Background Global electrical heterogeneity (GEH) is associated with sudden cardiac death (SCD) in adults of 45 years and above. However, GEH has not been previously measured in young athletes. The goal of this study was to establish a reference for vectorcardiograpic (VCG) metrics in male and female athletes. Methods Skiers (n = 140; mean age 19.2 ± 3.5 years; 66% male, 94% white; 53% professional athletes) were enrolled in a prospective cohort. Resting 12‐lead ECGs were interpreted per the International ECG criteria. Associations of age, sex, and athletic performance with GEH were studied. Results In age and training level‐adjusted analyses, male sex was associated with a larger T vector [T peak magnitude +186 (95% CI 106–266) µV] and a wider spatial QRS‐T angle [+28.2 (17.3–39.2)°] as compared to women. Spatial QRS‐T angle in the ECG left ventricular hypertrophy (LVH) voltage group (n = 21; 15%) and normal ECG group did not differ (67.7 ± 25.0 vs. 66.8 ± 28.2; p = 0.914), suggesting that ECG LVH voltage in athletes reflects physiological remodeling. In contrast, skiers with right ventricular hypertrophy (RVH) voltage (n = 26, 18.6%) had wider QRS‐T angle (92.7 ± 29.6 vs. 66.8 ± 28.2°; p = 0.001), larger SAI QRST (194.9 ± 30.2 vs. 157.8 ± 42.6 mV × ms; p < 0.0001), but similar peak SVG vector magnitude (1976 ± 548 vs. 1939 ± 395 µV; p = 0.775) as compared to the normal ECG group. Better athletic performance was associated with the narrower QRS‐T angle. Each 10% worsening in an athlete's Federation Internationale de’ Ski downhill ranking percentile was associated with an increase in spatial QRS‐T angle by 2.1 (95% CI 0.3–3.9) degrees (p = 0.013). Conclusion Vectorcardiograpic adds nuances to ECG phenomena in athletes.

BackgroundThe risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD).MethodsAtherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured.ResultsOver a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63–1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37–2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526–0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303–0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526–0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515–0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668–0.753 for ECG recorded within 10 years before SCD).ConclusionShort-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

Summary A variety of genetic cardiovascular diseases may one day be curable using gene editing technology. Germline genome editing and correction promises to permanently remove monogenic cardiovascular disorders from the offspring and subsequent generations of affected families. Although technically feasible and likely to be ready for implementation in humans in the near future, this approach remains ethically controversial. Although currently beset by several technical challenges, and not yet past small animal models, somatic genome editing may also be useful for a variety of cardiovascular disorders. It potentially avoids ethical concerns about permanent editing of the germline and allows treatment of already diseased individuals. If technical challenges of Cas9-gRNA delivery (viral vector immune response, nonviral vector delivery) can be worked out, then CRISPR-Cas9 may have a significant place in the treatment of a wide variety of disorders in which partial or complete gene knockout is desired. However, CRISPR may not work for gene correction in the human heart because of low rates of homology directed repair. Off-target effects also remain a concern, although, thus far, small animal studies have been reassuring. Some of the therapies mentioned in this review may be ready for small clinical trials in the near future.

Background—The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). Methods—Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n=15,716; 55% female, 73% white, age 54.2±5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results—Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1,000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303-0.75). SVG elevation more accurately predicted SCD if ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526-0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515-0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% SCD events were reclassified from low or intermediate risk to a high-risk category. Long-term, QRS-T angle was the strongest predictor of SCD (AUC 0.710; 95%CI 0.668-0.753 for ECG recorded within 10 years before SCD). Conclusion—Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

Gallbladder tissue from patients with acute acalculous cholecystitis contains increased amounts of prostanoids when compared to normal gallbladder tissue. Platelet-activating factor (PAF) is a potent stimulus of eicosanoid formation. It has been implicated as a mediator of acute inflammatory processes and systemic responses to shock. In this study the role of PAF in acute acalculous cholecystitis was evaluated. Anesthetized cats underwent gallbladder perfusion with a physiologic buffer solution containing [14C]polyethylene glycol as a nonabsorbable tracer to quantitate mucosal water absorption. Platelet-activating factor was infused into the hepatic artery for 2 hours. Control experiments were performed when vehicle alone was infused. Experiments also were performed when indomethacin was administered intravenously and when indomethacin and PAF were administered. Gallbladder mucosal absorption/secretion and perfusate and tissue prostaglandin E (PGE) and 6 keto prostaglandin F1 alpha (6-keto PGF1 alpha) levels were evaluated. Gallbladder inflammation was evaluated by beta-glucuronidase and myeloperoxidase tissue concentrations and by a histologic scoring system. Platelet-activating factor eliminated gallbladder absorption and produced net fluid secretion associated with dose-related increases in perfusate PGE concentrations and gallbladder tissue PGE and 6 keto PGF1 alpha levels when compared to control values. Platelet-activating factor produced significant inflammation in the gallbladder with increases in the histologic score of inflammation and tissue lysosomal enzyme activities. Indomethacin significantly decreased the fluid secretion, prostanoid levels, and inflammation produced by PAF. The results suggest that PAF may induce acute gallbladder inflammation associated with systemic stress through a prostanoid-mediated mechanism.