BackgroundHyper‐ 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 ResultsTwo 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).ConclusionsThe 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.

Introduction 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. Purpose Identification of electrocardiographic predictors of torsadogenic risk through the application of a novel T wave analysis tool Methods 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. Results 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/sec, p= 0.002, r=0.45). The addition of T wave right slope to QTc improved prediction accuracy from 79 to 88%. Conclusion 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.

Objective To determine if ECG repolarization measures can be used to detect small changes in serum potassium levels in hemodialysis patients. Patients and Methods 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) (Figure 1). Results The correlations with potassium were statistically significant for T right slope (P < 0.0001), 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 0.2 mmol/L were detectable. Conclusion 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.

Objective We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring. Patients and methods Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2 min intervals during hemodialysis. Individualized potassium estimation models were generated for each patient. ECG-calculated potassium values were compared to blood potassium results at subsequent visits to evaluate the accuracy of the potassium estimation models. Results The mean absolute error between the estimated potassium and blood potassium 0.38 ± 0.32 mEq/L (9% of average potassium level) decreasing to 0.6 mEq/L using predictors of poor signal. Conclusions A single-lead ECG acquired using electrodes attached to a smartphone device can be processed to calculate the serum potassium with an error of 9% in patients undergoing hemodialysis. Summary A single-lead ECG acquired using electrodes attached to a smartphone can be processed to calculate the serum potassium in patients undergoing hemodialysis remotely.

C ongenital long QT syndrome (LQTS) affects ≈1 in 2000 people and is an important cause of sudden cardiac death in the young. 1 There are 17 known LQTS-susceptibility genes (LQT1-17), but the most common types LQT1, LQT2, and LQT3 account for ≈75% to 80% of all congenital LQTS and over 95% of genetically proven cases.2 The current diagnostic approach is based on measurement of the heart rate-corrected QT interval (QTc) and clinical factors, such as the medical and family history and clinical presentation. However, unless the QTc is repeatedly ≥500 ms without alternative acquired factors present, the QTc from the resting 12-lead ECG is insufficient for diagnosis.3 Genetic testing plays an important role in the diagnosis, risk stratification, tailoring of genotypedirected therapies and for mutation-specific confirmatory testing of appropriate relatives once the index case's diseasecausing mutation has been identified. 4,5 However, despite its excellent diagnostic yield, some genetic test results are difficult to interpret, 6 and access to genetic testing in general can be hampered by insurance reimbursement considerations. Accordingly, there is ongoing need for more refined diagnosis and risk stratification based on readily available, noninvasive means.The 12-lead surface ECG is an inexpensive test that is performed widely and could be used as an efficient diagnostic © 2016 American Heart Association, Inc. Original ArticleBackground-Congenital long QT syndrome (LQTS) is characterized by QT prolongation. However, the QT interval itself is insufficient for diagnosis, unless the corrected QT interval is repeatedly ≥500 ms without an acquired explanation. Further, the majority of LQTS patients have a corrected QT interval below this threshold, and a significant minority has normal resting corrected QT interval values. Here, we aimed to develop and validate a novel, quantitative T wave morphological analysis program to differentiate LQTS patients from healthy controls. Methods and Results-We analyzed a genotyped cohort of 420 patients (22±16 years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a novel, proprietary T wave analysis program that quantitates subtle changes in T wave morphology. The top 3 discriminating features in each ECG lead were determined and the lead with the best discrimination selected. Classification was performed using a linear discriminant classifier and validated on an untouched cohort. The top 3 features were Tpeak-Tend interval, T wave left slope, and T wave center of gravity x axis (last 25% of the T wave). Lead V6 had the best discrimination. It could distinguish 86.8% of LQTS patients from healthy controls. Moreover, it distinguished 83.33% of patients with concealed LQTS from controls, despite having essentially identical resting corrected QT interval values. Conclusions-T wave quantitative analysis on the 12-lead surface ECG provides an effective, novel tool to distinguish patients with either LQT1/LQT2 from healthy matched controls. It can provide guid...