Evidence map›Paper›PMID 39844951›Full record

ReviewSurgery in practice and science2024

Emergency laparotomy preoperative risk assessment tool performance: A systematic review.

Joseph N Hewitt, Thomas J Milton, Jack Jeanes, Ishraq Murshed, Silas Nann, Susanne Wells, Aashray K Gupta, Christopher D Ovenden, Joshua G Kovoor, Stephen Bacchi and 2 more

Abstract readReview
In one paragraph

Review in Surgery in practice and science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Artificial intelligence for predicting 30-day mortality after emergency laparotomy.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
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

12 authors.

Joseph N HewittThe University of Adelaide, Discipline of Surgery, The Queen Elizabeth Hospital, South Australia, Australia.
Thomas J MiltonDepartment of Surgery, Royal Adelaide Hospital, South Australia, Australia.
Jack JeanesCollege of Medicine and Public Health, Flinders University, South Australia, Australia.
Ishraq MurshedThe University of Adelaide, Discipline of Surgery, The Queen Elizabeth Hospital, South Australia, Australia.
Silas NannDepartment of Surgery, Gold Coast University Hospital, Queensland, Australia.
Susanne WellsThe University of Adelaide, Discipline of Surgery, The Queen Elizabeth Hospital, South Australia, Australia.
Aashray K GuptaDepartment of Surgery, Gold Coast University Hospital, Queensland, Australia.
Christopher D OvendenDepartment of Surgery, Royal Adelaide Hospital, South Australia, Australia.
Joshua G KovoorThe University of Adelaide, Discipline of Surgery, The Queen Elizabeth Hospital, South Australia, Australia.
Stephen BacchiThe University of Adelaide, Discipline of Surgery, The Queen Elizabeth Hospital, South Australia, Australia.
Christopher DobbinsDepartment of Surgery, Royal Adelaide Hospital, South Australia, Australia.
Markus I TrochslerThe University of Adelaide, Discipline of Surgery, The Queen Elizabeth Hospital, South Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Preoperative assessment of risk for emergency laparotomy may enhance decision making with regards to urgency or perioperative critical care admission and promote a more informed consent process for patients. Accordingly, we aimed to assess the performance of risk assessment tools in predicting mortality after emergency laparotomy. Methods: PubMed, Embase, the Cochrane Library and CINAHL were searched to 12 February 2022 for observational studies reporting expected mortality based on a preoperative risk assessment and actual mortality after emergency laparotomy. Study screening, data extraction, and risk of bias using the Downs and Black checklist were performed in duplicate. Data on setting, operation undertaken, expected and actual mortality rates were extracted. Meta-analysis was planned but not possible due to heterogeneity. This study is registered with PROSPERO, CRD42022299227. Results: From 10,168 records, 82 observational studies were included. 17 risk assessment tools were described, the most common of which were P-POSSUM (42 studies), POSSUM (13 studies), NELA (12 studies) and MPI (11 studies). Articles were published between 1990 and 2022 with the most common country of origin being the UK (33 studies) followed by India (11 studies). Meta-analysis was not possible. Observed mortality and expected mortality based on risk assessment is reported for each study and generally shows most studies show accurate risk prediction. Conclusions: This review synthesises available literature to characterise the performance of various risk assessment tools in predicting mortality after emergency laparotomy. Findings from this study may benefit those undertaking emergency laparotomy and future research in risk prediction.

Indexed as

Emergency laparotomyGeneral surgeryRisk assessment

Identifiers

PMID39844951
PMCPMC11750015

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.