» Articles » PMID: 35837354

Prevalence of Structural Heart Diseases Detected by Handheld Echocardiographic Device in School-Age Children in Iran: The SHED LIGHT Study

Abstract

Background: Structural heart disease (SHD) has great impacts on healthcare systems, creating further public health concerns. Proper data are scant regarding the magnitude of the affected population by SHD.

Objectives: This study aimed to determine the prevalence of SHD among children and adolescents in an Iranian population.

Methods: In this population-based study, a multistage cluster-random sampling was used to choose schools from the Tehran urban area. All students were examined using a handheld Vscan device by echocardiographer, and the results were concurrently supervised and interpreted by cardiologists. All the major findings were reevaluated in hospital clinics.

Results: Of 15,130 students (6-18 years, 52.2% boys) who were examined, the prevalence of individuals with congenital heart disease (CHD) and cardiomyopathy was 152 (10.046 per 1,000 persons) and 9 (0.595 per 1,000 persons), respectively. The prevalence of definite and borderline rheumatic heart disease (RHD) was 30 (2 per 1,000 persons) and 113 (7.5 per 1,000 persons), correspondingly. Non-rheumatic valvular heart disease (VHD) was also detected in 465 (30.7 per 1,000 persons) students. Of all the pathologies, only 39 (25.6%) cases with CHD and 1 (0.007%) cases with RHD had already been diagnosed. Parental consanguinity was the strongest predictor of CHD and SHD (odds ratio [OR]: 1.907, 95% CI, 1.358 to 2.680; P < 0.001 and OR, 1.855, 95% CI, 1.334 to 2.579; P < 0.001, respectively). The female sex (OR, 1.262, 95% CI, 1.013 to 1.573; P = 0.038) and fathers' low literacy (OR, 1.872, 95% CI, 1.068 to 3.281; P = 0.029) were the strongest predictors of non-rheumatic VHD and RHD, correspondingly.

Conclusions: The implementation of echocardiographic examinations for detecting SHD among young population is feasible which detected SHD prevalence in our population comparable to previous reports. Further studies are required to delineate its economic aspects for community-based screening.

Citing Articles

Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.

Hajianfar G, Khorgami M, Rezaei Y, Amini M, Samiei N, Tabib A Cardiovasc Eng Technol. 2023; 14(6):786-800.

PMID: 37848737 DOI: 10.1007/s13239-023-00687-x.

References
1.
Mirabel M, Badano L . Leveraging Mobile Technology to Reduce Resource-Related Health Care Disparities: Challenges and Opportunities. JACC Cardiovasc Imaging. 2017; 11(4):558-560. DOI: 10.1016/j.jcmg.2017.08.015. View

2.
Shrestha N, Karki P, Mahto R, Gurung K, Pandey N, Agrawal K . Prevalence of Subclinical Rheumatic Heart Disease in Eastern Nepal: A School-Based Cross-sectional Study. JAMA Cardiol. 2016; 1(1):89-96. DOI: 10.1001/jamacardio.2015.0292. View

3.
Marijon E, Ou P, Celermajer D, Ferreira B, Mocumbi A, Jani D . Prevalence of rheumatic heart disease detected by echocardiographic screening. N Engl J Med. 2007; 357(5):470-6. DOI: 10.1056/NEJMoa065085. View

4.
Williams K, Thomson D, Seto I, Contopoulos-Ioannidis D, Ioannidis J, Curtis S . Standard 6: age groups for pediatric trials. Pediatrics. 2012; 129 Suppl 3:S153-60. DOI: 10.1542/peds.2012-0055I. View

5.
Marelli A, Ionescu-Ittu R, Mackie A, Guo L, Dendukuri N, Kaouache M . Lifetime prevalence of congenital heart disease in the general population from 2000 to 2010. Circulation. 2014; 130(9):749-56. DOI: 10.1161/CIRCULATIONAHA.113.008396. View