Abstract
BackgroundsValidated 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 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-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 <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 cohort and 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The prediction rule 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 in-hospital mortality, improving triage and the management of patients with COVID-19.
Highlights
Severe acute respiratory syndrome coronavirus 12 (SARS-CoV-2) was first identified in China in December 2019 and has since spread rapidly all over the world [1]
We aimed to develop and to validate a simple clinical prediction rule for early identification of inhospital mortality of patients with COVID-19
C-reactive protein (CRP) was increased in 83% of patients, and lactate dehydrogenase (LDH) resulted elevated in 88% of patients
Summary
Severe acute respiratory syndrome coronavirus 12 (SARS-CoV-2) was first identified in China in December 2019 and has since spread rapidly all over the world [1]. In the context of triaging patients in emergency departments or in special clinics set up during an acute outbreak, the lack of clinical criteria to identify the most severe cases and to define the evolution of the disease has made the management of the pandemic even more difficult. A risk stratification of COVID-19 patients is crucial in order to improve the healthcare organization and to best manage a new potential second wave of the epidemic in the coming winter. In this complex epidemiological and clinical scenario, a competing risks model is a robust statistical method to predict patients risk profile when more than one competing event, such as in-hospital mortality and discharge, is present [3]. The aims of this retrospective multicenter study are 1) to derive a simple clinical prediction rule capable of promptly identifying risk factors for in-hospital mortality and discharge in hospitalized patients with COVID-19 by competing risks analysis; 2) to validate this prediction rule in an external validation cohort; 3) to design a free web-app for calculating the risk of in-hospital mortality (COVID-CALC)
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