Flecainide acetate is a class IC antiarrhythmic agent and its clinical efficacy has been confirmed by the results of several clinical trials. Nowadays, flecainide is recommended as one of the first line therapies for pharmacological conversion as well as maintenance of sinus rhythm in patients with atrial fibrillation and/or supraventricular tachycardias. Based on the Cardiac Arrhythmia Suppression Trial study results, flecainide is not recommended in patients with structural heart disease due to high proarrhythmic risk. Recent data support the role of flecainide in preventing ventricular tachyarrhythmias in patients with catecholaminergic polymorphic ventricular tachycardia associated both with ryanodine receptor and calsequestrin mutations. We herein review the current clinical data related to flecainide use in clinical practice and some concerns about its role in the management of patients with coronary artery disease.

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Background: Inflammation plays a key role in the pathogenesis of acute coronary syndromes (ACS). In this context we assessed neutrophil count as a predictor of major in-hospital events in patients admitted for a non-ST-segment elevation (NSTE) ACS. Methods: We measured neutrophils on admission in 160 patients with a NSTE ACS and we correlated their count with the incidence of a combined in-hospital end point including: cardiac death, acute heart failure, ST-segment elevation myocardial infarction, and recurrent myocardial ischemia. Results: Patients who had a major in-hospital event also had a higher neutrophil count (P = 0.02) and higher serum levels of troponin I (P = 0.04). In the univariate logistic regression analysis, in-hospital major events could be predicted by troponin I >0.07 ng/mL (odds ratio [OR]: 5.65, 95% confidence interval [CI]: 1.26-25.32, P = 0.02), white blood cell count >8650 cells/µL (OR: 2.68, 95% CI: 1.03-6.95, P = 0.04), neutrophil count >6700 cells/µL (OR: 7.74, 95% CI: 2.79-21.47, P < 0.001), and C-reactive protein >0.97 mg/dL (OR: 3.56, 95% CI: 1.13-11.19, P = 0.02). However, in multivariate regression, neutrophil count >6700 cells/µL (OR: 6.52, 95% CI: 1.56-27.22, P = 0.01) was the only independent in-hospital prognostic factor. Conclusions: In patients with a NSTE ACS of moderate or high risk, neutrophil count on admission may identify those who are at risk of having an adverse in-hospital outcome.

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Beyond low-density lipoprotein (LDL)-cholesterol concentrations, in recent years, several clinical studies have shown that both oxidised and small, dense LDL have a strong predictive role for the presence of vascular atherosclerosis. These two lipid parameters seem to have a synergistic impact on cardiovascular risk, with a greater importance in patients at higher-risk, such as those with type-2 diabetes. Increased levels of oxidised and small, dense LDL levels are a feature of diabetic dyslipidaemia, and small, dense LDL have been shown to be a good predictor of future cardiovascular events, at both univariate and multivariate analyses. On the other hand, although the association of oxidised LDL with surrogate markers of atherosclerosis is consistent, the correlation with hard clinical end points seems to be smaller. Yet, measurement of these two lipid parameters has not been widely used in daily practice because of the limited availability of clinical data and methodological problems: lack of availability of easy, cheap and reproducible essays for measurement of oxidised and, particularly, small, dense LDL has reduced their assessment in large clinical end-points trials. However, on the basis of available data, the therapeutic modulation of small, dense LDL is significantly associated with reduced cardiovascular risk, even after adjustment for confounding factors. In conclusion, the routine measurement of oxidised and small, dense LDL in patients with type-2 diabetes cannot be recommended in daily clinical practice so far; yet, their measurement is strongly encouraged to better understand their role on the cardiovascular risk of patients with type-2 diabetes.

Sudden unexpected death is frequent in street heroin addicts. We conducted a histologic study of the sinus node (SN) to offer some evidence about the possible arrhythmogenic cause of death. Postmortem coronary angiography and microscopic examination of the SN and the perinodal area were performed in 50 heroin addicts (group 1) and in 50 nonaddicts (group 2), all men (16-40 years old). In heroin addicts, fatty and/or fibrous tissue replaced SN tissue in 21 cases (42%). Perinodal infiltration was found in 15 cases (30%). Fibromuscular dysplasia in branches of the sinus node artery (SNA) was found in eight cases (16%). Inflammation with focal and/or diffuse concentration of round cells was detected in the SN in 22 cases (44%). Old mural thrombi were also found in 13 cases (26%). The histologic changes in the SN and perinodal area offer an explanation about the possible mechanism of arrhythmia and sudden death in this population.

Rosiglitazone may increase cardiovascular risk in patients with type 2 diabetes. Yet, its effects on atherogenic dyslipidemia are still not fully elucidated. In a prospective open-label study rosiglitazone (4 mg/day for 12 weeks) was added to a maximum of 2 oral antidiabetic drugs in 18 diabetic patients. We evaluated the effects on plasma lipids before and after an oral fat load. The size and subclasses of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) were also determined (by gradient gel electrophoresis). Rosiglitazone improved glycosylated hemoglobin ([HbA1c] P = .0023), without significant effects on fasting and postprandial plasma lipids. Fasting LDL size increased (+1.4%, P = .034), with less small, dense LDL-IIIA (-25.1%, P = .018). Postprandially, larger HDL-2b reduced (-8.7%, P = .006) and smaller HDL-3b increased (+12.2%, P = .05), without any effects on HDL size. Rosiglitazone led to antiatherogenic changes in LDL size and subclasses, with proatherogenic changes in HDL subclasses, despite no effects on plasma lipids. Their clinical relevance remains to be established.

A mid-septal lead location may be identified using a simple stepwise algorithm, based on the presence of positive QRS in lead V6, positive QRS in any of the inferior leads, and a QR pattern in lead aVL.