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Initiation of class III anti-arrhythmic medications requires telemetric monitoring for ventricular arrhythmias and QT prolongation to reduce the risk of torsades de pointes (TdP). Heart rate-corrected QT interval (QTc) is an indicator of risk, however it is imperfect, and subtle abnormalities of repolarization have been linked with arrhythmogenesis.Identification of electrocardiographic predictors of torsadogenic risk through the application of a novel T wave analysis tool.Among all patients admitted to Mayo Clinic for initiation of dofetilide or sotalol, we identified 13 cases who developed drug-induced TdP and 26 age and sex matched controls that did not develop TdP. The immediate pre-TdP ECG of those with TdP was compared to the last ECG performed prior to hospital discharge in controls using a novel T wave program that quantified subtle changes in T wave morphology.The QTc and 12 T wave parameters successfully distinguished TdP cases from controls. The top performing parameters were the QTc in lead V3 (mean case vs control 480 vs 420 msec, p 0.001, r = 0.72) and T wave right slope in lead I (mean case vs control -840.29 vs -1668.71 mV/s, p = 0.002, r = 0.45). The addition of T wave right slope to QTc improved prediction accuracy from 79 to 88 %.Our data demonstrate that, in addition to QTc, the T wave right slope is correlated strongly with TdP risk. This suggests that a computer-based repolarization measurement tool that integrates additional data beyond the QTc may identify patients with the greatest torsadogenic potential.

To determine if ECG repolarization measures can be used to detect small changes in serum potassium levels in hemodialysis patients.Signal-averaged ECGs were obtained from standard ECG leads in 12 patients before, during, and after dialysis. Based on physiological considerations, five repolarization-related ECG measures were chosen and automatically extracted for analysis: the slope of the T wave downstroke (T right slope), the amplitude of the T wave (T amplitude), the center of gravity (COG) of the T wave (T COG), the ratio of the amplitude of the T wave to amplitude of the R wave (T/R amplitude), and the center of gravity of the last 25% of the area under the T wave curve (T4 COG) (Fig. 1).The correlations with potassium were statistically significant for T right slope (P0.0001), T COG (P=0.007), T amplitude (P=0.0006) and T/R amplitude (P=0.03), but not T4 COG (P=0.13). Potassium changes as small as 0.2mmol/L were detectable.Small changes in blood potassium concentrations, within the normal range, resulted in quantifiable changes in the processed, signal-averaged ECG. This indicates that non-invasive, ECG-based potassium measurement is feasible and suggests that continuous or remote monitoring systems could be developed to detect early potassium deviations among high-risk patients, such as those with cardiovascular and renal diseases. The results of this feasibility study will need to be further confirmed in a larger cohort of patients.

Background Functional magnetic resonance imaging (fMRI) is a powerful method for identifying in vivo network activation evoked by deep brain stimulation (DBS). Objective Identify the global neural circuitry effect of subthalamic nucleus (STN) DBS in nonhuman primates (NHP). Method An in-house developed MR image-guided stereotactic targeting system delivered a mini-DBS stimulating electrode, and blood oxygenation level-dependent (BOLD) activation during STN DBS in healthy NHP was measured by combining fMRI with a normalized functional activation map and general linear modeling. Results STN DBS significantly increased BOLD activation in the sensorimotor cortex, supplementary motor area, caudate nucleus, pedunculopontine nucleus, cingulate, insular cortex, and cerebellum (FDR Conclusion Our results demonstrate that STN DBS evokes neural network grouping within the motor network and the basal ganglia. Taken together, these data highlight the importance and specificity of neural circuitry activation patterns and functional connectivity.

Background Hyper‐ and hypokalemia are clinically silent, common in patients with renal or cardiac disease, and are life threatening. A noninvasive, unobtrusive, blood‐free method for tracking potassium would be an important clinical advance. Methods and Results Two groups of hemodialysis patients (development group, n=26; validation group, n=19) underwent high‐resolution digital ECG recordings and had 2 to 3 blood tests during dialysis. Using advanced signal processing, we developed a personalized regression model for each patient to noninvasively calculate potassium values during the second and third dialysis sessions using only the processed single‐channel ECG . In addition, by analyzing the entire development group's first‐visit data, we created a global model for all patients that was validated against subsequent sessions in the development group and in a separate validation group. This global model sought to predict potassium, based on the T wave characteristics, with no blood tests required. For the personalized model, we successfully calculated potassium values with an absolute error of 0.36±0.34 mmol/L (or 10% of the measured blood potassium). For the global model, potassium prediction was also accurate, with an absolute error of 0.44±0.47 mmol/L for the training group (or 11% of the measured blood potassium) and 0.5±0.42 for the validation set (or 12% of the measured blood potassium). Conclusions The signal‐processed ECG derived from a single lead can be used to calculate potassium values with clinically meaningful resolution using a strategy that requires no blood tests. This enables a cost‐effective, noninvasive, unobtrusive strategy for potassium assessment that can be used during remote monitoring.