The prognostic value of the TyG index in non-diabetic COVID 19 patients with myocardial injury

Background and Aim: Insulin resistance (IR) is strongly associated with endothelial dysfunction. There is also evidence that endothelial dysfunction is associated with COVID 19 infection. Triglyceride glucose (TyG) index is newly defined promising surrogate index for IR as a cardiometabolic risk marker. It has been found to be associated with coronary artery calcification and high risk of cardiovascular disease. No data are currently available taking into account the effects of the TyG index on mortality in non-diabetic COVID 19 patients with myocardial injury. We aimed to investigate whether TyG predicts the in hospital mortality in non-diabetic COVID 19 patients with myocardial injury Methods: This was a retrospective study. 218 non-diabetic patients who have myocardial injury due to COVID 19 infection were included in the study. Blood samples including high-sensitivity (hs) cardiac troponin (cTn), triglycerides, eGFR, haemoglobin, platelet, D-Dimer, CRP, albumine, uric acid, ferritin and plasma fasting glucose (PFG) concentrations, were collected from the antecubital vein from each patient after at least 8 h of fasting. The TyG index was calculated as follows: log [serum triglycerides (mg/dL) x plasma glucose (mg/dL)/2]. We defined myocardial injury as cTn concentrations >99th percentile upper reference limit. The study cohort was divided into 2 groups as those survivors and non-survivors. Triglyceride and fasting blood glucose were evaluated in a separate multivariate analysis model (model 1). The receiver operating characteristics (ROC) curve analysis was used to evaluate the sensitivity and specificity of the TyG and it's cut-off value in determining the in-hospital mortality. Survival evaluations for the low-and high TyG groups were determined by using Kaplan-Meier and long-rank test Results: 169 patients were survivors and 49 patients were non-survivors. D-Dimer and CRP levels were more higher in non-survivors group (p<0.01 and p<0.01 respectively). Non survivor patients had also higher TyG index than the others (p<0.01) (Table 1). Age, CHF, uric asid, TG, TyG were found to be independently associated with in-hospital mortality in univariate anaylsis. We used AUC value for diagnostic accuracies and discriminatory performances of the TyG (AUC:0.786, CI 95% 0.721-0.852, p<0.001), TG (AUC:0.738, CI 95% 0.656-0.820, p<0.001), and PFG (AUC:0.660, CI 95% 0.573-0.748, p=0.001) for detecting the in-hospital mortality. The receiver operating characteristics curve analysis revealed a cut off value of TyG index greater than 4.97 predicts the development of in hospital mortality in non-diabetic COVID 19 patients with myocardial injury with a 82% sensitivity, and a 66% specificity. Conclusion(s): A TyG above 4.97 was found as a risk factor for in hospital mortality in non-diabetic COVID 19 patients with myocardial injury. TyG may be a part of cardiovascular mortality to identify individuals at high risk for nondiabetic COVID 19 patients with myocardial injury.

The Predictive Ability of the C-reactive Protein to Albumin Ratio As A Mortality Predictor in Hospitalized Severe SARS-CoV-2 Infected Patients with Cardiovascular Diseases

Aim: Although there are few studies on the predictive value of C-reactive protein-to-albumin ratio (CAR) in coronavirus disease-2019 (COVID-19) patients, to the best of our knowledge, there are no studies specifically conducted in COVID-19 patients with cardiovascular disease (CVD). This study assessed the use of baseline CAR levels to predict death in hospitalized COVID-19 patients with CVD. Methods: This study was designed as a single-center cross-sectional study. Patients diagnosed with COVID-19 who were admitted to the University of Health Sciences Turkey, Bagcilar Training and Research Hospital between April 16 and May 20, 2020 were analyzed retrospectively. The patients were divided into 2 groups: those who died and those who survived, considering the follow-up period. The CAR values of the study population, as well as patients with CVD, were calculated, and the association of CAR with in-hospital mortality was evaluated. Results: The in-hospital mortality rate was 11.1% (49/442 pts) in all populations. Deceased patients had significantly more frequent CVD (p<0.001) and the mortality rate was 34.4% (30/96 pts) in those patients. Median CAR values were higher in nonsurvivors than among survivors (p<0.001). Multivariate analysis demonstrated that CAR was an independent predictor of mortality in patients with CVD [hazard ratio 1.013 (95% confidence interval: 1.002-1.022), p=0.018]. Conclusion: CAR is an inflammatory risk marker that independently predicts mortality in all COVID-19 hospitalized patients and patients with CVD.

Mortality prediction with CHA2DS2-VASc, CHA2DS2-VASc-HS and R2CHA2DS2-VASc score in patients hospitalized due to COVID-19.

