TY - JOUR
T1 - Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection
T2 - a multicentre cohort study (PREDI-CO study)
AU - PREDICO Study Group
AU - Bartoletti, Michele
AU - Giannella, Maddalena
AU - Scudeller, Luigia
AU - Tedeschi, Sara
AU - Rinaldi, Matteo
AU - Bussini, Linda
AU - Fornaro, Giacomo
AU - Pascale, Renato
AU - Pancaldi, Livia
AU - Pasquini, Zeno
AU - Trapani, Filippo
AU - Badia, Lorenzo
AU - Campoli, Caterina
AU - Tadolini, Marina
AU - Attard, Luciano
AU - Puoti, Massimo
AU - Merli, Marco
AU - Mussini, Cristina
AU - Menozzi, Marianna
AU - Meschiari, Marianna
AU - Codeluppi, Mauro
AU - Barchiesi, Francesco
AU - Cristini, Francesco
AU - Saracino, Annalisa
AU - Licci, Alberto
AU - Rapuano, Silvia
AU - Tonetti, Tommaso
AU - Gaibani, Paolo
AU - Ranieri, Vito M.
AU - Viale, Pierluigi
AU - Raumer, Luigi
AU - Guerra, Luca
AU - Tumietto, Fabio
AU - Cascavilla, Alessandra
AU - Zamparini, Eleonora
AU - Verucchi, Gabriella
AU - Coladonato, Simona
AU - Rubin, Arianna
AU - Ianniruberto, Stefano
AU - Francalanci, Eugenia
AU - Volpato, Francesca
AU - Virgili, Giulio
AU - Rossi, Nicolò
AU - Del Turco, Elena Rosselli
AU - Guardigni, Viola
AU - Fasulo, Giovanni
AU - Dentale, Nicola
AU - Fulgaro, Ciro
AU - Legnani, Giorgio
AU - Esposito, Luca
N1 - Funding Information:
We would like to acknowledge Prof. Russell Lewis (Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna) for his advice on methodology. No funding was available for this study.
Publisher Copyright:
© 2020 European Society of Clinical Microbiology and Infectious Diseases
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - Age
KW - C-reactive proteine
KW - Coronavirus disease 2019
KW - Lactate dehydrogenase
KW - Obesity
KW - Prognostic tool
KW - Severe acute respiratory syndrome coronavirus 2
KW - Severe respiratory failure
UR - http://www.scopus.com/inward/record.url?scp=85090479528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090479528&partnerID=8YFLogxK
U2 - 10.1016/j.cmi.2020.08.003
DO - 10.1016/j.cmi.2020.08.003
M3 - Article
C2 - 32781244
AN - SCOPUS:85090479528
VL - 26
SP - 1545
EP - 1553
JO - Clinical Microbiology and Infection
JF - Clinical Microbiology and Infection
SN - 1198-743X
IS - 11
ER -