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COVID-19 Mortality Risk Assessment: An International Multi-Center Study
|Authors:||Bertsimas, Dimitris; Lukin, Galit; Mingardi, Luca; Nohadani, Omid; Orfanoudaki, Agni; Stellato, Bartolomeo; Wiberg, Holly; González-García, Sara ; Parra-Calderón, Carlos Luis; The Hellenic COVID-19 Study Group; Robinson, Kenneth; Schneider, Michelle; Stein, Barry; Estirado, Alberto; Beccara, Lia a; Canino, Rosario; Bello, Martina Dal; Pezzetti, Federica; Pan, Angelo|
|Abstract:||Background: Timely identification of COVID-19 patients at high risk of mortality can
significantly improve patient management and resource allocation within hospitals. This
study seeks to develop and validate a data-driven personalized mortality risk calculator for
hospitalized COVID-19 patients.|
Methods: De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts.
Findings: The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-ofsample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84- 0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use.
Interpretation: The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.
|Publisher version (URL):||https://doi.org/10.1101/2020.07.07.20148304|
|Appears in Collections:||(IBIS) Artículos|
(VICYT) Colección Especial COVID-19
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