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Comparative Evaluation of Mortality Risk Prediction Tools After Emergency Laparotomy

Year: 2026

Type: article

Abstract: ABSTRACT Background Accurate pre‐operative prediction of mortality after emergency laparotomy (EL) is essential as it may facilitate shared decision‐making. The aim of our study was to compare the prognostic accuracy of three risk prediction tools following EL. Methods This retrospective cohort study included all consecutive adult patients (≥ 18 years) who underwent EL for any non‐traumatic indication between December 2020 and October 2024 at Wellington Hospital (Aotearoa New Zealand). Demographic, physiological, comorbidity, and imaging data were extracted to enable risk calculation. Risk of 30‐day mortality was determined using the National Emergency Laparotomy Audit prognostic model (P‐NELA), American College of Surgeons National Surgical Quality Improvement Programme (ACS‐NSQIP), and the New Zealand Surgical Risk (NZRISK) tool. Calibration was assessed using McFadden's pseudo‐ R 2 ( R 2 McF ≥ 0.40, excellent; ≥ 0.30, good; ≥ 0.20, moderate; < 0.20, poor) and accuracy was evaluated with the c ‐statistic (< 1.0, excellent; < 0.90, good; < 0.80, fair; < 0.70, poor). Results A total of 312 EL procedures were performed, with observed 30‐day mortality of 7.7%. P‐NELA was most accurate in predicting 30‐day mortality after EL ( c = 0.91), followed closely by the ACS‐NSQIP tool ( c = 0.88; p = 0.43). NZRISK was the least accurate ( c = 0.75) relative to both P‐NELA ( p = 0.0005) and ACS‐NSQIP ( p = 0.0008). Compared with ACS‐NSQIP ( R 2 McF = 0.25) and NZRISK ( R 2 McF = 0.062), P‐NELA was the only well‐calibrated risk prediction tool ( R 2 McF = 0.38). Conclusions P‐NELA was most accurate in predicting 30‐day mortality after EL, while NZRISK performed least reliably in our cohort. These findings highlight the importance of providing external validation for different risk prediction tools within diverse populations to improve peri‐operative planning.

Source: ANZ Journal of Surgery

DOI: 10.1111/ans.70730

Authors: Sameer Bhat, Campbell Hodges, Shauna O'Brien, Wiremu MacFater, Anthony Y. Lin, Ahmed W. H. Barazanchi