tetano
Editor, Senior Moderator
Am J Med Sci
. 2021 May 21;S0002-9629(21)00112-9.
doi: 10.1016/j.amjms.2021.04.001. Online ahead of print.
Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model
Sang H Woo[SUP] 1 [/SUP], Arturo J Rios-Diaz[SUP] 2 [/SUP], Alan A Kubey[SUP] 3 [/SUP], Dianna R Cheney-Peters[SUP] 4 [/SUP], Lily L Ackermann[SUP] 4 [/SUP], Divya M Chalikonda[SUP] 4 [/SUP], Chantel M Venkataraman[SUP] 5 [/SUP], Joshua M Riley[SUP] 5 [/SUP], Michael Baram[SUP] 6 [/SUP]
Affiliations
Abstract
Background: Coronavirus 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 question: Can 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?
Methods: This 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%).
Results: The 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.
Interpretation: This 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.
Keywords: COVID-19; SARS-CoV-2; model; risk.
. 2021 May 21;S0002-9629(21)00112-9.
doi: 10.1016/j.amjms.2021.04.001. Online ahead of print.
Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model
Sang H Woo[SUP] 1 [/SUP], Arturo J Rios-Diaz[SUP] 2 [/SUP], Alan A Kubey[SUP] 3 [/SUP], Dianna R Cheney-Peters[SUP] 4 [/SUP], Lily L Ackermann[SUP] 4 [/SUP], Divya M Chalikonda[SUP] 4 [/SUP], Chantel M Venkataraman[SUP] 5 [/SUP], Joshua M Riley[SUP] 5 [/SUP], Michael Baram[SUP] 6 [/SUP]
Affiliations
- PMID: 34029558
- DOI: 10.1016/j.amjms.2021.04.001
Abstract
Background: Coronavirus 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 question: Can 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?
Methods: This 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%).
Results: The 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.
Interpretation: This 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.
Keywords: COVID-19; SARS-CoV-2; model; risk.