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

Journal
European journal of medical research
Journal Volume
28
Journal Issue
1
Pages
347
Date Issued
2023-09-15
Author(s)
SHENG-NAN CHANG 
Hu, Nian-Ze
Wu, Jo-Hsuan
Cheng, Hsun-Mao
Caffrey, James L
HSI-YU YU  
YIH-SHARNG CHEN 
Hsu, Jiun
JOU-WEI LIN 
DOI
10.1186/s40001-023-01294-1
URI
https://pubmed.ncbi.nlm.nih.gov/37715216/
https://scholars.lib.ntu.edu.tw/handle/123456789/636221
URL
https://api.elsevier.com/content/abstract/scopus_id/85171357298
Abstract
Background: It 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.

Methods: A 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.

Results: Urine 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).

Conclusions: Oliguria within the first 24 h was deemed especially significant in differentiating in-hospital death and 1 year mortality.
Subjects
Extracorporeal membrane oxygenation
Machine learning algorithm
Oliguria
Random forest
Publisher
BioMed Central Ltd
Type
journal article

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To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

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