Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study)

Abstract

Objectives: We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19).

Methods: We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: Spo₂ <93% with 100% Fio₂, respiratory rate >30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, β-coefficients were used to develop a risk score. Trial Registration NCT04316949.

Results: We analysed 1113 patients (644 derivation, 469 validation cohort). Mean (±SD) age was 65.7 (±15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in the derivation and validation cohorts, respectively. At multivariate analysis, risk factors for SRF in the derivation cohort assessed at hospitalization were age ≥70 years (OR 2.74; 95% CI 1.66-4.50), obesity (OR 4.62; 95% CI 2.78-7.70), body temperature ≥38°C (OR 1.73; 95% CI 1.30-2.29), respiratory rate ≥22 breaths/min (OR 3.75; 95% CI 2.01-7.01), lymphocytes ≤900 cells/mm³ (OR 2.69; 95% CI 1.60-4.51), creatinine ≥1 mg/dL (OR 2.38; 95% CI 1.59-3.56), C-reactive protein ≥10 mg/dL (OR 5.91; 95% CI 4.88-7.17) and lactate dehydrogenase ≥350 IU/L (OR 2.39; 95% CI 1.11-5.11). Assigning points to each variable, an individual risk score (PREDI-CO score) was obtained. Area under the receiver-operator curve was 0.89 (0.86-0.92). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 71.6% (65%-79%), 89.1% (86%-92%), 74% (67%-80%) and 89% (85%-91%), respectively. PREDI-CO score showed similar prognostic ability in the validation cohort: area under the receiver-operator curve 0.85 (0.81-0.88). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 80% (73%-85%), 76% (70%-81%), 69% (60%-74%) and 85% (80%-89%), respectively.

Conclusion: PREDI-CO score can be useful to allocate resources and prioritize treatments during the COVID-19 pandemic.

Keywords: Age; C-reactive proteine; Coronavirus disease 2019; Lactate dehydrogenase; Obesity; Prognostic tool; Severe acute respiratory syndrome coronavirus 2; Severe respiratory failure.

Fig. 1

Study flow-chart: derivation cohort (a) and validation cohort (b).

Fig. 2

Discrimination (a) and calibration (b) of the multivariable model and discrimination (c) of the PREDI-CO score in the derivation cohort. Discrimination (d), calibration (e) and discrimination (f) of the PREDICO score in the validation cohort.

Fig. 3

Comparison of prediction ability for severe respiratory failure in hospitalized individuals with a diagnosis of COVID-19 of the PREDICO score with qSOFA, SOFA, CURB-65 and MEWS scores. (a) Derivation cohort; (b) validation cohort.