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). (November 2020)

• Record Type: Journal Article
• Authors: Bartoletti, Michele; Giannella, Maddalena; Scudeller, Luigia; Tedeschi, Sara; Rinaldi, Matteo; Bussini, Linda; Fornaro, Giacomo; Pascale, Renato; Pancaldi, Livia; Pasquini, Zeno; Trapani, Filippo; Badia, Lorenzo; Campoli, Caterina; Tadolini, Marina; Attard, Luciano; Puoti, Massimo; Merli, Marco; Mussini, Cristina; Menozzi, Marianna; Meschiari, Marianna; Codeluppi, Mauro; Barchiesi, Francesco; Cristini, Francesco; Saracino, Annalisa; Licci, Alberto; Rapuano, Silvia; Tonetti, Tommaso; Gaibani, Paolo; Ranieri, Vito M.; Viale, Pierluigi; Raumer, Luigi; Guerra, Luca; Tumietto, Fabio; Cascavilla, Alessandra; Zamparini, Eleonora; Verucchi, Gabriella; Coladonato, Simona; Rubin, Arianna; Ianniruberto, Stefano; Francalanci, Eugenia; Volpato, Francesca; Virgili, Giulio; Rossi, Nicolò; Del Turco, Elena Rosselli; Guardigni, Viola; Fasulo, Giovanni; Dentale, Nicola; Fulgaro, Ciro; Legnani, Giorgio; Campaci, Emanuele; Basso, Cristina; Zuppiroli, Alberto; Passino, Amalia Sanna; Tesini, Giulia; Angelelli, Lucia; Badeanu, Adriana; Rossi, Agostino; Santangelo, Giulia; Dauti, Flovia; Koprivika, Vidak; Roncagli, Nicholas; Tzimas, Ioannis; Liuzzi, Guido Maria; Baxhaku, Irid; Pasinelli, Letizia; Neri, Mattia; Zanaboni, Tommaso; Dell'Omo, Francesco; Vatamanu, Oana; Gori, Alice; Zavatta, Idina; Antonini, Stefano; Pironi, Chiara; Piccini, Elena; Esposito, Luca; Zuccotti, Alessandro; Urbinati, Giacomo; Pratelli, Agnese; Sarti, Alberto; Semprini, Michela; Evangelisti, Enrico; D'Onofrio, Mara; Sasdelli, Giuseppe; Pizzilli, Giacinto; Pierucci, Elisabetta; Rossini, Giada; Vocale, Caterina; Marconi, Lorenzo; Leoni, Maria Cristina; Fronti, Elisa; Guaraldi, Giovanni; Bavaro, Davide; Laghetti, Paola;
• Is Part Of: Clinical microbiology and infection. Volume 26:Number 11(2020)
• Page Start: 1545
• Page End: 1553
• Publication Date: 2020-11
• Subjects: Age -- Coronavirus disease 2019 -- C-reactive proteine -- Lactate dehydrogenase -- Obesity -- Prognostic tool -- Severe acute respiratory syndrome coronavirus 2 -- Severe respiratory failure; Medical microbiology -- Periodicals; Diagnostic microbiology -- Periodicals; Communicable diseases -- Periodicals; Infection -- Periodicals; 616.01
• DOI: 10.1016/j.cmi.2020.08.003
• Languages: English
• ISSNs: 1198-743X

Abstract

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: Spo 2 <93% with 100% Fio 2, 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/mm 3 (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.