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

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 healthcare settings. Herein, we report a comparative external validation of 4 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 (NELA) audit inclusion criteria. 30-day mortality risk predictions were calculated using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), NELA, Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (P-POSSUM) and Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tools. Surgeons’ assessment of postoperative mortality using pre-defined 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

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 healthcare system. Subjective surgeon assessments of patient prognosis may optimise ACS-NSQIP predictions.

Level of Evidence

Level II, Diagnostic test/criteria