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Applied Sciences
Volume 15
Issue 9
10.3390/app15094823
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Predictive Models of Patient Severity in Intensive Care Units Based on Serum Cytokine Profiles: Advancing Rapid Analysis

by
Cristiana P. Von Rekowski
1,2,3,
Tiago A. H. Fonseca
1,2,3,
Rúben Araújo
1,2,3,
Ana Martins
2,4,5,
Iola Pinto
2,6,
M. Conceição Oliveira
7,
Gonçalo C. Justino
7,
Luís Bento
8,9 and
Cecília R. C. Calado
2,10,*
1
NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal
2
ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
3
CHRC—Comprehensive Health Research Centre, Universidade NOVA de Lisboa, 1150-082 Lisbon, Portugal
4
CIMOSM—Centro de Investigação em Modelação e Optimização de Sistemas Multifuncionais, ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
5
CIMA—Research Centre for Mathematics and Applications, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal
6
NOVA Math—Center for Mathematics and Applications, NOVA FCT—NOVA School of Science and Technology, Universidade NOVA de Lisboa, Largo da Torre, 2829-516 Caparica, Portugal
7
Centro de Química Estrutural—Institute of Molecular Sciences, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
8
Intensive Care Department, ULSSJ—Unidade Local de Saúde de São José, Rua José António Serrano, 1150-199 Lisbon, Portugal
9
Integrated Pathophysiological Mechanisms, CHRC—Comprehensive Health Research Centre, NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056 Lisbon, Portugal
10
iBB—Institute for Bioengineering and Biosciences, i4HB—The Associate Laboratory Institute for Health and Bioeconomy, IST—Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(9), 4823; https://doi.org/10.3390/app15094823 (registering DOI)
Submission received: 12 March 2025 / Revised: 19 April 2025 / Accepted: 22 April 2025 / Published: 26 April 2025
(This article belongs to the Special Issue Advances in Biological and Biomedical Optoelectronics)
Versions Notes

Abstract

Predicting disease states and outcomes—and anticipating the need for specific procedures—enhances the efficiency of patient management, particularly in the dynamic and heterogenous environments of intensive care units (ICUs). This study aimed to develop robust predictive models using small sets of blood analytes to predict disease severity and mortality in ICUs, as fewer analytes are advantageous for future rapid analyses using biosensors, enabling fast clinical decision-making. Given the substantial impact of inflammatory processes, this research examined the serum profiles of 25 cytokines, either in association with or independent of nine routine blood analyses. Serum samples from 24 male COVID-19 patients admitted to an ICU were divided into three groups: Group A, including less severe patients, and Groups B and C, that needed invasive mechanical ventilation (IMV). Patients from Group C died within seven days after the current analysis. Naïve Bayes models were developed using the full dataset or with feature subsets selected either through an information gain algorithm or univariate data analysis. Strong predictive models were achieved for IMV (AUC = 0.891) and mortality within homogeneous (AUC = 0.774) or more heterogeneous (AUC = 0.887) populations utilizing two to nine features. Despite the small sample, these findings underscore the potential for effective prediction models based on a limited number of analytes.
Keywords: cytokine profiling; inflammatory biomarkers; intensive care unit; invasive mechanical ventilation; mortality; machine learning; prompt analyses cytokine profiling; inflammatory biomarkers; intensive care unit; invasive mechanical ventilation; mortality; machine learning; prompt analyses

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MDPI and ACS Style

Von Rekowski, C.P.; Fonseca, T.A.H.; Araújo, R.; Martins, A.; Pinto, I.; Oliveira, M.C.; Justino, G.C.; Bento, L.; Calado, C.R.C. Predictive Models of Patient Severity in Intensive Care Units Based on Serum Cytokine Profiles: Advancing Rapid Analysis. Appl. Sci. 2025, 15, 4823. https://doi.org/10.3390/app15094823

AMA Style

Von Rekowski CP, Fonseca TAH, Araújo R, Martins A, Pinto I, Oliveira MC, Justino GC, Bento L, Calado CRC. Predictive Models of Patient Severity in Intensive Care Units Based on Serum Cytokine Profiles: Advancing Rapid Analysis. Applied Sciences. 2025; 15(9):4823. https://doi.org/10.3390/app15094823

Chicago/Turabian Style

Von Rekowski, Cristiana P., Tiago A. H. Fonseca, Rúben Araújo, Ana Martins, Iola Pinto, M. Conceição Oliveira, Gonçalo C. Justino, Luís Bento, and Cecília R. C. Calado. 2025. "Predictive Models of Patient Severity in Intensive Care Units Based on Serum Cytokine Profiles: Advancing Rapid Analysis" Applied Sciences 15, no. 9: 4823. https://doi.org/10.3390/app15094823

APA Style

Von Rekowski, C. P., Fonseca, T. A. H., Araújo, R., Martins, A., Pinto, I., Oliveira, M. C., Justino, G. C., Bento, L., & Calado, C. R. C. (2025). Predictive Models of Patient Severity in Intensive Care Units Based on Serum Cytokine Profiles: Advancing Rapid Analysis. Applied Sciences, 15(9), 4823. https://doi.org/10.3390/app15094823

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