Machine Learning-Based Identification of Risk Factors for ICU Mortality in 8,902 Critically Ill Patients with Pandemic Viral Infection
Papiol, Elisabeth (Universitat Autònoma de Barcelona)
Ferrer, Ricard (Universitat Autònoma de Barcelona. Departament de Medicina)
Ruíz-Rodríguez, Juan Carlos (Hospital Universitari Vall d'Hebron)
Díaz, Emili (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Zaragoza, Rafael (Hospital Universitari Doctor Peset (València))
Borges-Sa, Marcio (Hospital Universitari Son Llàtzer (Palma de Mallorca, Balears))
Berrueta, Julen (Hospital Universitari Joan XXIII de Tarragona)
Gómez, Josep (Hospital Universitari Joan XXIII de Tarragona)
Bodí, María (Hospital Universitari Joan XXIII de Tarragona)
Sancho, Susana (Hospital Universitari i Politècnic La Fe (València))
Suberviola, Borja (Hospital Universitario Marqués de Valdecilla (Santander, Cantabria))
Trefler, Sandra (Hospital Universitari Joan XXIII de Tarragona)
Rodríguez, Alejandro (Hospital Universitari Joan XXIII de Tarragona)

Data:	2025
Resum:	Abstract: The SARS-CoV-2 and influenza A(H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may differ depending on the type of analysis used. Our aim is to compare the risk factors and performance of a linear model (multivariable logistic regression, GLM) with a non-linear model (random forest, RF) in a large national cohort. Methods: A retrospective analysis was performed on a multicenter database including 8902 critical patients with influenza A(H1N1)pdm09 or COVID-19 admitted to 184 Spanish ICUs. Demographic, clinical, laboratory and microbiological data from the first 24 hours were used. Prediction models were built using GLM and RF. The performance of the GLM was evaluated by area under the ROC curve (AUC), precision, sensitivity and specificity, while the RF by out-of-bag (OOB) error and accuracy. In addition, in the RF, the importance of the variables in terms of accuracy reduction (AR) and Gini index reduction (GI) was determined. Results: Overall mortality in the ICU was 25. 8%. Model performance was similar, AUC =76% for GLM and AUC 75. 6% for RF. GLM identified 17 independent risk factors, while RF identified 19 for AR and 23 for GI. Thirteen variables were found to be important in both models. Laboratory variables such as procalcitonin, white blood cells, lactate or D-dimer were not significant in GLM but were significant in RF. On the contrary, acute kidney injury and the presence of Acinetobacter spp. were important variables in the GLM but not in the RF. Conclusions: Although the performance of linear and non-linear models was similar, different risk factors were determined depending on the model used. This alerts clinicians to the limitations and usefulness of studies limited to a single type of model.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	ICU Mortality ; Pandemic viruses ; Mortality risk factors ; Random Forest ; Generalized linear 49 model
Publicat a:	Journal of clinical medicine, Vol. 14, Num. 15 (July 2025) , ISSN 2077-0383

DOI: 10.3390/jcm14155383
PMID: 40807005

16 p, 1.4 MB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut d’Investigació i Innovació Parc Taulí (I3PT)
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