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Additional file 1: Supplementary Table 1: Association of post-dialysis recovery time (RT) with ECG measurements QT interval, QTc interval, and heart rate variability, by sex (male vs. female).

Additional file 7: Supplementary Table 7: Association of ECG measurements with recovery time adjusted for baseline length of hemodialysis treatment, relative fluid removal, use of QT prolonging medications*, and serum potassium levels.

Additional file 4: Supplementary Table 4: Association of ECG measurements with post-dialysis recovery time (RT) recorded within six months (��180 days).

Additional file 7: Supplementary Table 7: Association of ECG measurements with recovery time adjusted for baseline length of hemodialysis treatment, relative fluid removal, use of QT prolonging medications*, and serum potassium levels.

Additional file 5: Supplementary Table 5: Association of post-dialysis recovery time (RT) with QTc using Fridericia���s formula.

Additional file 4: Supplementary Table 4: Association of ECG measurements with post-dialysis recovery time (RT) recorded within six months (��180 days).

Additional file 3: Supplementary Table 3: Association of ECG measurements with post-dialysis recovery time dichotomized at the median (median = 20).

Additional file 3: Supplementary Table 3: Association of ECG measurements with post-dialysis recovery time dichotomized at the median (median = 20).

Additional file 6: Supplementary Table 6: Association of ECG measurements without outlier.

Additional file 2: Supplementary Table 2: Association of post-dialysis recovery time (RT) with ECG measurements QT interval, QTc interval, and heart rate variability, by age (���55 years old vs. > 55 years old).

Additional file 2: Supplementary Table 2: Association of post-dialysis recovery time (RT) with ECG measurements QT interval, QTc interval, and heart rate variability, by age (���55 years old vs. > 55 years old).

Additional file 5: Supplementary Table 5: Association of post-dialysis recovery time (RT) with QTc using Fridericia���s formula.

Additional file 6: Supplementary Table 6: Association of ECG measurements without outlier.

published manuscript. Waks JW, Hamilton C, Das S, Ehdaie A, Minnier J, Narayan S, Niebauer M, Raitt M, Tompkins C, Varma N, Chugh S, Tereshchenko LG. Improving sudden cardiac death risk stratification by evaluating electrocardiographic measures of global electrical heterogeneity and clinical outcomes among patients with implantable cardioverter-defibrillators: rationale and design for a retrospective, multicenter, cohort study. J Interv Card Electrophysiol. 2018 Jun;52(1):77-89. doi: 10.1007/s10840-018-0342-2. Epub 2018 Mar 14. PubMed PMID: 29541969.

Several reports have demonstrated that prolongation of the QT interval is associated with sudden cardiac death (SCD). However, it is unknown whether any of the components within the QT interval are responsible for its association with SCD. We examined the association of the individual QT-interval components (R-wave onset to R-peak, R-peak to R-wave end, ST-segment, T-wave onset to T-peak, and T-peak to T-wave end) with SCD in 12 241 participants (54±5.7 years; 26% black; 55% women) from the ARIC study (Atherosclerosis Risk in Communities). The QT interval and its components were measured at baseline (1987-1989) from 12-lead ECGs. SCD cases were adjudicated by a group of physicians through December 31, 2012. During a median follow-up of 23.6 years, a total of 346 cases of SCD were identified. Although prolongation of the QT interval was associated with a 49% increased risk of SCD (hazard ratio, 1.49; 95% confidence interval, 1.01-2.18), only the T-wave onset to T-peak component (per 1-SD increase: hazard ratio, 1.19; 95% confidence interval, 1.06-1.34) was associated with SCD and not any of the other components in separate models. When all of the QT-interval components were included in the same model, T-wave onset to T-peak remained the strongest predictor of SCD (per 1-SD increase: hazard ratio, 1.21; 95% confidence interval, 1.06-1.37). The risk of SCD with the QT interval is driven by prolongation of the T-wave onset to T-peak component. This suggests that shifting the focus from the overall QT interval to its individual components will refine SCD prediction in the community.

We examined whether there is a relationship between repolarization abnormalities on electrocardiography (EKG) and deformation abnormalities by echocardiography. Analysis of baseline EKGs and mechanical (echo-based deformation) changes was performed in 128 patients with a clinical diagnosis of hypertrophic cardiomyopathy (HCM). Patients with left ventricular hypertrophy (LVH) or repolarization abnormalities had higher septal thickness when compared to patients with normal EKG. Patients with EKG evidence of LVH or QTc prolongation had lower systolic velocity, systolic strain, systolic strain rate, late diastolic velocity, and late diastolic strain rate than patients with a normal EKG. Patients with strain pattern or ST depression/T-wave inversion had lower systolic velocity, systolic strain, systolic strain rate, early diastolic velocity, and late diastolic velocity when compared to patients with normal EKGs. LVH and repolarization abnormalities on surface EKG are markers of impaired systolic and diastolic mechanics in HCM.

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction.

Author(s): Drew, Barbara J; Harris, Patricia; Zegre-Hemsey, Jessica K; Mammone, Tina; Schindler, Daniel; Salas-Boni, Rebeca; Bai, Yong; Tinoco, Adelita; Ding, Quan; Hu, Xiao | Abstract: PURPOSE:Physiologic monitors are plagued with alarms that create a cacophony of sounds and visual alerts causing "alarm fatigue" which creates an unsafe patient environment because a life-threatening event may be missed in this milieu of sensory overload. Using a state-of-the-art technology acquisition infrastructure, all monitor data including 7 ECG leads, all pressure, SpO(2), and respiration waveforms as well as user settings and alarms were stored on 461 adults treated in intensive care units. Using a well-defined alarm annotation protocol, nurse scientists with 95% inter-rater reliability annotated 12,671 arrhythmia alarms. RESULTS:A total of 2,558,760 unique alarms occurred in the 31-day study period: arrhythmia, 1,154,201; parameter, 612,927; technical, 791,632. There were 381,560 audible alarms for an audible alarm burden of 187/bed/day. 88.8% of the 12,671 annotated arrhythmia alarms were false positives. Conditions causing excessive alarms included inappropriate alarm settings, persistent atrial fibrillation, and non-actionable events such as PVC's and brief spikes in ST segments. Low amplitude QRS complexes in some, but not all available ECG leads caused undercounting and false arrhythmia alarms. Wide QRS complexes due to bundle branch block or ventricular pacemaker rhythm caused false alarms. 93% of the 168 true ventricular tachycardia alarms were not sustained long enough to warrant treatment. DISCUSSION:The excessive number of physiologic monitor alarms is a complex interplay of inappropriate user settings, patient conditions, and algorithm deficiencies. Device solutions should focus on use of all available ECG leads to identify non-artifact leads and leads with adequate QRS amplitude. Devices should provide prompts to aide in more appropriate tailoring of alarm settings to individual patients. Atrial fibrillation alarms should be limited to new onset and termination of the arrhythmia and delays for ST-segment and other parameter alarms should be configurable. Because computer devices are more reliable than humans, an opportunity exists to improve physiologic monitoring and reduce alarm fatigue.