Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data - A Machine Learning Approach

Aug 3, 2026

Delirium is a common but often underrecognized complication in critically ill patients, linked to prolonged ICU stays, cognitive impairment, and higher mortality. Because its causes are multifactorial and symptoms fluctuate, early prediction remains challenging, limiting timely interventions. Using routinely available ICU admission information, this study builds machine learning models to predict delirium risk in 426 SARS-CoV-2 patients from a prospective cohort, combining demographic, clinical, and laboratory data. Five models are trained with information-gain feature selection and evaluated via 10-fold cross-validation, with Naïve Bayes achieving moderate performance and improved interpretability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Abstract

Introduction

Article Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach Ana Viegas 1,2,3,4,5, *, Cristiana P. Von Rekowski 1,2,6, Rúben Araújo 1,2,6, Miguel Viana-Baptista 1,7,8, Maria Paula Macedo 1,9 and Luís Bento 1,2,10, * Academic Editor: Gabriele Melegari Received: 31 May 2025 Revised: 26 June 2025 Accepted: 27 June 2025 Published: 30 June 2025 Citation: Viegas, A.; Von Rekowski, C.P.; Araújo, R.; Viana-Baptista, M.; Macedo, M.P.; Bento, L. Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach. Life 2025, 15, 1045. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)life15071045 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/lice](https://creativecommons.org/lice)[nses/by/4.0/](nses/by/4.0/)) . 1 NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal 2 CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1150-082 Lisbon, Portugal 3 ESTeSL—Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa, Avenida D. João II, Lote 4.69.01, Parque das Nações, 1990-096 Lisbon, Portugal 4 H&TRC—Health & Technology Research Center, ESTeSL—Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa, Avenida D. João II, Lote 4.69.01, Parque das Nações, 1990-096 Lisbon, Portugal 5 Neurosciences Area, Clinical Neurophysiology Unit, ULSSJ—Unidade Local de Saúde São José, Rua José António Serrano, 1150-199 Lisbon, Portugal 6 ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal 7 Neurology Department, ULSLO—Unidade Local de Saúde de Lisboa Ocidental, Rua da Junqueira 126, 1349-019 Lisbon, Portugal 8 CCAL—Centro Clínico Académico de Lisboa, NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal 9 iNOVA4Health—Advancing Precision Medicine, NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal 10 Intensive Care Department, ULSSJ—Unidade Local de Saúde São José, Rua José António Serrano, 1150-199 Lisbon, Portugal * Correspondence: [a2020449@nms.unl.pt](a2020449@nms.unl.pt) (A.V.); [luis.bento@ulssjose.min-saude.pt](luis.bento@ulssjose.min-saude.pt) (L.B.) Abstract Delirium is a common and underrecognized complication among critically ill patients, associated with prolonged ICU stays, cognitive dysfunction, and increased mortality. Its multifactorial causes and fluctuating course hinder early prediction, limiting timely management. Predictive models based on data available at ICU admission may help to identify high-risk patients and guide early interventions. This study evaluated machine learning models used to predict delirium in critically ill patients with SARS-CoV-2 infections using a prospective cohort of 426 patients. The dataset included demographic characteristics, clinical data (e.g., comorbidities, medication, reason for ICU admission, interventions), and routine lab test results. Five models—Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Naïve Bayes—were developed using 112 features. Feature selection relied on Information Gain, and model performance was assessed via 10-fold cross-validation. The Naïve Bayes model showed moderate predictive performance and high interpretability, achieving an AUC of 0.717, accuracy of 65.3

FAQs

Why is ICU delirium difficult to predict in critically ill patients?

ICU delirium has multifactorial causes and a fluctuating course, which complicates early prediction. In addition, risk assessment can be limited by subjective screening and the lack of a universally accepted diagnostic standard.

What data inputs were used to predict delirium at ICU admission?

The study used demographic characteristics, clinical variables (including comorbidities, medications, reason for ICU admission, and interventions), and routine laboratory test results.

Which machine learning models were evaluated and what was the best-performing approach?

Five models were developed: Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Naïve Bayes. The Naïve Bayes model showed moderate predictive performance with an AUC of 0.717 and relatively high interpretability.

Aug 3, 2026

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