Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model

Abstract

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 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.

Keywords: COVID-19; Critical illness; Easily obtained clinical variables; Initial assessment; Prognostic models.

Fig. 1

Discriminatory ability of the PRIORITY model in (a) the development and (b) the validation cohorts. Discriminative ability was assessed using the C-statistic, as the area under the receiver operating characteristic curve, with 95% confidence intervals (CIs) computed with 1000 bootstrap replicates.

Fig. 2

Decision curve analysis within the validation cohort. Clinical usefulness of the PRIORITY model compared to risk stratification based on oxygen saturation (binary: SpO₂ ≤93% or oxygen requirement) and/or age (quadratic term). The x-axis represents the whole range of decision threshold probabilities for critical illness (p_t) and the y-axis represents the net benefit (NB). NB calculated as: True positives/N – (false positives/N)∗(p_t/(1–p_t)), with N total sample size.