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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 are poorly understood., Methods: Individuals with RHD who met indications for valve surgery were identified using the Uganda National RHD Registry (January 2010-August 2022). Kaplan-Meier estimates and multivariable Cox proportional hazard models were used., Results: Of the cohort with clinical RHD, 64% (1452 of 2269) met criteria for an index operation. Of those, 13.5% obtained a surgical intervention, whereas 30.6% died before the operation. The estimated likelihood of first operation 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 postsecondary education vs primary school or less (HR, 3.60; 95% CI, 1.88-6.89), and those with a history of atrial fibrillation (HR, 2.78; 95% CI, 1.63-4.76). Surgical intervention was less likely for adults vs those aged <18 years (HR, 0.49; 95% CI, 0.32-0.77) and those with New York Heart Association Functional Class III/IV vs I/II (HR, 0.51; 95% CI, 0.32-0.83). The median preoperative survival time among those awaiting first operation was 4.6 years (95% CI, 3.9-5.7 years). History of infective endocarditis, right ventricular dysfunction, pericardial effusion, atrial fibrillation, and having surgical indications for multiple valves were associated with increased probability of death., Conclusions: Our analysis revealed a prolonged time to first surgical intervention and high preintervention death for RHD in Uganda, with factors such as age, sex, and education level remaining barriers to obtaining surgery., Competing Interests: Disclosures The authors have no conflicts of interest to disclose., (Copyright © 2024 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

A 19-month-old boy presented with a murmur and was found to have an unusual etiology of subvalvar aortic stenosis with discrete subaortic membrane and anomalous attachment of the anterior mitral valve papillary muscle to the interventricular septum. Preoperative suspicion for mitral valve involvement impacted surgical planning., Competing Interests: The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

Background: Health-related quality of life (HRQOL) is a critical issue for patients undergoing surgery for congenital heart disease (CHD) but has never been assessed in a low-income country. We conducted a cross-sectional mixed methods study with age-matched healthy siblings serving as controls at the Uganda Heart Institute., Methods: One-hundred fifteen CHD pediatric and young adult patients and sibling control participants were recruited. Health-related quality of life was assessed using the Pediatric Quality of Life Inventory Version 4.0 in participants ages 5-17 and 36-Item Short Form Survey for young adults aged 18-25. A subset of 27 participants completed face-to-face interviews to supplement quantitative findings., Results: Eighty-six pediatric (age 5-17) sibling and parent pairs completed Peds QOL surveys, and 29 young adult (age 18-25) sibling pairs completed SF-36 surveys. One third of patients had surgery in Uganda. Ventricular septal defects and tetralogy of Fallot were the most common diagnoses. Health-related quality of life scores in patients were lower across all domains compared to control participants in children. Reductions in physical and emotional domains of HRQOL were also statistically significant for young adults. Variables associated with lower HRQOL score on multivariate analysis in pediatric patients were younger age in the physical and emotional domains, greater number of surgeries in the physical domain and surgery outside Uganda in the school domain. The only predictor of lower HRQOL score in young adults was surgery outside Uganda in the social domain. Qualitative interviews identified a number of themes that correlated with survey results including abandonment by family, isolation from peers and community, financial hardship and social stigmatization., Conclusion: Health-related quality of life was lower in Ugandan patients after CHD surgery than siblings. Younger patients and those who had surgery outside of Uganda had lower HRQOL. These data have important implications for patients undergoing CHD surgery in LMIC and have potential to inform interventions., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2024 The Author(s).)

Background: Secondary antibiotic prophylaxis reduces progression of latent rheumatic heart disease (RHD) but not all children benefit. Improved risk stratification could refine recommendations following positive screening. We aimed to evaluate the performance of a previously developed echocardiographic risk score to predict mid-term outcomes among children with latent RHD., Methods: We included children who completed the GOAL, a randomized trial of secondary antibiotic prophylaxis among children with latent RHD in Uganda. Outcomes were determined by a 4-member adjudication panel. We applied the point-based score, consisting of 5 variables (mitral valve (MV) anterior leaflet thickening (3 points), MV excessive leaflet tip motion (3 points), MV regurgitation jet length ≥ 2 cm (6 points), aortic valve focal thickening (4 points) and any aortic regurgitation (5 points)), to panel results. Unfavorable outcome was defined as progression of diagnostic category (borderline to definite, mild definite to moderate/severe definite), worsening valve involvement or remaining with mild definite RHD., Results: 799 patients (625 borderline and 174 definite RHD) were included, with median follow-up of 24 months. At total 116 patients (14.5%) had unfavorable outcome per study criteria, 57.8% not under prophylaxis. The score was strongly associated with unfavorable outcome (HR = 1.26, 95% CI 1.16-1.37, p < 0.001). Unfavorable outcome rates in low (≤6 points), intermediate (7-9 points) and high-risk (≥10 points) children at follow-up were 11.8%, 30.4%, and 42.2%, (p < 0.001) respectively (C-statistic = 0.64 (95% CI 0.59-0.69))., Conclusions: The simple risk score provided an accurate prediction of RHD status at 2-years, showing a good performance in a population with milder RHD phenotypes., Competing Interests: Declaration of Competing Interest The authors of the manuscript have no relevant conflicts of interest to disclose regarding this study., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms., Methods and Results: We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements ( P value=0.83) and a 9-feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87., Conclusions: Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.