Johnson, Jonathan N.

AbstractIntroductionPediatric heart transplant patients are routinely followed in dermatology clinics due to elevated risk of cutaneous malignancy. However, transplant patients may experience other, non‐cancer‐related dermatologic conditions including skin infections, inflammatory diseases, and drug eruptions that can cause significant medical and psychosocial comorbidity.MethodsA retrospective chart review of all pediatric heart transplant patients at Mayo Clinic Children's Center in Rochester, MN, was performed to determine the prevalence and spectrum of non‐cancer dermatologic conditions. Statistical analysis was conducted to look for associations between episodes of rejection and skin condition development.ResultsOf the 65 patients who received heart transplants under the age of 18 and were followed at Mayo Clinic, 69% (N = 45) were diagnosed with at least one skin condition between transplant and the time of most recent follow‐up. Sixty‐two percent (N = 40) of patients were diagnosed with an inflammatory skin condition (most commonly acne and atopic dermatitis), 45% (N = 29) with an infectious skin condition (most commonly warts and dermatophyte infection), and 32% (N = 21) with a drug eruption (most commonly unspecified rash and urticaria). No association was found between presence of skin disease and number of rejection episodes.ConclusionsNon‐cancer dermatologic conditions are prevalent within pediatric heart transplant recipients and may directly impact their medical needs and quality of life. Dermatologist involvement in the care of post‐transplant pediatric patients is important, not only for cancer screening but also for diagnosis and treatment of common infectious and inflammatory skin conditions.

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.

Introduction: Left and right ventricular systolic dysfunction (LVSD and RVSD respectively) contribute to considerable pediatric morbidity. Both LVSD and RVSD can occur in children before symptom onset, emphasizing the need for early detection. Existing deep-learning algorithms can identify LVSD in adults using 12-lead ECG; however, their efficacy in children is uncertain. Aims: We aimed to create a deep-learning model for LVSD and RVSD detection in pediatric patients using an AI-enabled ECG and compare novel pediatric models to a previously validated adult LVSD model. Methods: We identified 10,214 pediatric patients at the Mayo Clinic with a transthoracic echocardiogram (TTE) and a 10-second, 12-lead surface ECG within 14 days of TTE, performed from 2002 -2022. Our cohort comprised the first TTE-ECG pair from each patient, totaling 138 with EF ≤ 35%, 312 with EF ≤ 50%, 482 with RVSD, and 9,567 without any LVSD or RVSD. The cohort was split into training, validation, and testing datasets to develop neural networks for systolic dysfunction detection. Results: The cohort was 52.3% male, with an average age of 10.3 years. Novel models generated in the pediatric cohort demonstrated excellent performance in systolic dysfunction detection (Figure 1). Pediatric models for EF ≤ 35% and EF ≤ 50% achieved test AUCs of 0.92 (95% CI 0.89, 0.95) and 0.89 (95% CI 0.85, 0.94) respectively. The RVSD model reached a test AUC of 0.91 (95% CI 0.88, 0.94). A combined model for EF ≤ 35% or RVSD exhibited a test AUC of 0.90 (95% CI 0.87, 0.93), sensitivity 0.88, specificity 0.82, PPV 0.20, and NPV 0.95. A combined model for EF ≤ 50% or RVSD exhibited a test AUC of 0.88 (95% CI 0.85, 0.91). The previously validated adult data-derived model achieved an AUC of 0.86 (95% CI 0.83, 0.89) for EF ≤ 50% in children. Conclusions: AI-enabled ECGs demonstrate efficient detection of both LVSD and RVSD in pediatric patients. Future multi-site validation is needed to further establish the reliability of these models.

Introduction: Biologic sex and hormonal concentrations shape ECG parameters due to sex hormone effects on cardiac function. AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. Aims: We aimed to test the ability of AI-enabled ECGs to predict pediatric patient sex and explore the influence of pubertal development. Methods: Two AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead surface ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 89,063 pediatric patients (aged <19 years) recorded from 1988-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), pubertal (8-14 years), and post-pubertal (15-18 years) patients. Results: The cohort consisted of 46.6% males, with 21,434 prepubertal, 26,300 pubertal, and 41,329 post-pubertal children. No significant performance difference was observed between the two models. Both models demonstrated 81% accuracy and an overall AUC of 0.91 in the testing cohorts, with high discriminatory ability in post-pubertal teenagers (AUC = 0.98). (Figure 1) Conclusions: AI-ECG predicts pediatric patient sex with varying performance across pubertal stages. The highest discriminatory ability is observed post-puberty, decreasing in pubertal and prepubertal children. The results suggest hormonal and physiological changes during puberty may influence cardiac electrophysiology measurably. More research is required to interpret CNN-predicted sex discordance with actual sex and its implications for future cardiovascular risk.

