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BACKGROUND: There is an unmet surgical burden among people living with rheumatic heart disease (RHD) in Uganda. Nevertheless, risk factors associated with time to first intervention and preoperative mortality is poorly understood. METHODS: Individuals with RHD who met indications for valve surgery were identified using the Uganda National RHD Registry (Jan. 2010- Aug. 2022). Kaplan-Meier estimates and multivariable Cox proportional hazard models were utilized. RESULTS: 64% of the cohort with clinical RHD (1452 of 2269) met criteria for index operation. Of those, 13.5% obtained surgical intervention while 30.6% died before surgery. The estimated likelihood of first surgery was 50% at 9.3 years of follow up (95% CI 8.1-upper limit not reached). Intervention was more likely in men vs. women (hazard ratio [HR] 1.78; 95% CI 1.21-2.64), those with post-secondary education vs. primary school or less (HR 3.60; 95% CI 1.88-6.89), and those with history of atrial fibrillation (HR 2.78; 95% CI 1.63-4.76). Surgery was less likely for adults (vs. those <18 years; HR 0.49; 95% CI 0.32-0.77) and those with NYHA class III/IV (vs. I/II; HR 0.51; 95% CI 0.32-0.83). The median preoperative survival time among those awaiting surgery was 4.6 years (95% CI, 3.9-5.7). History of infective endocarditis, RV dysfunction, pericardial effusion, atrial fibrillation, and having surgical indications for multiple valves were associated with increased mortality. CONCLUSIONS: Our analysis revealed a prolonged time to first surgical intervention and high pre-intervention mortality for RHD in Uganda, with factors such as age, sex, and education level remaining barriers to obtaining surgery.
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INTRODUCTION: A novel technology utilizing artificial intelligence (AI) to provide real-time image-acquisition guidance, enabling novices to obtain diagnostic echocardiographic images, holds promise to expand the reach of echo screening for rheumatic heart disease (RHD). We evaluated the ability of nonexperts to obtain diagnostic-quality images in patients with RHD using AI guidance with color Doppler. METHODS: Novice providers without prior ultrasound experience underwent a 1-day training curriculum to complete a 7-view screening protocol using AI guidance in Kampala, Uganda. All trainees then scanned 8 to 10 volunteer patients using AI guidance, half RHD and half normal. The same patients were scanned by 2 expert sonographers without the use of AI guidance. Images were evaluated by expert blinded cardiologists to assess (1) diagnostic quality to determine presence/absence of RHD and (2) valvular function and (3) to assign an American College of Emergency Physicians score of 1 to 5 for each view. RESULTS: Thirty-six novice participants scanned a total of 50 patients, resulting in a total of 462 echocardiogram studies, 362 obtained by nonexperts using AI guidance and 100 obtained by expert sonographers without AI guidance. Novice images enabled diagnostic interpretation in >90% of studies for presence/absence of RHD, abnormal MV morphology, and mitral regurgitation (vs 99% by experts, P ≤ .001). Images were less diagnostic for aortic valve disease (79% for aortic regurgitation, 50% for aortic stenosis, vs 99% and 91% by experts, P < .001). The American College of Emergency Physicians scores of nonexpert images were highest in the parasternal long-axis images (mean, 3.45; 81% ≥ 3) compared with lower scores for apical 4-chamber (mean, 3.20; 74% ≥ 3) and apical 5-chamber images (mean, 2.43; 38% ≥ 3). CONCLUSIONS: Artificial intelligence guidance with color Doppler is feasible to enable RHD screening by nonexperts, performing significantly better for assessment of the mitral than aortic valve. Further refinement is needed to optimize acquisition of color Doppler apical views.