OBJECTIVE: We aimed to test the efficiency of CHA2DS2-VASc, CHA2DS2-VASc-HS, R2CHA2DS2-VASc score systems on the prediction of mortality in the patients with COVID-19. PATIENTS AND METHODS: The data were collected from 508 hospitalized patients with COVID-19. Comorbidity features including coronary artery disease, peripheral arterial disease, congestive heart failure, hypertension, atrial fibrillation, diabetes mellitus, hyperlipidemia, smoking, chronic obstructive pulmonary disease, cerebrovascular event, cancer status, and renal disease were recorded. The patients were divided as surviving group (n=440) and non-survivors (n=68). RESULTS: The in-hospital mortality rate of the patients with COVID-19 was 13.4%. Factors found to be associated with mortality in univariate analysis were CHA2DS2-VASc, CHA2DS2-VASc-HS, R2CHA2DS2-VASc, cancer state, atrial fibrillation, hemoglobin, lymphocyte count, CRP, albumin and ferritin. Model 1 multivariate cox regression analysis revealed CHA2DS2-VASc, hemoglobin, CRP and ferritin levels to be independently associated with mortality. Factors that were found to be independently associated with in-hospital mortality in Model 2 analysis were CHA2DS2-VASc-HS, R2CHA2DS2-VASc, hemoglobin, CRP and ferritin whereas except hemoglobin in Model 3 analysis, the other variables had been the same. Predictive power of R2CHA2DS2-VASc was better than of both CHA2DS2-VASc (p=0.002) and CHA2DS2-VASc-HS (p=0.034) in determining the in-hospital mortality. Patients with higher R2CHA2DS2-VASc (> 3 points), CHA2DS2-VASc-HS (> 3 points) and CHA2DS2-VASc (> 2 points) scores exhibited the highest mortality rate in survival analysis by using Kaplan-Meier and long-rank tests. CONCLUSIONS: CHA2DS2-VASc, CHA2DS2-VASc-HS, and R2CHA2DS2-VASc were found to be independent predictors of mortality in hospitalized COVID-19 patients. The current study revealed that the predictive ability of R2CHA2DS2-VASc was better than the both of CHA2DS2-VASc and CHA2DS2-VASc-HS score.

Mortality Predictors for severe coronavirus disease 2019 patients who suffered miyocardial injury: A single center experience

Background and Aim: To investigate the clinical features of the severe coronavirus disease 2019 (COVID-19) patients with myocardial injury and predictors of adverse outcome in this population. Methods: The 81 consecutive severe COVID-19 patients who suffered myocardial injury were admitted to this study, from 15 March to 30 April 2020. The demographic characteristics (age and gender), clinical data (comorbidities, laboratory findings, treatments, complications, and outcomes), laboratory findings and results of cardiac examinations (cardiac biomarkers) for participants during hospitalization were were collected by using electronic medical records. The study outcome was rate of in-hospital death, and the national death notification system and hospital records were used to obtain information on mortality. Results: A total of 81 severe COVID-19 cases (mean age of 63±14 years) with myocardial injury (26%) constituted the study population. During the follow up period (median 13 days), in-hospital mortality was observed in 27 (33.3%) patients. Compared to the survivors, those who died were older (60±13 vs 70±14;p<0.01, respectively). In terms of the most common main comorbidities, hypertension (33.3% vs 59.3%;p=0.03), coronary artery disease (9.3% vs, 44.4%;p<0.01), heart failure (7.4% vs 37%;p<0.01, respectively), diabetes (20.4% vs 44.4%;p=0.02), and chronic renal failure (9.3% vs, 29.6%;p=0.02) were frequently observed in nonsurvivors than the others. Acute heart failure were also more frequent in nonsurvivors (p=0.03) and two of the nonsurvivors had malignant ventricular arrhythmia. Detailed demographic, clinical and laboratory characteristics of the population are summarized in Table 1 and Table 2. Multivariate cox regression analysis by using Backward LR method revealed that to be older age (HR: 1.049;p=0.04), coronary artery disease history (HR: 3.098;p=0.02), decreased eGFR value (HR: 0.984;p=0.04), increased admission CRP level (HR: 1.006;p<0.01) and higher peak Dimer level (HR: 1.567;p=0.04) were independent predictors for in-hospital mortality. The area under the ROC curves of hs-TnI, CK-MB, peak D-Dimer, and admission CRP for the in-hospital mortality were 0.81, 0.80, 0.72, and 0.79, respectively (all p<0.01). The single cut-off concentrations of hs-TnI, CK-MB, peak D-Dimer, and admission CRP were >309 pg/mL, > 15 ng/mL, >2.5 mg/L, and >60 mg/L, respectively. On Kaplan-Meier analysis, levels of the peak cTnI, peak CK-MB, baseline CRP and peak D-Dimer above aforementioned cut-offs were significantly associated with higher in-hospital mortality (Figure 2). Conclusions: The risk of in-hospital mortality was significantly associated with older age, inflammatory response, and cardiovascular comorbidities and can be predicted by the biomarkers for the myocardial injury in severe COVID-19 patients.