Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use

Massimo Attanasio, Marco Enea, Matteo Tacelli, Calogero Camma', Ciro Celsa, Vincenzo Giuseppe Genova, Salvatore Battaglia, Laura Paris, Valentina Zuccaro, Antonio Gasbarrini, Giulia Cammà, Matteo Tacelli, Ciro Celsa, Bianca Magro, Antonio Gasbarrini, Francesco Antonio Mancarella, Luca Novelli, Luca Ferdinando Lorini, Luca Ferdinando Lorini, Federico RaimondiAntonio Gasbarrini, Fabiano Di Marco, Laura Paris, Mauro Gori, Lorenzo Zileri, Michele Senni, Stefano Fagiuoli, Raffaele Bruno, Mauro Gori, Bianca Magro

Risultato della ricerca: Articlepeer review

10 Citazioni (Scopus)

Abstract

BackgroundsValidated tools for predicting individual in-hospital mortality of COVID-19 are lacking. Weaimed to develop and to validate a simple clinical prediction rule for early identification of inhospital mortality of patients with COVID-19.Methods and findingsWe 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-hospitalmortality. Fine and Gray competing risks multivariate model (with discharge as a competingevent) 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% ConfidenceInterval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptomsbefore hospital admission <10 days (HR 1.72, 95%CI 1.39–2.12), diabetes (HR 1.21, 95% CI 1.02–1.45), coronary heart disease (HR 1.40 95% CI 1.09–1.80), chronic liver disease(HR 1.78, 95%CI 1.16–2.72), and lactate dehydrogenase levels at admission (HR 1.0003,95%CI 1.0002–1.0005). The AUC was 0.822 (95%CI 0.722–0.922) in the derivation cohortand 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The predictionrule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp).ConclusionsA validated simple clinical prediction rule can promptly and accurately assess the risk for inhospital mortality, improving triage and the management of patients with COVID-19
Lingua originaleEnglish
Numero di pagine12
RivistaPLoS One
Volume16
Stato di pubblicazionePublished - 2021

All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.1300.1300???
  • ???subjectarea.asjc.1100.1100???
  • ???subjectarea.asjc.1000???

Fingerprint

Entra nei temi di ricerca di 'Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use'. Insieme formano una fingerprint unica.

Cita questo