The Impact of Statin Therapy in the Covid-19 Patients

Overview

In the retrospective case series, consecutive COVID-19 patients admitted from June 1 to December 31, 2020, were analyzed. A 1:1 matched cohort was created by propensity score-matched analysis. Demographic data, laboratory findings, comorbidities, treatments, and in-hospital outcomes were collected and compared between COVID-19 patients with and without statin use.

Methods Study population The study is retrospective single-center review of all patients admitted to Antalya Training and Research Center, tested positive for SARS-CoV-2 reverse transcriptase-polymerase chain reaction testing of nasopharyngeal or oropharyngeal specimens from January 1, 2020, to December 31, 2020., and informed consent was waived. The study conformed to the principles of the Declaration of Helsinki and was approved by the ethics committee of Antalya training and research center (2021-059). Baseline demographic, clinical, and laboratory variables were retrieved from the electronic medical record system. Patients were classified according to the HeartScore high-risk countries risk chart. Patients identified in the very high cardiovascular risk group were included in the analysis. The study population was divided into two groups: patients who received a statin vs. those who did not receive a statin before the hospitalization. The primary outcome was in-hospital mortality during the follow-up period. Statistical analysis Summary statistics were presented as percentages for categorical variables and medians with interquartile ranges or means with standard deviations for continuous variables. Differences in demographic, clinical characteristics and outpatient medications stratified by statin use were examined using the two-sided independent t-test and chi-squared test, as appropriate. To minimize the influence of confounding by indication, propensity-score matching was used to balance the clinical characteristics of the two groups. Matching was performed using a 1:1 matching protocol without replacement (greedy-matching algorithm), with a caliper width equal to 0.02 of the standard deviation of the propensity score's logit. The following variables were used for adjustment: age, sex, history of atrial fibrillation, cancer, chronic kidney disease, chronic obstructive pulmonary disease, congestive heart failure, coronary artery disease, diabetes mellitus, hypertension, smoking, and corticosteroid treatment. Descriptive analyses were performed for all baseline variables in the overall cohort and the propensity-matched cohort. To identify potential predictors of mortality, we initially performed a univariate logistic regression in the overall cohort. Covariables with p \< 0.20 were selected for entry into the multivariable model, and covariables with p \> 0.05 were removed from the final model. Similarly, both univariable and multivariable logistic regression was performed in the propensity-score matched cohort. All analyses were performed with the SAS software version 9.4 (SAS Institute, Inc., Cary, NC, USA).

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Outcomes