Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model
| Autor: | Michael Baram, Arturo J. Rios-Diaz, Dianna R. Cheney-Peters, Divya M. Chalikonda, Joshua M. Riley, Alan A. Kubey, Sang H. Woo, Chantel M. Venkataraman, Lily Ackermann |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Critical Care medicine.medical_treatment 030204 cardiovascular system & hematology Logistic regression Risk Assessment law.invention 03 medical and health sciences 0302 clinical medicine Interquartile range law Covariate Humans Medicine 030212 general & internal medicine Clinical Investigation Aged Retrospective Studies risk Philadelphia Mechanical ventilation Models Statistical model SARS-CoV-2 business.industry Area under the curve COVID-19 Retrospective cohort study General Medicine Middle Aged Respiration Artificial Intensive care unit Emergency medicine Female business Predictive modelling |
| Zdroj: | The American Journal of the Medical Sciences |
| DOI: | 10.1101/2020.07.16.20155739 |
| Popis: | BackgroundCoronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments.Research QuestionCan we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death?MethodsThis retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%).ResultsThe median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website.InterpretationThis web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation. |
| Databáze: | OpenAIRE |
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