Development and Validation of a Clinical Score to Estimate Progression to Severe or Crítical State in COVID-19 Pneumonia Hospitalized Patients

Autor: Jorge Ricoy Gabaldón, María Jesús Domínguez-Santalla, Ana T. Marques-Afonso, Carmen Martínez-Rey, Nuria Rodríguez-Núñez, Julián Álvarez Escudero, Luis Valdés, Antonio Pose, Óscar Lado-Baleato, María Pazo-Núñez, Cristobal Galban, Carlos Rábade Castedo, Martín Vidal-Vázquez, Cristina Pou Álvarez, María Luisa Pérez del Molino Bernal, Adriana Lama Lopez, Plácido Mayán Conesa, Felipe Calle Velles, Pablo Manuel Varela-García, Ana Casal Mouriño, Romina Abelleira, Vanessa Blanco, Néstor Agra-Vázquez, Sonia Molinos-Castro, María E. Toubes, Juan Suárez-Antelo, Manuel Taboada Muñiz, Arturo Gonzalez-Quintela, Francisco Gude, Tamara Lourido Cebreiro, Carmen Beceiro-Abad, Hadrián Pernas-Pardavila, Emilio Páez-Guillán, Lucía Ferreiro
Rok vydání: 2020
Předmět:
Zdroj: SSRN Electronic Journal.
ISSN: 1556-5068
Popis: Background: 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. Methods: 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. Findings: 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. Interpretation: A simple risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis. Funding Statement: This project was funded by the Carlos III Health Institute, Spain, Grant/Award Number: COV20/00404; Ministry of Economy and Competitiveness (SPAIN) and the European Regional Development Fund (FEDER). Declaration of Interests: The authors declare no conflicts of interest associated with this publication. Ethics Approval Statement: The study was conducted in accordance with the guidelines of the Declaration of Helsinki and the principles of good clinical practice and was approved by the Institutional Review Board (IRB) of the Galician Health Service on April 3, 2020 (#2020/194). Informed consent forms were waived by the IRB.
Databáze: OpenAIRE