Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model
Miguel Martínez-Lacalzada, Adrián Viteri-Noël, Luís Manzano, M. Fabregate, Manuel Rubio‐Rivas, Sara L. Garcia, Francisco Arnalich, José Luis Beato-Pérez, J.A. Vargas-Núñez, Elpidio Calvo-Manuel, A. Espiño-Álvarez, Santiago Freire-Castro, José Loureiro-Amigo, Paula María Pesqueira Fontán, Adela Pina, Ana María Álvarez Suárez, Andrea Silva-Asiain, Beatriz García López, Jairo Luque del Pino, Jaime Sanz‐Cánovas, Paloma Chazarra-Pérez, Gema-María García-García, Jesús Millán Núñez-Cortés, José Manuel Casas‐Rojo, Ricardo Gómez‐Huelgas, Luis F. Abrego-Vaca, Ana Andreu-Arnanz, Octavio A. Arce-García, Marta Bajo-González, Pablo Borque-Sanz, Alberto Cózar-Llistó, Beatriz Del Hoyo-Cuenda, Alejandra Gamboa-Osorio, Isabel Sánchez, Óscar A. López-Cisneros, Borja Merino-Ortiz, Elisa Riera-González, Jimena Rey-García, Cristina Sánchez-Díaz, Grisell Starita-Fajardo, Cecilia Suárez Carantoña, Svetlana Zhilina Zhilina
Abstract
ObjectivesWe aimed to develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of coronavirus disease 2019 (COVID-19), to identify patients at risk of critical outcomes.MethodsWe used data from the SEMI-COVID-19 Registry, a cohort of consecutive patients hospitalized for COVID-19 from 132 centres in Spain (23rd March to 21st May 2020). For the development cohort, tertiary referral hospitals were selected, while the validation cohort included smaller hospitals. The primary outcome was a composite of in-hospital death, mechanical ventilation, or admission to intensive care unit. Clinical signs and symptoms, demographics, and medical history ascertained at presentation were screened using least absolute shrinkage and selection operator, and logistic regression was used to construct the predictive model.ResultsThere were 10 433 patients, 7850 in the development cohort (primary outcome 25.1%, 1967/7850) and 2583 in the validation cohort (outcome 27.0%, 698/2583). The PRIORITY model included: age, dependency, cardiovascular disease, chronic kidney disease, dyspnoea, tachypnoea, confusion, systolic blood pressure, and SpO2 ≤93% or oxygen requirement. The model showed high discrimination for critical illness in both the development (C-statistic 0.823; 95% confidence interval (CI) 0.813, 0.834) and validation (C-statistic 0.794; 95%CI 0.775, 0.813) cohorts. A freely available web-based calculator was developed based on this model (https://www.evidencio.com/models/show/2344).ConclusionsThe PRIORITY model, based on easily obtained clinical information, had good discrimination and generalizability for identifying COVID-19 patients at risk of critical outcomes.