Background Little is known about outcomes following heart failure (HF) hospitalization among adults with congenital heart disease (CHD) in the United States. We aim to compare the outcomes of HF versus non‐HF hospitalizations in adults with CHD. Methods and Results Using a national deidentified administrative claims data set, patients with adult congenital heart disease (ACHD) hospitalized with and without HF (ACHDHF+, ACHDHF−) were characterized to determine the predictors of 90‐day and 1‐year mortality and quantify the risk of mortality, major adverse cardiac and cerebrovascular events, and health resource use. Cox proportional hazard regression was used to compare ACHDHF+ versus ACHDHF− for risk of events and health resource use. Of 26 454 unique ACHD admissions between January 1, 2010 and December 31, 2020, 5826 (22%) were ACHDHF+ and 20 628 (78%) were ACHDHF−. The ACHD HF+ hospitalizations increased from 6.6% to 14.0% ( P <0.0001). Over a mean follow‐up period of 2.23 ± 2.19 years, patients with ACHDHF+ had a higher risk of mortality (hazard ratio [HR], 1.86 [95% CI, 1.67–2.07], P <0.001), major adverse cardiac and cerebrovascular events (HR, 1.73 [95% CI, 1.63–1.83], P <0.001) and health resource use including rehospitalization (HR, 1.09 [95% CI, 1.05–1.14], P <0.001) and increased postacute care service use (HR, 1.56 [95% CI, 1.32–1.85], P <0.001). Cardiology clinic visits within 30 days of hospital admission were associated with lower 90‐day and 1‐year all‐cause mortality (odds ratio [OR], 0.62 [95% CI, 0.49–0.78], P <0.001; OR, 0.69 [95% CI, 0.58–0.83], P <0.001, respectively). Conclusions HF hospitalization is associated with increased risk of mortality and morbidity with high health resource use in patients with ACHD. Recent cardiology clinic attendance appears to mitigate these risks.

AbstractBackgroundThe SARS‐CoV‐2 pandemic and corresponding acute respiratory syndrome have affected all populations and led to millions of deaths worldwide. The pandemic disproportionately affected immunocompromised and immunosuppressed adult patients who had received solid organ transplants (SOTs). With the onset of the pandemic, transplant societies across the world recommended reducing SOT activities to avoid exposing immunosuppressed recipients. Due to the risk of COVID‐19‐related outcomes, SOT providers adapted the way they deliver care to their patients, leading to a reliance on telehealth. Telehealth has helped organ transplant programs continue treatment regimens while protecting patients and physicians from COVID‐19 transmission. This review highlights the adverse effects of COVID‐19 on transplant activities and summarizes the increased role of telehealth in the management of solid organ transplant recipients (SOTRs) in both pediatric and adult populations.MethodsA comprehensive systematic review and meta‐analysis were conducted to accentuate the outcomes of COVID‐19 and analyze the efficacy of telehealth on transplant activities. This in‐depth examination summarizes extensive data on the clinical detriments of COVID‐19 in transplant recipients, advantages, disadvantages, patient/physician perspectives, and effectiveness in transplant treatment plans via telehealth.ResultsCOVID‐19 has caused an increase in mortality, morbidity, hospitalization, and ICU admission in SOTRs. Telehealth efficacy and benefits to both patients and physicians have increasingly been reported.ConclusionsDeveloping effective systems of telehealth delivery has become a top priority for healthcare providers during the COVID‐19 pandemic. Further research is necessary to validate the effectiveness of telehealth in other settings.

The congenital heart surgeon frequently encounters patients with various genetic disorders requiring surgical intervention. Although the specifics of the genetics for these patients and their families lie in the purview of specialists in genetics, the surgeon is well-served to be familiar with aspects of specific syndromes that impact surgical management and perioperative care. This aids in counseling families in expectations for the hospital course and recovery as well as can impact intraoperative and surgical management. This review article summarizes key characteristics for the congenital heart surgeon to be familiar with for common genetic disorders as they help coordinate care.