Simplified echocardiography as a tool for community screening of rheumatic heart disease: A systematic review and meta-analysis on the role of nonexpert operators - Putra, I Gusti Ngurah Agung Dian Angga; Indirayanti, I Gusti Ayu Thasya (Edelweiss Applied Science and Technology, 2025)
Rheumatic heart disease (RHD) remains a major global health challenge, particularly in low- and middle-income countries with limited access to expert echocardiography. Simplified echocardiography performed by non-expert operators is a scalable solution for community-level screening. This systematic review and meta-analysis aim to evaluate the diagnostic accuracy, feasibility, and effectiveness of non-expert-led echocardiographic screening using simplified protocols. A systematic search of PubMed, Web of Science, Scopus, ProQuest, and ScienceDirect identified eligible studies. Inclusion criteria encompassed studies evaluating non-expert-operated echocardiography for RHD screening. A bivariate random-effects meta-analysis estimated pooled sensitivity, specificity, and area under the curve (AUC), adhering to PRISMA guidelines. Eight studies met inclusion criteria, involving various non-expert operators (e.g., nurses, medical students, community health workers). Pooled sensitivity and specificity for the MR ≥ 1.5 cm or any AR threshold were 0.76 (95% CI: 0.63–0.82) and 0.86 (95% CI: 0.79–0.90), with an AUC of 0.88 and SROC curve I² of 96%. For the MR ≥ 2 cm or any AR threshold, specificity rose to 0.94 (95% CI: 0.91–0.96) but sensitivity dropped to 0.57 (95% CI: 0.42–0.71), with an AUC of 0.91 and SROC curve I² of 91%. Substantial heterogeneity was observed, likely from variations in operator training and protocols. Simplified echocardiography with proper training is a promising screening method for RHD in resource-limited areas. Despite variability in image acquisition and operator performance, task-sharing models can significantly improve RHD detection. Further research is needed to refine screening protocols and assess cost-effectiveness in large-scale programs.