Evidence map›Paper›PMID 42671572›Full record

ArticleEuropean journal of trauma and emergency surgery : official publication of the European Trauma Society2026

Artificial intelligence for predicting 30-day mortality after emergency laparotomy.

Sergei Bedrikovetski, Ishraq Murshed, Warren Seow, Zachary Bunjo, Jahan Singh De Fontgalland, Luke Traeger, Ryash Vather

Abstract read
In one paragraph

Article in European journal of trauma and emergency surgery : official publication of the European Trauma Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sergei BedrikovetskiDiscipline of Surgery, School of Medicine, College of Health, Adelaide University, Adelaide, SA, Australia. sergei.bedrikovetski@adelaide.edu.au.ORCID http://orcid.org/0000-0001-9330-625X
Ishraq MurshedDiscipline of Surgery, School of Medicine, College of Health, Adelaide University, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0001-5598-6397
Warren SeowDiscipline of Surgery, School of Medicine, College of Health, Adelaide University, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0002-5024-1000
Zachary BunjoDiscipline of Surgery, School of Medicine, College of Health, Adelaide University, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0002-1398-3857
Jahan Singh De FontgallandSchool of Public Health, College of Health, Adelaide University, Adelaide, SA, Australia.
Luke TraegerDiscipline of Surgery, School of Medicine, College of Health, Adelaide University, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0002-0327-7334
Ryash VatherDiscipline of Surgery, School of Medicine, College of Health, Adelaide University, Adelaide, SA, Australia.ORCID http://orcid.org/0000-0002-9878-3597

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAccurate pre-operative risk prediction in emergency laparotomy (EL) is essential for appropriate resource allocation and improve patient outcomes. This study aimed to establish the accuracy of an artificial intelligence (AI) model in predicting 30-day mortality after EL using a national dataset.

methodsData was extracted from the Australian and New Zealand Emergency Laparotomy Audit-Quality Improvement (ANZELA-QI) database from 1 July 2018 to 24 July 2023. AI models were created employing multilayer perceptron (MLP) and radial basis function (RBF) architectures. Extended AI models were developed using all available pre-operative variables and simplified AI models were developed using statistically significant variables identified through univariate analysis. We assessed the performance of AI models with that of a multivariate logistic regression (LR) and the UK-based National Emergency Laparotomy Audit (NELA) models using the area under the receiver operator characteristic curve (AUROC).

resultsData from 8293 ELs were included in the model and were randomly divided into a training dataset of 6619 (80%) and a testing dataset of 1674 (20%). There were 537 (6.5%) deaths 30 days after EL. In the testing dataset, the NELA model performed the best (AUROC 0.836), followed by the multivariate LR model (0.824), MLP simplified (0.817). MLP extended (0.802), RBF extended (0.719), and RBF simplified (0.672). Pairwise comparisons showed no significant difference in discrimination among the NELA, multivariate LR and MLP models.

conclusionPre-operative MLP and multivariate LR models demonstrated comparable performance to the NELA model in predicting 30-day mortality after EL.

Indexed as

Artificial IntelligenceLaparotomyAustraliaEmergenciesFemaleHumansLogistic ModelsMaleMultilayer PerceptronsNew ZealandPrediction AlgorithmsPredictive Learning ModelsRisk AssessmentROC CurveArtificial intelligenceEmergency laparotomyMortalityNational emergency laparotomy audit

Identifiers

PMID42671572
PMCPMC13529842

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.