A rare pediatric case of left ventricular outflow tract infective endocarditis in hypertrophic cardiomyopathy

Clinical profile and outcomes of pediatric hypertrophic cardiomyopathy in a South Indian tertiary care cardiac center: a three decade experience

INTRODUCTION

Although much research has been done on adult hypertrophic cardiomyopathy, data on pediatric hypertrophic cardiomyopathy is still limited.

METHODS AND RESULTS

The study enrolled all patients with cardiomyopathy who presented to us between 1990 to 2020 and were younger than 18 yrs. During the thirty-year study period, we identified 233 cases of pediatric cardiomyopathy. Sixty-three cases (27%) had hypertrophic cardiomyopathy. Out of the 63 HCM cases, 12% presented in the neonatal period and 37% presented in the first year of life. The median age of presentation was 7 yrs (Range 0.1-18 yrs). Sixteen patients had proven syndromic, metabolic, or genetic disease (25%). LV outflow obstruction was present in 30 patients (47%). Noonan syndrome was present in 9 of the 63 patients (14%). Dyspnea on exertion was the most common mode of presentation. Cardiac MRI was done in 28 patients, out of which 17 had late gadolinium enhancement (LGE). Mid myocardial enhancement was the most common pattern. Four patients had LGE of more than 15%. Over a mean follow-up period of 5.6 years (0.1-30 years), twenty-one were lost to follow-up (33%). Among the patients whose outcome was known, eleven died (26%), and thirty-one (73%) were alive. The 5-year survival rate of HCM patients was 82%, and the 10-year survival rate was 78%. Seven died of sudden cardiac death, three from heart failure, and one from ventricular arrhythmias. Sustained ventricular arrhythmias were seen in three patients and atrial arrhythmias in two. First-degree AV block was seen in 10 patients (15%) and bundle branch blocks (BBB) in five (8%). Eight patients required ICD or transplant (12.7%). Two patients underwent ICD for primary prevention, and one underwent PPI for distal AV conduction disease. Among the various clinical, echocardiographic, and radiological risk factors studied, only consanguinity showed a trend towards higher events of death or ventricular arrhythmias (P-value 0.08).

CONCLUSION

More than one-third of our HCM cohort presented in infancy. LV outflow tract obstruction is common (47%). Mid myocardial enhancement was the most common pattern of late gadolinium enhancement. SCD was the most common cause of death. The outcome in our HCM cohort is good and similar to other population cohorts. Only Consanguinity showed a trend towards higher events of death or ventricular arrhythmias.

The predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy complicating arrhythmias

OBJECTIVE

To investigate the predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy (HCM) complicated by arrhythmias.

METHODS

The clinical data of 158 patients with hypertrophic cardiomyopathy were retrospectively collected from July 2019 to December 2021, and additionally divided into training group 106 cases, validation group 26 cases and test group 26 cases according to the ratio of 4:1:1, and divided into concurrent and non-concurrent groups according to whether they were complicated by arrhythmia or not, respectively. General data of patients (age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR) were collected, a deep learning model for cardiac ultrasound flow imaging was established, and image data, LVEF, LAVI, E/e', vortex area change rate, circulation intensity change rate, mean blood flow velocity, and mean EL value were extracted.

RESULTS

The differences in general data (age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR) between the three groups were not statistically significant, P > 0.05. The differences in age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR between the patients in the concurrent and non-concurrent groups in the training group were not statistically significant, P > 0.05.

CONCLUSIONS

Deep learning-based cardiac ultrasound flow imaging can identify cardiac ultrasound images more accurately and has a high predictive value for arrhythmias complicating hypertrophic cardiomyopathy, and vortex area change rate, circulation intensity change rate, mean flow velocity, mean EL, LAVI, and E/e' are all risk factors for arrhythmias complicating hypertrophic cardiomyopathy.