Increased Risk of Valvular Heart Disease in Systemic Sclerosis: An Underrecognized Cardiac Complication
Bibliographic record
Abstract
OBJECTIVE: Cardiac involvement is a poor prognostic marker in systemic sclerosis (SSc). While diastolic dysfunction, myocardial fibrosis, and arrhythmias are traditionally considered features of primary cardiac involvement in SSc, the incidence of valvular heart disease (VHD) is not well reported. Our objective was to examine the prevalence of VHD at the time of SSc diagnosis and incidence of VHD during follow-up compared to non-SSc subjects. METHODS: Medical records of patients with suspicion of SSc were reviewed to identify incident cases. SSc subjects were matched 1:2 by age and sex to non-SSc subjects. RESULTS: = 0.004) was identified. During follow-up, 18 SSc and 12 non-SSc patients developed moderate/severe VHD. The cumulative incidence of VHD at 10 years after SSc incidence/index was 17.9% (95% CI 10.7-29.9) in patients with SSc compared with 2.3% (95% CI 0.7-7.0) in non-SSc subjects (HR 4.23, 95% CI 2.03-8.83). Coronary artery disease was the only significant risk factor for VHD. CONCLUSION: Patients with SSc have a 4-fold increase in the prevalence of moderate/severe VHD at diagnosis compared to non-SSc patients. They also have a 4-fold increased risk of developing moderate/severe VHD after diagnosis of SSc. Aortic stenosis and mitral regurgitation have a much higher prevalence in patients with SSc, besides secondary tricuspid regurgitation. Underlying mechanisms for this association require further elucidation.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".