Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

[EN]Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventil...

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Detalles Bibliográficos
Autores: Marcos Martín, Miguel, Belhassen-García, Moncef, Sánchez-Puente, Antonio, Sampedro-Gómez, Jesús, Azibeiro, Raúl, Dorado Díaz, Pedro Ignacio, Marcano-Millán, Edgar, García-Vidal, Carolina, Moreiro Barroso, María Teresa, Cubino Bóveda, Noelia, Pérez-García, María-Luisa, Rodríguez-Alonso, Beatriz, Encinas-Sánchez, Daniel, Peña-Balbuena, Sonia, Sobejano-Fuertes, Eduardo, Inés, Sandra, Carbonell, Cristina, López Parra, Miriam, Andrade-Meira, Fernanda, López-Bernús, Amparo, Lorenzo, Catalina, Carpio, Adela, Polo-San-Ricardo, David, Sánchez Hernández, Miguel Vicente, Borrás Beato, Rafael, Sagredo-Meneses, Víctor, Sánchez Fernández, Pedro Luis, Soriano, Alex, Martín Oterino, José Ángel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/161982
Acceso en línea:http://hdl.handle.net/10366/161982
Access Level:acceso abierto
Palabra clave:Machine learning
Covid-19
Disease score
Hospitalized
Artificial intelligence
SARS-CoV-2
Area Under Curve
Aged
Adult
Risk Assessment
Forecasting
Humans
Middle Aged
Hospitalization
Severity of Illness Index
Respiration
Cohort Studies
ROC Curve
Retrospective Studies
humanos
índice de gravedad de la enfermedad
anciano
mediana edad
curva ROC
estudios retrospectivos
adulto
hospitalización
evaluación de riesgos
estudios de cohortes
predicción
respiración
área bajo la curva
Descripción
Sumario:[EN]Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

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