Development and validation of a clinical score to estimate progression to severe or critical state in Covid-19 pneumonia hospitalized patients
Gude Sampedro, Francisco; RIVEIRO BLANCO, VANESSA; Rodríguez Núñez, Nuria; Ricoy Gabaldón, Jorge; Lado-Baleato, Óscar; Lourido Cebreiro, Tamara; Rábade Castedo, Carlos; Lama López, Adriana; CASAL MOURIÑO, ANA; Abelleira-París, Romina; Ferreiro Fernández, Lucía; Suárez Antelo, Juan; Toubes Navarro, Maria Elena; Pou, Cristina; TABOADA MUÑIZ, MANUEL; CALLE VELLES, FELIPE; Mayán-Conesa, Plácido; Pérez del Molino Bernal, María Luisa; Galbán Rodríguez, Cristobal; Álvarez Escudero, Julián; Beceiro Abad, María del Carmen; Molinos Castro, Sonia; Agra-Vázquez, Néstor; Pazo-Núñez, María; Páez-Guillán, Emilio; Varela García, Pablo Manuel; Martínez-Rey, Carmen; Pernas-Pardavila, Hadrián; Domínguez-Santalla, María J; Vidal-Vázquez, Martín; Marques-Afonso, Ana T; González Quintela, Arturo; González Juanatey, José Ramón; Pose Reino, Antonio; Valdés Cuadrado, Luis
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Type of content
DeCSneumonía | síndrome respiratorio agudo grave | coronavirus
MeSHCoronavirus | Severe Acute Respiratory Syndrome | Pneumonia
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, analytical, 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 1,152 patients presented with Covid-19 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 hours of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, 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 simple risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.
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