Qiang, Bo

Introduction: Congenital long QT syndrome (LQTS) is a sudden cardiac death-predisposing genetic disorder that has a characteristic electrocardiographic signature. Specific LQTS genotypes have been associated classically with different qualitative electrocardiographic ST - T wave patterns; however, clinical recognition of these patterns could be improved by an automated and quantitative method. We therefore aimed to use a novel, quantitative T wave morphological analysis technique from 12-lead surface ECGs to identify genotype specific LQTS ECG patterns. Methods: We analyzed a genotyped cohort of 420 patients (22 ± 15.7years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a custom T wave program to automatically detect subtle changes in T wave morphology across all 12 Leads. Classification was performed using the top three T wave features in a linear discriminant classifier by 10x10 cross-validation. Results: The top three discriminant features were (1) a steeper T wave left slope for LQT1 in lead II (2950 vs 1700 mV/ms, p < 0.0001), (2) a greater center of gravity in 1st segment of T wave in lead aVR in LQT1 (0.248 vs 0.209, p<0.0001) and (3) a smaller Tpeak - Tend/QT ratio in lead aVR in LQT1 (0.20 vs 0.24, p<0.0001). Using these top three features, we were able to successfully differentiate LQT1 and LQT2 in 79% of cases (figure 1). Conclusions: An automated morphological T wave analysis was able to discriminate between the two most common LQTS genotypes. This novel method enhances phenotypic characterization of LQT and could have a significant clinical impact on the diagnosis and management of patients with suspected LQTS.

Introduction: Long QT syndrome (LQTS) is a genetic disorder associated with abnormal ventricular repolarization and a predisposition for torsade de pointes and sudden cardiac death. Identification of markers to elucidate high risk patients is of major practical importance. Our aim was to use a novel, automated quantitative T wave analysis program to distinguish symptomatic from asymptomatic LQTS patients. Methods: We analyzed a genotyped cohort of 420 patients (22 ± 15.7years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using novel software that we created to automatically detect subtle changes in T wave morphology. Symptomatic patients were defined as those with a history of syncope or resuscitated cardiac arrest that was suspected of being LQTS triggered. Classification was performed using the top three T wave features in a linear discriminant classifier by 10x10 cross-validation. Results: Symptomatic LQT1 patients have a greater QTc duration in lead aVF (437 msec vs 416 msec, p <0.0001), a greater T wave center of gravity on the x axis (COGx) in lead V2 (0.304 vs 0.280, p <0.0001) and longer Tpeak-Tend/QT in lead II (0.208 vs 0.188, p < 0.0001) than asymptomatic patients with LQT1. Use of these 3 features enabled classification of symptomatic status in 72% of LQT1 cases, compared to 68% when QTc was used to classify alone. Symptomatic LQT2 patients had a longer QTc in Lead 1 (460 vs 418 msec, p < 0.0001), COGx in 4th segment of T wave was greater in lead V5 (0.399 vs 0.350, p<0.0001), and the Tpeak-Tend interval in V2 was longer (126 vs 99 msec, P =0.0004). Use of these 3 features enabled correct classification in 73% of LQT2 cases, compared to 67% when QTc was used alone. Conclusions: Application of an automated T wave analysis program was able to identify T wave features that distinguished the patients who presented as symptomatic LQTS, associated with syncope or resuscitated cardiac arrest. Use of these features could help assess arrhythmic risk and guide management strategies for patients with LQTS.

