Predictive value of CAR for In-Hospital Mortality in Patients with COVID-19 Pneumonia: A Retrospective Cohort Study.
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ABSTRACT: Background
In the current literature, there is a growing evidence that supports the significant role of inflammation in the progression of viral pneumonia, including patients with coronavirus disease 2019 (COVID-19).Aim
The present study aimed to investigate the predictive value of C-reactive protein/albumin ratio (CAR) for in-hospital mortality in patients with COVID-19.Material and methods
This retrospective study included the data of 275 consecutive COVID-19 patients who were hospitalized in a referral pandemic center. The CAR ratio was obtained by dividing the CRP level with albumin level. The study population was divided into tertiles (T1, T2, and T3) according to their admission CAR values. The endpoint of the study was a composite outcome of in-hospital mortality.Results
During the in-hospital course, 33 (12%) patients died. The patients classified into T3 group had significantly higher CAR compared those classified into T2 and T1 groups. After the adjustment for the confounders, T3 group had 8.2 (95% CI: 4.2-48.1) times higher rates of in-hospital mortality compared to T1 group (the reference group) in a logistic regression model using CAR values.Conclusion
To the best of our knowledge, this is the first study to demonstrate the predictive value of CAR for in-hospital mortality in COVID-19 patients.
SUBMITTER: Guney BC
PROVIDER: S-EPMC7874980 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVES:Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19. DESIGN:The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework. SETTING:3 secondary and tertiary level centres in Greater Manchester, the UK. PARTICIPANTS:392 hospitalised patients with a diagnosis of COVID-19. RESULTS:392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome. CONCLUSIONS:This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.
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