Obstructive sleep apnoea (OSA) is a recognised risk factor for cardiovascular disease. However, it is difficult to evaluate the risk of cardiovascular disease in patients with OSA due to multiple shared risk factors. Composite lipid indices, such as atherogenic index of plasma (AIP), visceral adiposity index (VAI) and lipid accumulation product (LAP) have been shown to predict cardiovascular disease better than their individual lipid components. This study aimed to evaluate these indices in patients with OSA. Patients and Methods: Six hundred sixty-seven (667) patients with OSA and 139 non-OSA control volunteers participated in the study. Fasting serum triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C) levels were measured, and AIP, LAP and VAI were calculated following cardiorespiratory polygraphy. The relationship between lipid parameters, OSA and its comorbidities was evaluated using receiver operating curve (ROC) analysis. Results: We found a significant difference in all lipid parameters between OSA patients and controls. Comparing ROCs, LAP was significantly more strongly associated with OSA compared to all the other parameters. The optimal cut-off value for LAP to detect OSA was 76.4, with a sensitivity of 63% and a specificity of 76%. In addition, LAP was the best parameter to predict hypertension and diabetes in patients with OSA, and it was predictive for ischaemic heart disease together with HDL-C. Conclusion: Our results support the use of LAP in clinical practice when evaluating cardiovascular risk in patients with OSA. However, the optimal cut-off value should be determined in large-scale follow-up studies.

Background: Dyslipidaemia is well recognised in obstructive sleep apnoea (OSA) and could contribute to the development of cardiovascular disease (CVD). Atherogenic index of plasma (AIP) predicts cardiovascular morbidity and mortality better than the individual lipid levels. The aim of this study was to investigate the AIP in patients with OSA in relation with disease severity. Methods: Four hundred sixty-one patients with OSA and 99 controls participated in this study. AIP was assessed in the morning following a diagnostic sleep study. The association between lipid values and OSA were adjusted for age, gender, and body mass index. Results: Patients with OSA had higher AIP and triglyceride, and lower high-density lipoprotein cholesterol (HDL-C) levels (all p < 0.05). AIP significantly correlated with the Epworth Sleepiness Scale score (ρ = 0.19), the apnoea-hypopnoea index (ρ = 0.40) and oxygen desaturation index (ρ = 0.43, all p < 0.05). However, there was no relationship between the AIP and markers of sleep quality such as total sleep time, sleep period time, sleep efficiency, arousal index or percentage of REM sleep (all p > 0.05). AIP was not a better predictor for self-reported cardiovascular disease or diabetes than HDL-C. Conclusions: AIP is elevated in OSA and is related to disease severity. However, it does not seem to have an additional clinical value compared to HDL-C.

Obstructive sleep apnoea (OSA) is associated with increased insulin resistance. Triglyceride-glucose index (TyG) is a simple marker of insulin resistance; however, it has been investigated only by two studies in OSA. The aim of this study was to evaluate TyG in non-diabetic, non-obese patients with OSA. A total of 132 patients with OSA and 49 non-OSA control subjects were included. Following a diagnostic sleep test, fasting blood was taken for the analysis of the lipid profile and glucose concentrations. TyG was calculated as ln(triglyceride [mg/dL] × glucose [mg/dL]/2). Comparison analyses between OSA and control groups were adjusted for age, gender, body mass index (BMI) and smoking. TyG was higher in men (p < 0.01) and in ever-smokers (p = 0.02) and it was related to BMI (ρ = 0.33), cigarette pack-years (ρ = 0.17), apnoea–hypopnoea index (ρ = 0.38), oxygen desaturation index (ρ = 0.40), percentage of total sleep time spent with oxygen saturation below 90% (ρ = 0.34), and minimal oxygen saturation (ρ = −0.29; all p < 0.05). TyG values were significantly higher in OSA (p = 0.02) following adjustment for covariates. OSA is independently associated with higher TyG values which are related to disease severity in non-obese, non-diabetic subjects. However, the value of TyG in clinical practice should be evaluated in follow-up studies in patients with OSA.