Esophageal diseases often necessitate resection of tissue. Current reconstruction options using autologous conduits such as stomach, colon, and small bowel have significant morbidity. Given these limitations in current therapy, we have developed a large animal model to regenerate short segments of esophageal tissue after circumferential full thickness resection. This is performed by implantation of an electrospun polyurethane tissue engineered graft which results in esophageal regrowth. These implantations have been performed both with cell based seeding the polymer scaffold and without with comparable regenerative results. It is possible that esophageal regeneration in this model has unequal contributions from either the proximal or distal esophagus. To determine preferential directionality of regeneration we aim to utilize biomechanical markers to suggest that there are unequal proximal and distal contributions to regenerated tissue. Given limited availability of regenerated tissue, use of a non‐destructive method to perform this testing would have significant utility. Through using our previously validated non‐destructive sonometric ex vivo method we aim to determine differences in mechanical behavior of the regenerated tissue in comparison to proximal and distal esophageal tissues. Using this method we are able to determine longitudinal and circumferential moduli and derive strain energy functions and parameters. Within this work we demonstrate that there are differences in mechanical behavior and that the regenerated tissue behaves more comparably to proximal esophageal tissue suggesting preferential regrowth and unequal contribution to regenerated tissue. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Background: Although the hallmark of long-QT syndrome (LQTS) is abnormal cardiac repolarization, there are varying degrees of phenotypic expression and arrhythmic risk. Our aim was to evaluate the performance of a morphological T-wave analysis program in defining breakthrough LQTS arrhythmic risk beyond the QTc value. Methods and Results: We analyzed 407 genetically confirmed patients with LQT1 (n=246; 43% men) and LQT2 (n=161; 41% men) over the mean follow-up period of 6.4±3.9 years. ECG analysis was conducted using a novel, proprietary T-wave analysis program. Time to a LQTS-associated cardiac event was analyzed using Cox proportional hazards regression methods. Twenty-three patients experienced ≥1 defined breakthrough cardiac arrhythmic events with 5- and 10-year event rates of 4% and 7%. Two independent predictors of future LQTS-associated cardiac events from the surface ECG were identified: left slope of T wave in lead V6 (hazard ratio=0.40 [0.24–0.69]; P <0.001) and T-wave center of gravity x axis (last 25% of wave) in lead I (hazard ratio=1.90 [1.21–2.99]; P =0.005), C statistic of 0.77 (0.65–0.89). When added to the QTc (C statistic 0.68 for QTc alone), discrimination improved to 0.78. Genotype analysis showed weaker association between these T-wave variables and LQT1-triggered events while these features were stronger in patients with LQT2 and significantly outperformed the QTc (C statistic, 0.82 [0.71–0.93]). Conclusion: Detailed morphological analysis of the T wave provides novel insights into risk of breakthrough arrhythmic events in LQTS, particularly LQT2. This observation has the potential to guide clinical decision making and further refine risk stratification.

BackgroundProlongation of the QT on the surface electrocardiogram can be due to either genetic or acquired causes. Distinguishing congenital long QT syndrome (LQTS) from acquired QT prolongation has important prognostic and management implications. We aimed to investigate if quantitative T‐wave analysis could provide a tool for the physician to differentiate between congenital and acquired QT prolongation.MethodsPatients were identified through an institution‐wide computer‐based QT screening system which alerts the physician if the QTc ≥ 500 ms. ECGs were retrospectively analyzed with an automated T‐wave analysis program. Congenital LQTS was compared in a 1:3 ratio to those with an identified acquired etiology for QT prolongation (electrolyte abnormality and/or prescription of known QT prolongation medications). Linear discriminant analysis was performed using 10‐fold cross‐validation to statistically test the selected features.ResultsThe 12‐lead ECG of 38 patients with congenital LQTS and 114 patients with drug‐induced and/or electrolyte‐mediated QT prolongation were analyzed. In lead V5, patients with acquired QT prolongation had a shallower T wave right slope (−2,322 vs. −3,593 mV/s), greater T‐peak‐Tend interval (109 vs. 92 ms), and smaller T wave center of gravity on the x axis (290 ms vs. 310 ms; p < .001). These features could distinguish congenital from acquired causes in 77% of cases (sensitivity 90%, specificity 58%).ConclusionT‐wave morphological analysis on lead V5 of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.

Background— 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 guidance while mutation-specific genetic testing is in motion for family members.