RT Journal Article
T1 Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model
A1 Martinez-Lacalzada, Miguel
A1 Viteri-Noel, Adrian
A1 Manzano, Luis
A1 Fabregate, Martin
A1 Rubio-Rivas, Manuel
A1 Garcia, Sara Luis
A1 Arnalich-Fernandez, Francisco
A1 Beato-Perez, Jose Luis
A1 Vargas-Nunez, Juan Antonio
A1 Calvo-Manuel, Elpidio
A1 Espino-Alvarez, Alexia Constanza
A1 Freire-Castro, Santiago J.
A1 Loureiro-Amigo, Jose
A1 Fontan, Paula Maria Pesqueira
A1 Pina, Adela
A1 Suarez, Ana Maria Alvarez
A1 Silva-Asiain, Andrea
A1 Garcia-Lopez, Beatriz
A1 del Pino, Jairo Luque
A1 Sanz-Canovas, Jaime
A1 Chazarra-Perez, Paloma
A1 Garcia-Garcia, Gema Maria
A1 Nunez-Cortes, Jesus Millan
A1 Casas-Rojo, Jose Manuel
A1 Gómez-Huelgas, Ricardo
A1 SEMI-COVID-19 Network, 
K1 COVID-19
K1 Critical illness
K1 Easily obtained clinical variables
K1 Initial assessment
K1 Prognostic models
K1 Pneumonia
K1 Features
K1 Children
AB Objectives: We 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.Methods: We 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.Results: There 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 SpO(2)
PB Elsevier sci ltd
SN 1198-743X
YR 2021
FD 2021-11-29
LK https://hdl.handle.net/10668/26764
UL https://hdl.handle.net/10668/26764
LA en
DS RISalud
RD Apr 17, 2025