Niaz, Talha

Background Bicuspid aortic valve (BAV) is the most common congenital heart defect, associated with a lifetime morbidity burden exceeding 80% and an estimated 7% probability of transmission to first-degree relatives. Hence, echocardiographic screening of relatives is recommended. The aim of our study is to develop an artificial intelligence-enabled electrocardiogram (AI-ECG) model for the detection of BAV. Methods Between January 1, 1990, and June 30, 2023, the Mayo Clinic healthcare system database was searched for patients aged ≥18 years with echocardiograms performed for family history of BAV, abnormal auscultation, a BAV-related diagnosis, or to rule out the condition, and with an electrocardiogram (ECG) obtained within 6 months. Cases were patients with a confirmed BAV diagnosis, and controls were those with normal aortic valves. ECGs were randomly assigned into training (80%), internal validation (10%), and test (10%) groups. The AI-ECG model was built using Keras under TensorFlow. Results A total of 13,065 valid ECG–echocardiogram pairs were included (mean [SD] age, 58 [17] years; 44% women), with a median time of 1 day (interquartile range, 0–3 days) between studies. With a 45% BAV prevalence in the test group ( n = 1301), the area under the curve was 0.704, positive predictive value (PPV) 60.1%, and negative predictive value (NPV) 69.4%. At a prevalence of 7%, the NPV increased to 96.1%, but the PPV decreased to 12.2%. Conclusions In this retrospective cohort and using the current AI-ECG model, BAV detection showed modest diagnostic performance and limited standalone clinical utility, suggesting that ECG-only screening for BAV may require additional modalities or more refined phenotyping.

Abstract Background DDX3X- syndrome is a rare neurodevelopmental disorder characterized by varying degrees of intellectual disability, predominantly affecting females. We present an institutional cohort supplemented by a systematic literature review, expanding the cardiovascular phenotype of DDX3X- syndrome. Methods We conducted a retrospective chart review of patients diagnosed with DDX3X -syndrome at Mayo Clinic. Additionally, we performed a systematic literature review to identify studies reporting cardiovascular abnormalities in patients with DDX3X -syndrome. Results A total of 200 patients with DDX3X -syndrome were analyzed, comprising 14 patients from our institutional cohort and 186 patients identified through a systematic review of 9 published studies. Our institutional cohort included 14 patients (12 females and 2 males) from 13 unrelated families diagnosed with DDX3X -syndrome, at a median age of 4.5 years (IQR 1.2–9.5). Echocardiogram was performed on nine patients, and cardiovascular abnormalities were found in 7 out of 9 patients who underwent echocardiography (78%), two of whom had major congenital heart defect (CHD) requiring surgical intervention. At the time of assessment, 13 individuals were still alive, while one had died at age six due to extracardiac complications. The systematic review included 9 studies involving 186 patients, of whom 32 (17.4%, 25 females and 7 males) had reported cardiovascular abnormalities, ranging from simple CHDs to more complex defects. Conclusion DDX3X -syndrome carries a significant cardiovascular burden, which is possibly higher than previously reported, including complex congenital heart disease requiring surgical repair. A thorough cardiovascular assessment, including an electrocardiogram and echocardiogram, should be universally recommended for all patients at the time of diagnosis.

Background: Cardiac disease is the leading cause of premature mortality in Friedreich Ataxia (FA). However, validated prognostic indicators or early imaging biomarkers that can reliably predict cardiac events are lacking. To address this gap, we created a multicenter FA cardiac registry to characterize disease progression and identify early echocardiographic (echo) predictors of major adverse cardiac events (MACE). Methods: FA subjects from 6 North American centers with at least two echos performed ≥5 years apart were included. Demographics, FA-specific disease characteristics, and MACE (a composite of death, heart failure hospitalization, aborted sudden cardiac death, and/or life-threatening arrhythmia) were abstracted. Digital echo images were re-read at a centralized core lab. Paired T tests were utilized for comparison of the normally distributed first and second echo parameters, and logistic regression models were utilized to evaluate for predictors of MACE. Results: Of 115 subjects, 52 (45%) were female. Mean age at diagnosis was 10 ± 3 years, median GAA1 repeat length (a marker of genetic severity) was 700 (range 66–1200), and median age at first analyzed echo was 12 years (range 4-36). MACE occurred in 35 (30%) of the cohort. Serial echocardiograms demonstrated progressive worsening of multiple cardiac parameters over time, including markers of ventricular thickness and hypertrophy, chamber size, systolic function, and novel markers of atrial and ventricular strain (Table 1). On univariate analysis, several markers of hypertrophy and systolic function from the earliest analyzed echo predicted MACE. However, after adjusting for age of diagnosis, GAA1 repeat length, and collinearity among variables, only posterior wall thickness in diastole (PWTd) remained an independent predictor of MACE (odds ratio 1.8 per mm increase, 95% CI 1.1–3.3, p = 0.028). Conclusions: This multicenter FA registry identifies early posterior wall thickness as a significant and independent predictor of major adverse cardiac events in FA. Each millimeter increase in PWTd on early echo is associated with nearly double the odds of MACE. This commonly acquired parameter is often overlooked and may serve as a powerful early marker of cardiac risk. Further investigation of PWTd as a potential surrogate endpoint for therapeutic trials in FA is warranted.

