The atherogenic index of plasma as a predictor of mortality in patients with COVID-19

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a global health threat, and thus, an early and effective set of predictors is needed to manage the course of the disease. OBJECTIVES: We aim to determine the effect of SARS-CoV-2 on lipid profile and to evaluate whether the atherogenic index of plasma (AIP) could be used to predict in-hospital mortality in COVID-19 patients. METHODS: In this retrospective chart review study, a total of 139 confirmed COVID-19 patients, whose diagnoses are confirmed by PCR and computerized tomography results, are enrolled. The study population is divided into two groups: the deceased patient group and the survivor group. For each patient, fasting total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and the triglyceride values are obtained from the laboratory tests required at the admission to hospital. Finally, the AIP is calculated as the base 10 logarithm of the triglyceride to HDL-C ratio. Distributional normality of the data is checked and depending on the normality of the data, either T test or Mann Whithey U test is employed to compare the two aforementioned study groups. RESULTS: Mean age of the study population is 49.2 ± 20.8 and 61.2% (n = 85) is male. Out of the 139 patients 26 have deceased and the remaining 113 patients survived the disease. Mean age of the deceased patients was 71.8*8.9 and mean age of the survivor patients is 44.0*19.2 (p < 0.001). The deceased group had more patients with hypertension (50.0% vs. 23.0, p = 0.006), diabetes mellitus (35.6% vs. 10.6%, p = 0.002), cardiovascular diseases (23.1% vs. 4.4%, p = 0.001), chronic renal insufficiency (11.5% vs. 0.9%, p = 0.003) and atrial fibrillation (7.7% vs 0%, p = 0.003). The AIP values in the deceased group are found to be statistically higher (p < 0.001) than the survivor group. As a measure of mortality, the area under the operating characteristic curve for the AIP is calculated as 0.850 (95% confidence interval: 0.772–0.928) along with the optimal cut-off value of 0.6285 (78.6% sensitivity and 80.5% specificity). Furthermore, the AIP value is observed to be elevated in patients with pneumonia, intubation history, and intensive care admission during hospital stay (p = 0.002, p < 0.001 and p < 0.001, respectively). Finally, compared to the survivor group, total cholesterol, HDL-C, LDL-C values are lower (p = 0.004, p < 0.001 and p < 0.001, respectively) and triglyceride levels are higher (p < 0.001) in deceased patients. CONCLUSION: In this study, we show that the AIP levels higher than 0.6285 can predict in-hospital mortality for COVID-19 patients. Moreover, the AIP emerges as a good candidate to be used as an early biomarker to predict pneumonia, intubation and intensive care need. Hence, regular check of the AIP levels in COVID-19 patients can improve management of these patients and prevent deterioration of the disease.