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

Autor:	Marcos M; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Belhassen-García M; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Sánchez-Puente A; Department of Cardiology, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain.; CIBERCV, Instituto de Salud Carlos III, Madrid, Spain., Sampedro-Gomez J; Department of Cardiology, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain.; CIBERCV, Instituto de Salud Carlos III, Madrid, Spain., Azibeiro R; Department of Hematology, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Dorado-Díaz PI; Department of Cardiology, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain.; CIBERCV, Instituto de Salud Carlos III, Madrid, Spain., Marcano-Millán E; Department of Intensive Care Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., García-Vidal C; Department of Infectious Diseases, Hospital Clínic-Universitat de Barcelona, IDIBAPS, Barcelona, Spain., Moreiro-Barroso MT; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Cubino-Bóveda N; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Pérez-García ML; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Rodríguez-Alonso B; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Encinas-Sánchez D; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Peña-Balbuena S; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Sobejano-Fuertes E; Department of Hematology, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Inés S; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Carbonell C; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., López-Parra M; Department of Hematology, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Andrade-Meira F; Department of Infectious Diseases, Hospital Clínic-Universitat de Barcelona, IDIBAPS, Barcelona, Spain., López-Bernús A; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Lorenzo C; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Carpio A; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Polo-San-Ricardo D; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Sánchez-Hernández MV; Department of Anesthesiology and Reanimation, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Borrás R; Department of Emergency Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Sagredo-Meneses V; Department of Intensive Care Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain., Sanchez PL; Department of Cardiology, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain.; CIBERCV, Instituto de Salud Carlos III, Madrid, Spain., Soriano A; Department of Infectious Diseases, Hospital Clínic-Universitat de Barcelona, IDIBAPS, Barcelona, Spain., Martín-Oterino JÁ; Department of Internal Medicine, University Hospital of Salamanca-IBSAL, University of Salamanca, Salamanca, Spain.
Jazyk:	angličtina
Zdroj:	PloS one [PLoS One] 2021 Apr 21; Vol. 16 (4), pp. e0240200. Date of Electronic Publication: 2021 Apr 21 (Print Publication: 2021).
DOI:	10.1371/journal.pone.0240200
Abstrakt:	Background: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. Methods: 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. Results: 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. Conclusions: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients. Competing Interests: The authors have declared that no competing interests exist.
Databáze:	MEDLINE
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