Predicting critical illness on initial diagnosis of COVID-19 based on easil - Evidencio
Predicting critical illness on initial diagnosis of COVID-19 based on easily-obtained clinical variables: development and validation of the PRIORITY model
Objectives
We aimed to develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of 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 centers in Spain (23 March to 21 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, 7,850 in the development cohort (primary outcome 25.1%, 1,967/7,850) and 2,583 in the validation cohort (outcome 27.0%, 698/2,583). 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).

Conclusions
The PRIORITY model, based on easily-obtained clinical information, had good discrimination and generalizability for identifying COVID-19 patients at risk of critical outcomes.
Autori della ricerca: Miguel Martinez-Lacalzada, Adrián Viteri-Noël, Luis Manzano, Martin Fabregate, Manuel Rubio-Rivas, Sara Luis Garcia , Francisco Arnalich Fernández, José Luis Beato Pérez, Juan Antonio Vargas Núñez, Elpidio Calvo Manuel, Alexia-Constanza Espiño, Santiago J. Freire Castro, Jose Loureiro-Amigo, Paula Maria Pesqueira Fontan, 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
Versione: 1.42
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Risk for COVID-19 Critical Illness

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