Aortic dilation following single ventricle palliation is increasingly recognized, although its long-term clinical implications remain unclear. We present the case of a 38-year-old male born with a bicuspid aortic valve, d-transposition of the great arteries, ventricular septal defect, right ventricular hypoplasia with a straddling right atrioventricular valve. He was palliated with Fontan circulation and developed severe neo-aortic root and arch dilation with native aortic valve stenosis, requiring surgical intervention. His neo-aortic root measured 81 × 65 mm, necessitating a Bentall procedure with total arch and native aortic valve replacement. This case underscores the variable progression of aortic dilation in patients with palliated single ventricle physiology and the need for strict surveillance and individualized management. Larger cohort studies are needed to further refine the criteria for surgical intervention in this unique population.

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. Methods and Results We identified 10 142 unique pediatric patients (age≤18) with a 10‐second, 12‐lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89–0.98) and 0.88 (95% CI, 0.83–0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data‐derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84–0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87–0.94). Conclusions An artificial intelligence–enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult‐trained models offer good performance, improvements are seen when training pediatric‐specific models.

ABSTRACTNoonan syndrome (NS) is a predominantly autosomal dominant condition with various cardiac and extra‐cardiac manifestations. Although it has been linked with atrial arrhythmias, ventricular arrhythmias are extremely rare in the absence of underlying structural cardiac abnormalities. We report an instance of aborted sudden cardiac arrest in a 7‐year‐old male with a confirmed SOS1 variant and a lack of evidence to support a structural cardiac, metabolic, or infectious etiology. This is the second reported instance of sudden cardiac arrest related to ventricular fibrillation in a child with SOS1‐related NS in the absence of any structural cardiac defects. Although no definitive correlation can be ascertained from a limited existing body of knowledge surrounding SOS1 and ventricular fibrillation unrelated to structural heart defects, it provokes the idea of an arrhythmia phenotype and future research is warranted to guide proper clinical treatment, monitoring, and management of such individuals.

Background Quadricuspid aortic valve (QAV) is an exceedingly rare congenital heart defect (CHD) which has not been well-defined in a pediatric population. Methods The Mayo Clinic echocardiography database was retrospectively analyzed to identify patients ≤18 years diagnosed with QAV from January 1990 to December 2023. Patients with truncus arteriosus were excluded. All images were independently reviewed to define morphology of the QAV by using the Hurwitz and Roberts classification. Results Fourteen patients with QAV were identified with a median age at time of diagnosis being 10.5 years (interquartile range [IQR] 6-14 years). Male-to-female ratio was 3:1. Associated CHDs were present in 50% (n = 7) patients. The most common morphological subtypes of QAV were Type D in 43% (n = 6) and Type B in 29% (n = 4). Aortic regurgitation was the most frequently associated valvular abnormality affecting 86% (n = 12) cases, with greater than moderate regurgitation in only two patients. Aortic valve stenosis was observed in 14% (n = 2) patients. Ascending aortic dilatation was present in 21% (3/14) of the cohort, but only 14% (1/7) of isolated QAV patients. At a mean follow up of 11 ± 6.6 years and a median follow-up age of 22 years (IQR 14-27 years), survival was 100% with no primary interventions on the aortic valve or aorta. However, four patients required surgical interventions for associated CHDs. Conclusion Among children with QAV, almost half of the patients had additional CHD. Aortic regurgitation was the predominant hemodynamic abnormality. Long-term survival was excellent with minimal progression during childhood and adolescence.

Background: Atrial septal defects (ASD) and ventricular septal defects (VSD) are among the most common forms of congenital heart disease (CHD). Early detection and intervention for these defects are crucial to prevent complications. Given advancements in AI technology, the development of AI-enabled ECG models for detecting these defects is of significant interest. Aims: This study aimed to create AI models using pediatric ECG data for the detection of ASD, VSD, and a combined model detecting either ASD or VSD. Methods: We analyzed a dataset of 7,795 pediatric patients from Mayo Clinic who had undergone echocardiography between 2002 and 2022, with corresponding 10-second, 12-lead surface ECGs taken within 14 days of echocardiography. This dataset included 259 patients with ASDs and 139 patients with VSDs (single or multiple). The control group was comprised of 7,397 patients without significant or complex CHD. For cases and controls, we included patients with all forms of electrocardiographic abnormalities and those with valvular lesions who had less than moderate stenosis or regurgitation. The dataset was divided into training, validation, and testing subsets to develop convolutional neural network (CNN) models for ASD, VSD, and either ASD or VSD detection. Results: Our ASD and VSD detection models achieved test AUCs of 0.82 (95% CI 0.76, 0.88) and 0.80 (95% CI 0.70, 0.90), respectively. Meanwhile, a combined model that detected either ASD or VSD demonstrated a test AUC of 0.81 (95% CI, 0.76, 0.86), sensitivity of 0.73, specificity of 0.76, PPV of 0.14, and NPV of 0.98 (Figure 1). Conclusions: AI-enabled ECG models demonstrate promising performance in detecting ASD and VSD in pediatric patients. A combined model that detects either ASD or VSD also shows robust results. Future research should focus on multi-center validation of these models, refining their diagnostic capacities, and investigating their potential for early intervention in CHDs.