A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms

Rocío Aznar-Gimeno, Luis M. Esteban, Gorka Labata-Lezaun, Rafael del-Hoyo-Alonso, David Abadia-Gallego, J. Ramón Paño-Pardo, M. José Esquillor-Rodrigo, Ángel Lanas and M. Trinidad Serrano
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Rocío Aznar-Gimeno: Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain
Luis M. Esteban: Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor, 5, 50100 La Almunia de Doña Godina, Spain
Gorka Labata-Lezaun: Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain
Rafael del-Hoyo-Alonso: Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain
David Abadia-Gallego: Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain
J. Ramón Paño-Pardo: Infectious Disease Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain
M. José Esquillor-Rodrigo: Internal Medicine Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain
Ángel Lanas: Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain
M. Trinidad Serrano: Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain

IJERPH, 2021, vol. 18, issue 16, 1-20

Abstract: The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787–0.854) and accurate calibration (slope = 1, intercept = ?0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.

Keywords: COVID-19; ICU; mortality; machine learning; predictive model; clinical decision web tool (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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