Evidence map›Paper›PMID 36233806›Full record

ArticleJournal of clinical medicine2022

Mortality Prediction Model before Surgery for Acute Mesenteric Infarction: A Population-Based Study.

Shang-Wei Lin, Chung-Yen Chen, Yu-Chieh Su, Kun-Ta Wu, Po-Chin Yu, Yung-Chieh Yen, Jian-Han Chen

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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.

Shang-Wei LinDepartment of Surgery, E-Da Hospital, Kaohsiung 82445, Taiwan.
Chung-Yen ChenDepartment of Surgery, E-Da Hospital, Kaohsiung 82445, Taiwan.
Yu-Chieh SuSchool of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan.
Kun-Ta WuDepartment of Surgery, E-Da Hospital, Kaohsiung 82445, Taiwan.ORCID 0000-0001-7099-4301
Po-Chin YuDepartment of Surgery, E-Da Hospital, Kaohsiung 82445, Taiwan.
Yung-Chieh YenDepartment of Psychiatry, E-Da Hospital, Kaohsiung 82445, Taiwan.ORCID 0000-0002-2174-2244
Jian-Han ChenDepartment of Surgery, E-Da Hospital, Kaohsiung 82445, Taiwan.ORCID 0000-0001-7360-2238

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surgery for acute mesenteric infarction (AMI) is associated with high mortality. This study aimed to generate a mortality prediction model to predict the 30-day mortality of surgery for AMI. We included patients ≥18 years who received bowel resection in treating AMI and randomly divided into the derivation and validation groups. After multivariable analysis, the ‘Surgery for acute mesenteric infarction mortality score’ (SAMIMS) system was generated and was including age >62-year-old (3 points), hemodialysis (2 points), congestive heart failure (1 point), peptic ulcer disease (1 point), diabetes (1 point), cerebrovascular disease (1 point), and severe liver disease (4 points). The 30-day-mortality rates in the derivation group were 4.4%, 13.4%, 24.5%, and 32.5% among very low (0 point), low (1−3 point(s)), intermediate (4−6 points), and high (7−13 points)-risk patients. Compared to the very-low-risk group, the low-risk (OR = 3.332), intermediate-risk (OR = 7.004), and high-risk groups (OR = 10.410, p < 0.001) exhibited higher odds of 30-day mortality. We identified similar results in the validation group. The areas under the ROC curve were 0.677 and 0.696 in the derivation and validation groups. Our prediction model, SAMIMS, allowed for the stratification of the patients’ 30-day-mortality risk of surgery for acute mesenteric infarction.

Indexed as

acute mesenteric infarctionbowel resectionmortalitypreoperative risk factorsscoring systemsurgery

Identifiers

PMID36233806
PMCPMC9571294

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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.