accurate predictions of postoperative mortality in the HELAS cohort and outperformed three other commonly cited prognostic models [ 38 ]. Despite our endorsement, we found that using ACS-NSQIP in practice is burdensome and requires entering data for a large number of 21 preoperative factors in an external online calculator, which is proprietary with undisclosed equation.…”