1. Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use
- Author
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Francesco Antonio Mancarella, Massimo Attanasio, Mauro Gori, Valentina Zuccaro, Antonio Gasbarrini, Matteo Tacelli, Luca Ferdinando Lorini, Laura Paris, Calogero Cammà, Federico Raimondi, Bianca Magro, Raffaele Bruno, Lorenzo Zileri, Salvatore Battaglia, Vincenzo Giuseppe Genova, Fabiano Di Marco, Ciro Celsa, Giulia Cammà, Stefano Fagiuoli, Michele Senni, Luca Novelli, Marco Enea, Magro, B, Zuccaro, V, Novelli, L, Zileri, L, Celsa, C, Raimondi, F, Gori, M, Cammà, G, Battaglia, S, Genova, V, Paris, L, Tacelli, M, Mancarella, F, Enea, M, Attanasio, M, Senni, M, Di Marco, F, Lorini, L, Fagiuoli, S, Bruno, R, Cammà, C, Gasbarrini, A, Magro, Bianca, Zuccaro, Valentina, Novelli, Luca, Zileri, Lorenzo, Celsa, Ciro, Raimondi, Federico, Gori, Mauro, Cammà, Giulia, Battaglia, Salvatore, Genova, Vincenzo Giuseppe, Paris, Laura, Tacelli, Matteo, Mancarella, Francesco Antonio, Enea, Marco, Attanasio, Massimo, Senni, Michele, Di Marco, Fabiano, Lorini, Luca Ferdinando, Fagiuoli, Stefano, Bruno, Raffaele, Cammà, Calogero, and Gasbarrini, Antonio
- Subjects
Male ,Viral Diseases ,Epidemiology ,Clinical prediction rule ,Cardiovascular Medicine ,Vascular Medicine ,Steroid Therapy ,Chronic Liver Disease ,Cohort Studies ,Medical Conditions ,Endocrinology ,Retrospective Studie ,Risk Factors ,Medicine and Health Sciences ,80 and over ,Coronary Heart Disease ,Hospital Mortality ,Aged, 80 and over ,Multidisciplinary ,Pharmaceutics ,Liver Diseases ,Hazard ratio ,Middle Aged ,Mobile Applications ,Hospitals ,Hospitalization ,Infectious Diseases ,Brier score ,Italy ,Cardiovascular Diseases ,Medicine ,Female ,Human ,Research Article ,Cohort study ,Adult ,medicine.medical_specialty ,Endocrine Disorders ,Corticosteroid Therapy ,Science ,Settore MED/12 - GASTROENTEROLOGIA ,Mobile Application ,Cardiology ,Gastroenterology and Hepatology ,Risk Assessment ,Drug Therapy ,Internal medicine ,Diabetes Mellitus ,medicine ,Humans ,Aged ,Retrospective Studies ,Receiver operating characteristic ,business.industry ,SARS-CoV-2 ,Risk Factor ,Settore MED/09 - MEDICINA INTERNA ,COVID-19 ,Covid 19 ,Retrospective cohort study ,Cardiovascular Disease Risk ,Triage ,Confidence interval ,Health Care ,ROC Curve ,Health Care Facilities ,Medical Risk Factors ,Metabolic Disorders ,Cohort Studie ,business - Abstract
Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. Methods and findings We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptoms before hospital admission https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). Conclusions A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.
- Published
- 2021