ArticleANZ journal of surgery2026
Comparative Evaluation of Mortality Risk Prediction Tools After Emergency Laparotomy.
Article in ANZ journal of surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Comparative Evaluation of Mortality Risk Prediction Tools After Emergency Laparotomy.ANZ journal of surgery · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundAccurate 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.
methodsThis 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
resultsA 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
conclusionsP-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.
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