A clinical decision web to predict ICU admission or death for patients hospitalised with Covid-19 using machine learning algorithms

Aznar-Gimeno, Rocío ; Esteban, Luis M. ; Labata-Lezaun, Gorka ; Hoyo-Alonso, Rafael del ; Abadia-Gallego, David (Universidad de Zaragoza) ; Paño-Pardo, Ramón (Universidad de Zaragoza) ; Esquillor-Rodrigo, M. José ; Lanas, Ángel (Universidad de Zaragoza) ; Serrano, Trinidad (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.3390/ijerph18168677

Año: 2021

Publicado en: International journal of environmental research and public health 18, 16 (2021), 8677 [20 pp.]
ISSN: 1661-7827

Factor impacto JCR: 4.614 (2021)

Categ. JCR: PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH rank: 45 / 182 = 0.247 (2021) - Q1 - T1
Categ. JCR: ENVIRONMENTAL SCIENCES rank: 100 / 279 = 0.358 (2021) - Q2 - T2
Categ. JCR: PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH rank: 71 / 210 = 0.338 (2021) - Q2 - T2

Factor impacto CITESCORE: 4.5 - Medicine (Q2) - Environmental Science (Q2)

Factor impacto SCIMAGO: 0.814 - Pollution (Q1) - Health, Toxicology and Mutagenesis (Q1)

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Area Medicina (Dpto. Medicina, Psiqu. y Derm.)