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

Link to article at PubMed

Clin Microbiol Infect. 2020 Aug 8:S1198-743X(20)30479-1. doi: 10.1016/j.cmi.2020.08.003. Online ahead of print.

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 February 22 to April 3 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 (RR)>30bpm, 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 analyzed 1113 patients (644 derivation, 469 validation cohort). Mean (±standard deviation)age was 65.7(±15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in derivation and validation cohort, 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)], RR ≥22bpm [OR 3.75 (95%CI 2.01-7.01)], lymphocytes ≤900/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 ≥10mg/dl [OR 5.91 (95%CI 4.88-7.17)], and lactate dehydrogenase ≥350IU/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 receiver-operator curve (AUROC) was 0.89 (0.86-0.92). At score of >3, sensitivity, specificity, 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: AUROC 0.85 (0.81-0.88). At score of >3, sensitivity, specificity, 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 COVID-19 pandemic.

PMID:32781244 | PMC:PMC7414420 | DOI:10.1016/j.cmi.2020.08.003

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