• Andrzej Jaroszyński, Anna Jaroszyńska, Janusz Siebert, Wojciech Dąbrowski, Jarosław Niedziałek, Anna Bednarek-Skublewska, Tomasz Zapolski, Andrzej Wysokiński, Wojciech Załuska, Andrzej Książek, and Todd T Schlegel.
• Department of Family Medicine, Medical University of Lublin, Staszica 11, 20-081, Lublin, Poland. jaroszynskiaj@interia.pl.
• Clin. Exp. Nephrol. 2015 Dec 1; 19 (6): 1157-64.

BackgroundGiven that cardiac disease is the leading cause of mortality in hemodialysis (HD) patients, identification of patients at risk for cardiac mortality is crucial. The aim of this study was to determine if positive T-wave amplitude in lead aVR (TaVR) was predictive of cardiovascular (CV) mortality and sudden cardiac death (SCD) in a group of HD patients.Methods And ResultsAfter exclusion, 223 HD patients were prospectively followed-up for 25.43 ± 3.56 months. Patients were divided into TaVR negative (n = 186) and TaVR positive (n = 37) groups. Myocardial infarction, diabetes and beta-blocker therapy were more frequent in positive TaVR patients. Patients with upright TaVR were older, had higher left ventricular mass index, lower ejection fraction, higher calcium × phosphate product, higher troponin T level, higher prevalence of ST-T abnormalities, and increased width of QRS complex and QT interval, compared with patients with negative TaVR. A Kaplan-Meier analysis showed that the cumulative incidences of CV mortality as well as SCD were higher in patients with positive TaVR compared with those with negative TaVR (log-rank, p < 0.001 in both cases). A multivariate analysis selected age [hazard ratio (HR) 1.71, p < 0.001], heart rate (HR 1.42, p = 0.016), and positive TaVR (HR 2.21, p = 0.001) as well as age (HR 1.88, p < 0.001), and positive TaVR (HR 1.53, p = 0.014) as independent predictors of CV mortality and SCD, respectively.ConclusionIn HD patients, positive TaVR is an independent and powerful predictor of CV mortality as well as SCD. This simple ECG parameter provides additional information beyond what is available with other known traditional risk factors and allows the identification of patients most at risk of CV events.

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