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

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...

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Vydáno v:European journal of medical research Ročník 28; číslo 1; s. 1 - 11
Hlavní autoři: Chang, Sheng-Nan, Hu, Nian-Ze, Wu, Jo-Hsuan, Cheng, Hsun-Mao, Caffrey, James L., Yu, Hsi-Yu, Chen, Yih-Sharng, Hsu, Jiun, Lin, Jou-Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 15.09.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Algorithms
Analysis
Biomedicine
Cardiopulmonary resuscitation
CPR
Data mining
Extracorporeal membrane oxygenation
Health aspects
Infectious Diseases
Internal Medicine
Machine learning
Machine learning algorithm
Medical prognosis
Medical research
Medicine
Medicine & Public Health
Medicine, Experimental
Missing data
Mortality
Oliguria
Oncology
Patients
Random forest
Regression analysis
Surgery
Survival analysis
Taiwan
Urine
Variables
Taiwan
United States > US
Extracorporeal membrane oxygenation
Random forest
Oliguria
Machine learning algorithm
ISSN:2047-783X, 0949-2321, 2047-783X
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Shrnutí: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.
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ISSN:2047-783X
0949-2321
2047-783X
DOI:10.1186/s40001-023-01294-1