Association Between Major Adverse Cardiovascular Events and Left Ventricular Mass Index in Patients Who Have Undergone Coronary Computed Tomography Angiography: From the FU-CCTA Registry.

Background: Left ventricular mass (LVM) is a predictor of future cardiovascular risk. We determined the association between LVM measured by coronary computed tomography angiography (CCTA) and the prognosis in patients who have undergone CCTA for screening of coronary artery disease (CAD). Methods: We performed a prospective cohort study. Five hundred twenty consecutive patients who underwent CCTA at Fukuoka University Hospital (FU-CCTA registry) were enrolled. They were clinically suspected of having CAD or had at least one cardiovascular risk factor, and were a follow-up of up to 5 years. Equal to more than 50% of coronary stenosis as assessed by CCTA was diagnosed as CAD. Using CCTA, LVM index (LVMI), LV ejection fraction (LVEF), LV end-diastolic volume (LVEDV) and LV end-systolic volume were measured. The primary endpoint was major adverse cardiovascular events (MACEs: including all causes of death, ischemic stroke, acute myocardial infarction and coronary revascularization). The patients were divided into non-MACEs and MACEs groups. Results: The non-MACEs and MACEs groups consisted of 478 and 42 patients, respectively. Percent of CAD in the MACEs group was significantly higher than that in the non-MACEs group. The MACEs group showed significantly higher LVMI and tended to have a lower LVEF and LVEDV than the non-MACEs group. Although LVMI was not associated with MACEs in all patients, LVMI was independently associated with MACEs in males (odd ratio: 1.018, 95% confidence interval: 1.002 - 1.035, P = 0.030), but not females. Conclusions: Evaluation of LVMI by CCTA may be useful for predicting MACEs in males.

Simplified electrophysiological approach combining a point-of-care nerve conduction device and an electrocardiogram produces an accurate diagnosis of diabetic polyneuropathy.

AIMS/INTRODUCTION: This study aimed to investigate the diagnostic potential of two simplified tests, a point-of-care nerve conduction device (DPNCheck™) and a coefficient of variation of R-R intervals (CVR-R), as an alternative to traditional nerve conduction studies for the diagnosis of diabetic polyneuropathy (DPN) in patients with diabetes. MATERIALS AND METHODS: Inpatients with type 1 or type 2 diabetes (n = 167) were enrolled. The study population consisted of 101 men, with a mean age of 60.8 ± 14.8 years. DPN severity was assessed using traditional nerve conduction studies, and differentiated based on Baba's classification (BC). To examine the explanatory potential of variables in DPNCheck™ and CVR-R regarding the severity of DPN according to BC, a multiple regression analysis was carried out, followed by a receiver operating characteristic analysis. RESULTS: Based on BC, 61 participants (36.5% of the total) were categorized as having DPN severity of stage 2 or more. The multiple regression analysis yielded a predictive formula with high predictive power for DPN diagnosis (estimated severity of DPN in BC = 2.258 - 0.026 × nerve conduction velocity [m/s] - 0.594 × ln[sensory nerve action potential amplitude (µV)] + 0.528In[age(years)] - 0.178 × ln[CVR-R], r = 0.657). The area under the curve in receiver operating characteristic analysis was 0.880. Using the optimal cutoff value for DPN with severer than stage 2, the predictive formula showed good diagnostic efficacy: sensitivity of 83.6%, specificity of 79.2%, positive predictive value of 51.7% and negative predictive value of 76.1%. CONCLUSIONS: These findings suggest that DPN diagnosis using DPNCheck™ and CVR-R could improve diagnostic efficiency and accessibility for DPN assessment in patients with diabetes.