Mortality and Morbidity in Adults With Rheumatic Heart Disease
Bibliographic record
Abstract
Importance: Rheumatic heart disease (RHD) remains a public health issue in low- and middle-income countries (LMICs). However, there are few large studies enrolling individuals from multiple endemic countries. Objective: To assess the risk and predictors of major patient-important clinical outcomes in patients with clinical RHD. Design, Setting, and Participants: Multicenter, hospital-based, prospective observational study including 138 sites in 24 RHD-endemic LMICs. Main Outcomes and Measures: The primary outcome was all-cause mortality. Secondary outcomes were cause-specific mortality, heart failure (HF) hospitalization, stroke, recurrent rheumatic fever, and infective endocarditis. This study analyzed event rates by World Bank country income groups and determined the predictors of mortality using multivariable Cox models. Results: Between August 2016 and May 2022, a total of 13 696 patients were enrolled. The mean age was 43.2 years and 72% were women. Data on vital status were available for 12 967 participants (94.7%) at the end of follow-up. Over a median duration of 3.2 years (41 478 patient-years), 1943 patients died (15% overall; 4.7% per patient-year). Most deaths were due to vascular causes (1312 [67.5%]), mainly HF or sudden cardiac death. The number of patients undergoing valve surgery (604 [4.4%]) and HF hospitalization (2% per year) was low. Strokes were infrequent (0.6% per year) and recurrent rheumatic fever was rare. Markers of severe valve disease, such as congestive HF (HR, 1.58 [95% CI, 1.50-1.87]; P < .001), pulmonary hypertension (HR, 1.52 [95% CI, 1.37-1.69]; P < .001), and atrial fibrillation (HR, 1.30 [95% CI, 1.15-1.46]; P < .001) were associated with increased mortality. Treatment with surgery (HR, 0.23 [95% CI, 0.12-0.44]; P < .001) or valvuloplasty (HR, 0.24 [95% CI, 0.06-0.95]; P = .042) were associated with lower mortality. Higher country income level was associated with lower mortality after adjustment for patient-level factors. Conclusions and Relevance: Mortality in RHD is high and is correlated with the severity of valve disease. Valve surgery and valvuloplasty were associated with substantially lower mortality. Study findings suggest a greater need to improve access to surgical and interventional care, in addition to the current approaches focused on antibiotic prophylaxis and anticoagulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".