Background and Objectives: The COVID-19 pandemic is an ongoing public health emergency. Patients with chronic diseases are at greater risk for complications and poor outcomes. The objective of this study was to investigate the liver function abnormalities and clinical outcomes in patients with COVID-19 and chronic hepatitis C. Materials and Methods: This retrospective, single-center study was conducted on a cohort of 126 patients with a history of hepatitis C, confirmed with COVID-19 between 01 April 2020 and 30 December 2020. Several clinical outcomes were compared between patients with active and non-active HCV infection, and the risks of liver impairment and all-cause mortality in active HCV patients were analyzed using a multivariate logistic regression model. Results: Among 1057 patients under follow-up for chronic HCV infection, 126 (11.9%) were confirmed with COVID-19; of these, 95 (75.4%) were under treatment or achieved SVR, while in the other 31 (24.6%), we found active HCV replication. There was a significantly higher proportion of severe COVID-19 cases in the active HCV group as compared to the non-active HCV group (32.2 vs. 7.3%, p < 0.001). Multivariate analysis showed that age, sex, alanine aminotransferase, C-reactive protein, procalcitonin, and HCV viral load were significant independent risk factors for liver impairment and all-cause mortality. The length of stay in hospital and intensive care unit for COVID-19 was significantly higher in patients with active HCV infection (p-value < 0.001), and a higher proportion of these patients required mechanical ventilation. Conclusions: Active HCV infection is an independent risk factor for all-cause mortality in COVID-19 patients.

In this paper, we aim at understanding the broad spectrum of factors influencing the survival of infected patients and the correlations between these factors to create a predictive probabilistic score for surviving the COVID-19 disease. Initially, 510 hospital admissions were counted in the study, out of which 310 patients did not survive. A prediction model was developed based on this data by using a Bayesian approach. Following the data collection process for the development study, the second cohort of patients totaling 541 was built to validate the risk matrix previously created. The final model has an area under the curve of 0.773 and predicts the mortality risk of SARS-CoV-2 infection based on nine disease groups while considering the gender and age of the patient as distinct risk groups. To ease medical workers’ assessment of patients, we created a visual risk matrix based on a probabilistic model, ranging from a score of 1 (very low mortality risk) to 5 (very high mortality risk). Each score comprises a correlation between existing comorbid conditions, the number of comorbid conditions, gender, and age group category. This clinical model can be generalized in a hospital context and can be used to identify patients at high risk for whom immediate intervention might be required.

Background: Obstructive sleep apnea (OSA) is usually associated with cardiovascular and cerebrovascular disease, metabolic syndrome and depression. Data on relevant OSA-associated comorbidities in Central–European populations are scarce. The aim of this study was to compare the prevalence of comorbidities in two OSA cohorts from Hungary and Romania. Methods: Data from 588 (282 from Hungary, 306 from Romania) untreated patients with OSA were retrospectively analyzed. The prevalence rates of hypertension, diabetes, dyslipidemia, allergic rhinitis, asthma, chronic obstructive pulmonary disease (COPD), osteoporosis, cerebrovascular and cardiovascular disease, arrhythmia and depression were compared between the two populations following adjustment for demographics, body mass index, smoking history, comorbidities and sleep parameters. Results: The prevalence rates of hypertension, arrhythmia, cerebrovascular and cardiovascular disease, diabetes and COPD in the whole study population were directly related to the severity of OSA. We found an inverse correlation between the prevalence of osteoporosis and OSA severity (all p < 0.05). Following adjustment, the prevalence of dyslipidemia was higher in the Hungarian cohort, whilst the prevalence of asthma, cardiovascular and cerebrovascular diseases was higher in the Romanian cohort (all p < 0.05). Conclusions: There was no difference in the prevalence rate of most comorbidities in patients with OSA from the two cohorts, except for dyslipidemia, asthma, cardiovascular and cerebrovascular disease.

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