Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease
| Autor: | Bernadette Boden-Albala, Xiaohui Xie, Jie Wu, Justin Glavis-Bloom, Edwin S. Monuki, Daniel S. Chow, Jung In Park, Alpesh Amin, Leslie M. Thompson, Simukayi Mutasa, Daniela A. Bota, Peter D. Chang, Brent D. Weinberg, Jennifer E. Soun, Saahir Khan, Theresa B Loveless |
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| Přispěvatelé: | Ashkenazi, Itamar |
| Rok vydání: | 2020 |
| Předmět: |
Male
Viral Diseases Physiology Disease Logistic regression Biochemistry Medical Conditions Mathematical and Statistical Techniques Endocrinology 0302 clinical medicine Risk Factors Models Medicine and Health Sciences 80 and over Medicine 030212 general & internal medicine Virus Testing Aged 80 and over screening and diagnosis Multidisciplinary Statistics Middle Aged C-Reactive Proteins Prognosis Body Fluids Hospitalization Detection Infectious Diseases Blood Physiological Parameters Physical Sciences Female Patient Safety Anatomy Research Article 4.2 Evaluation of markers and technologies Adult medicine.medical_specialty Critical Care Endocrine Disorders General Science & Technology Science Concordance Vital signs and over Research and Analysis Methods Models Biological Risk Assessment 03 medical and health sciences Diagnostic Medicine Clinical Research Internal medicine Diabetes Mellitus Humans Obesity Statistical Methods Retrospective Studies Aged Ferritin Past medical history SARS-CoV-2 business.industry Body Weight Biology and Life Sciences Proteins Protein Complexes COVID-19 Covid 19 030208 emergency & critical care medicine Retrospective cohort study Biological Confidence interval 4.1 Discovery and preclinical testing of markers and technologies Blood Counts Good Health and Well Being Metabolic Disorders business Body mass index Mathematics Forecasting |
| Zdroj: | PLoS ONE, Vol 15, Iss 12, p e0242953 (2020) PloS one, vol 15, iss 12 PLoS ONE |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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