Evidence map›Paper›PMID 37715216›Full record

Trial reportEuropean journal of medical research2023

Urine output as one of the most important features in differentiating in-hospital death among patients receiving extracorporeal membrane oxygenation: a random forest approach.

Sheng-Nan Chang, Nian-Ze Hu, Jo-Hsuan Wu, Hsun-Mao Cheng, James L Caffrey, Hsi-Yu Yu, Yih-Sharng Chen, Jiun Hsu, Jou-Wei Lin

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in European journal of medical research, 2023. 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

9 authors.

Sheng-Nan Chang *Cardiovascular Center, National Taiwan University Hospital Yunlin Branch, Douliu City, Yunlin County, Taiwan.
Nian-Ze Hu *Department of Information Management, National Formosa University, Huwei, Yunlin, Taiwan. drhu@nfu.edu.tw.
Jo-Hsuan WuShiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
Hsun-Mao ChengOffice of Medical Informatics, National Taiwan University Hospital Yunlin Branch, Douliu City, Yunlin County, Taiwan.
James L CaffreyPhysiology and Cardiovascular Research Institute, University of North Texas Health Science Center, Fort Worth, TX, USA.
Hsi-Yu YuDepartment of Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Yih-Sharng ChenDepartment of Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Jiun HsuCardiovascular Center, National Taiwan University Hospital Yunlin Branch, Douliu City, Yunlin County, Taiwan. quichehsu@gmail.com.
Jou-Wei LinCardiovascular Center, National Taiwan University Hospital Yunlin Branch, Douliu City, Yunlin County, Taiwan.

Funding

Ministry of Science and Technology, Taiwan 110-2221-E-002-161National Science and Technology Council 111-2221-E-002-115National Taiwan University Hospital Yunlin Branch YLH110. A003
6 · The paper itself

Abstract

backgroundIt is common to support cardiovascular function in critically ill patients with extracorporeal membrane oxygenation (ECMO). The purpose of this study was to identify patients receiving ECMO with a considerable risk of dying in hospital using machine learning algorithms.

methodsA total of 1342 adult patients on ECMO support were randomly assigned to the training and test groups. The discriminatory power (DP) for predicting in-hospital mortality was tested using both random forest (RF) and logistic regression (LR) algorithms.

resultsUrine output on the first day of ECMO implantation was found to be one of the most predictive features that were related to in-hospital death in both RF and LR models. For those with oliguria, the hazard ratio for 1 year mortality was 1.445 (p < 0.001, 95% CI 1.265-1.650).

conclusionsOliguria within the first 24 h was deemed especially significant in differentiating in-hospital death and 1 year mortality.

Indexed as

Extracorporeal Membrane OxygenationRandom ForestAdultAlgorithmsHospital MortalityHumansOliguriaExtracorporeal membrane oxygenationMachine learning algorithmOliguriaRandom forest

Identifiers

PMID37715216
PMCPMC10503185

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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.