About: Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease

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About: Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease
Creator	Wu, Jie Park, Jung Khan, Saahir Glavis-Bloom, Justin Amin, Alpesh Weinberg, Brent Boden-Albala, Bernadette Bota, Daniela Chang, Peter Chow, Daniel Monuki, Edwin Mutasa, Simukayi Soun, Jennifer Thompson, Leslie Xie, Xiaohui Loveless, Theresa
Source	MedRxiv
abstract	Background: The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. Methods: This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. Results: Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21-88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27-88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87-1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. Conclusions and Relevance: We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.
has issue date	2020-05-11(xsd:dateTime)
bibo:doi	10.1101/2020.05.06.20093435
has license	medrxiv
sha1sum (hex)	8514ae0446b87aab183c59c22452bb9269039ddb
schema:url	https://doi.org/10.1101/2020.05.06.20093435
resource representing a document's title	Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease
resource representing a document's body	covid:8514ae0446b87aab183c59c22452bb9269039ddb#body_text
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