ABSTRACT
Objectives: The pandemic of the coronavirus disease 2019 (COVID-19) continuously poses a serious threat to public health, highlighting an urgent need for simple and efficient early detection and prediction. Methods: We comprehensively investigated and reanalyzed the published indexes and models for predicting severe illness among COVID‑19 patients in our dataset, and validated them on an independent dataset. Results: 696 COVID-19 cases in the discovery stage and 337 patients in the validation stage were involved. The AuROC of neutrophil to lymphocyte ratio (NLR) (0.782) was significantly higher than that of the other 11 independent risk indexes in severe outcome prediction. The combination of NLR and oxygen saturation (SaO2) (NLR+SaO2) showed the biggest AuROC calculations with a value of 0.901; with a cut-off value of 0.532, it exhibited 84.2% sensitivity, 88.4% specificity and 86.8% correct classification ratio. Moreover, we first identified that principal component analysis (PCA) is an effective tool to predict the severity of COVID-19. We obtained 86.5% prediction accuracy with 86% sensitivity when PCA was applied to predict severe illness. In addition, to evaluate the performance of NLR+SaO2 and PCA, we compared them with currently published predictive models in the same dataset. Conclusions: It showed that NLR+SaO2 is an appropriate and promising method for predicting severe illness, followed by PCA. We then validated the results on an independent dataset and revealed that they remained robust accuracy in outcome prediction. This study is significant for early treatment, intervention, triage and saving limited resources.
Subject(s)
COVID-19ABSTRACT
Objectives: The pandemic of the coronavirus disease 2019 (COVID-19) continuously poses a serious threat to public health, highlighting an urgent need for simple and efficient early detection and prediction. Methods: We comprehensively investigated and reanalyzed the published indexes and models for predicting severe illness among COVID‑19 patients in our dataset, and validated them on an independent dataset. Results: 696 COVID-19 cases in the discovery stage and 337 patients in the validation stage were involved. The AuROC of neutrophil to lymphocyte ratio (NLR) (0.782) was significantly higher than that of the other 11 independent risk indexes in severe outcome prediction. The combination of NLR and oxygen saturation (SaO2) (NLR+SaO2) showed the biggest AuROC calculations with a value of 0.901; with a cut-off value of 0.532, it exhibited 84.2% sensitivity, 88.4% specificity and 86.8% correct classification ratio. Moreover, we first identified that principal component analysis (PCA) is an effective tool to predict the severity of COVID-19. We obtained 86.5% prediction accuracy with 86% sensitivity when PCA was applied to predict severe illness. In addition, to evaluate the performance of NLR+SaO2 and PCA, we compared them with currently published predictive models in the same dataset. Conclusions: It showed that NLR+SaO2 is an appropriate and promising method for predicting severe illness, followed by PCA. We then validated the results on an independent dataset and revealed that they remained robust accuracy in outcome prediction. This study is significant for early treatment, intervention, triage and saving limited resources.
Subject(s)
COVID-19ABSTRACT
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.
Subject(s)
Critical Illness , Diabetes Mellitus , Obesity , Hypertension , COVID-19ABSTRACT
Background: Recent epidemiological evidence has demonstrated a higher rate of COVID-19 hospitalizations and deaths among minorities. This pattern of race-ethnic disparities emerging throughout the United States raises the question of what social factors may influence spread of a highly transmissible novel coronavirus. The purpose of this study is to describe race-ethnic and socioeconomic disparities associated with COVID-19 in patients in our community in Orange County, California and understand the role of individual-level factors, neighborhood-level factors, and access to care on outcomes. Methods: This is a case-series of COVID-19 patients from the University of California, Irvine (UCI) across six-weeks between 3/12/2020 and 4/22/2020. Note, California's shelter-in-place order began on 3/19/2020. Individual-level factors included race-ethnicity status were recorded. Neighborhood-level factors from census tracts included median household income, mean household size, proportion without a college degree, proportion working from home, and proportion without health insurance were also recorded. Results: A total of 210-patients tested were COVID-19 positive, of which 73.3% (154/210) resided in Orange County. Hispanic/Latinx patients residing in census tracts below the median income demonstrated exponential growth (rate = 55.9%, R2 = 0.9742) during the study period. In addition, there was a significant difference for both race-ethnic (p < 0.001) and income bracket (p = 0.001) distributions prior to and after California's shelter-in-place. In addition, the percentage of individuals residing in neighborhoods with denser households (p = 0.046), lower levels of college graduation (p < 0.001), health insurance coverage (p = 0.01), and ability to work from home (p < 0.001) significantly increased over the same timeframe. Conclusions and Relevance: Our study examines the race-ethnic disparities in Orange County, CA, and highlights vulnerable populations that are at increased risk for contracting COVID-19. Our descriptive case series illustrates that we also need to consider socioeconomic factors, which ultimately set the stage for biological and social disparities.