Evidence map›Paper›PMID 41181408›Full record

ArticleAnnals of medicine and surgery (2012)2025

Prediction of acute kidney injury after noncardiac surgery in intensive care unit by machine learning.

Jianxiao Chen, Tianyi Ai, Zishu Song, Weijun Zhang, Ting Yang, An Shi, Jiajia Tang, Peng Shao, Weiyan Zheng, Zhiqiang Shi and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of medicine and surgery (2012), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Jianxiao ChenDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Tianyi AiDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Zishu SongDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Weijun ZhangDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Ting YangDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
An ShiDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jiajia TangDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Peng ShaoDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Weiyan ZhengDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Zhiqiang ShiDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Mingli ZhuDepartment of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.ORCID https://orcid.org/0000-0002-0070-4556

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The study aimed to develop multiple machine learning models to predict acute kidney injury after noncardiac surgery (NCS-AKI) and screen for the best predictive model. Method: A total of 778 patients admitted to the surgical intensive care unit (SICU) were screened from July 2022 to December 2022. Six machine algorithms were used to predict early postoperative AKI. Results: A total of 704 patients were enrolled, 191 (27.1%) presented with early AKI. The survival rate of the AKI patients within 28 days was lower than that of the non-AKI (87.9% vs. 96.3%, log-rank test: Conclusion: NCS-AKI had a high incidence in the SICU, and AKI patients had a lower 28-day survival rate than non-AKI patients do. The SVM had a better ability to predict early postoperative AKI.

Indexed as

acute kidney injuryintensive care unitmachine learningnoncardiac surgeryprediction model

Identifiers

PMID41181408
PMCPMC12577871

What OpenQuestion holds

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Registered trials

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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.