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Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients

2020; Nature Portfolio; Volume: 10; Issue: 1 Linguagem: Inglês

10.1038/s41598-020-75651-z

2045-2322

Francisco Gudé, Vanessa Riveiro, Nuria Rodríguez‐Núñez, Jorge Ricoy, Óscar Lado‐Baleato, Tamara Lourido-Cebreiro, Carlos Rábade, Adriana Lama, Ana Casal, Romina Abelleira‐Paris, Lucía Ferreiro, Juan Suárez‐Antelo, María E. Toubes, Cristina Pou, Manuel Taboada, Felipe Calle-Velles, Placido Mayán-Conesa, María de Toro, Cristóbal Galbán-Rodríguez, J. Álvarez, Carmen Beceiro-Abad, Sonia Molinos‐Castro, Néstor Vázquez-Agra, María Pazo-Núñez, Emilio Páez-Guillán, Pablo Manuel Varela-García, Carmen Martínez-Rey, Hadrián Pernas-Pardavila, María Jesús Domínguez-Santalla, Martín Vidal-Vázquez, Ana T. Marques-Afonso, Arturo González‐Quintela, José Ramón González‐Juanatey, Antonio Pose, Luís Valdés,

Sepsis Diagnosis and Treatment

Abstract The prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO 2 , and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6–25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.