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. 2022 Feb 6;4(2):100162.
doi: 10.1016/j.opresp.2022.100162. eCollection 2022 Apr-Jun.

Risk Factors of Mortality in Hospitalized Patients With COVID-19 Applying a Machine Learning Algorithm

Irene Nieto-Codesido  1 , Uxio Calvo-Alvarez  1 , Carmen Diego  1 , Z Hammouri  2 , Narmeen Mallah  3 , María José Ginzo-Villamayor  4 , Francisco Javier Salgado  5   6 , José Martín Carreira  3 , Carlos Rábade  7 , Gema Barbeito  8 , Miguel Ángel Gonzalez-Perez  9 , Francisco Javier Gonzalez-Barcala  3   6   7   10
Affiliations

Risk Factors of Mortality in Hospitalized Patients With COVID-19 Applying a Machine Learning Algorithm

Irene Nieto-Codesido et al. Open Respir Arch. .

Abstract
in English, Spanish

Introduction: Risk stratification of patients with COVID-19 can be fundamental to support clinical decision-making and optimize resources. The objective of our study is to identify among the routinely tested clinical and analytical parameters those that would allow us to determine patients with the highest risk of dying from COVID-19.

Material and methods: We carried out a retrospective cohort multicentric study by consecutively, including hospitalized patients with COVID-19 admitted in any of the 11 hospitals in the healthcare network of HM Hospitals-Spain. We collected the clinical, demographic, analytical, and radiological data from the patient's medical records.To assess each of the biomarkers' predictive impact and measure the statistical significance of the variables involved in the analysis, we applied a random forest with a permutation method. We used the similarity measure induced by a previously classification model and adjusted the k-groups clustering algorithm based on the energy distance to stratify patients into a high and low-risk group. Finally, we adjusted two optimal classification trees to have a schematic representation of the cut-off points.

Results: We included 1246 patients (average age of 65.36 years, 62% males). During the study one hundred sixty-eight patients (13%) died. High values of age, D-Dimer, White Blood Cell, Na, CRP, and creatinine represent the factors that identify high-risk patients who would die.

Conclusions: Age seems to be the primary predictor of mortality in patients with SARS-CoV-2 infection, while the impact of acute phase reactants and blood cellularity is also highly relevant.

Introducción: La estratificación del riesgo de los pacientes con COVID-19 puede ser fundamental para apoyar la toma de decisiones clínicas y optimizar los recursos. El objetivo de nuestro estudio es identificar, entre los parámetros clínicos y analíticos probados de forma rutinaria, aquellos que nos permitirían determinar a los pacientes con mayor riesgo de morir por COVID-19.

Material y métodos: Se realizó un estudio multicéntrico de cohorte retrospectiva de forma consecutiva, incluyendo pacientes hospitalizados con COVID-19 ingresados en cualquiera de los 11 hospitales de la red sanitaria de HM Hospitales-España.Los datos clínicos, demográficos, analíticos y radiológicos se recopilaron de las historias clínicas de los pacientes.Para evaluar el impacto predictivo de cada uno de los biomarcadores y medir la significación estadística de las variables involucradas en el análisis, se aplicó un bosque aleatorio con un método de permutación. Utilizamos la medida de similitud inducida por un modelo de clasificación previo, y ajustamos el algoritmo de agrupación de grupos k en función de la distancia de energía para estratificar a los pacientes en un grupo de alto y bajo riesgo. Finalmente, ajustamos 2 árboles de clasificación óptimos para tener una representación esquemática de los puntos de corte.

Resultados: Se incluyeron 1.246 pacientes (edad promedio de 65,36 años, 62% varones). Durante el estudio murieron 168 pacientes (13%). Los factores que identifican a los pacientes de alto riesgo de mortalidad son los valores elevados de edad, dímero D, glóbulos blancos, Na, PCR y creatinina.

Conclusiones: La edad parece ser el principal predictor de mortalidad en pacientes con infección por SARS-CoV-2, mientras que el impacto de los reactantes de fase aguda y la celularidad sanguínea también es muy relevante.

Keywords: Age; Gender; Machine learning; Prognosis; Severity.

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Figures

Fig. 1
Fig. 1
Predictive capacity of each variable, according to Random Forest. The values are normalized by the age score, the variable with the highest predictive power.
Fig. 2
Fig. 2
(a) Result of estimating the optimal classification tree that discriminates between the risk and non-risk group. (b) Results of estimation of the optimal classification tree that discriminates between the risk patients who died and lived, and the patients included in the non-risk group.
Fig. 3
Fig. 3
Estimation of the density function in continuous biomarkers for patients that belong in three groups under consideration: (i) patients who belong to the risk group and live; (ii) patients who belong to the risk group and die, and (iii) patients who belong to the non-risk group. Continuous line, patients belong to the non-risk group. Dotted line, patients who belong to the risk group and die. Another case, patients who belong to the risk group and do not die.
See this image and copyright information in PMC

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