Contribution of Left Ventricular Diastolic Dysfunction to Survival and Breathlessness in Systemic Sclerosis–Associated Interstitial Lung Disease
Bibliographic record
Abstract
Objective To explore the effect of left ventricular (LV) diastolic dysfunction (LVDD) in systemic sclerosis (SSc)-associated interstitial lung disease (ILD), and to investigate SSc-specific associations and clinical correlates of LVDD. Methods There were 102 Australian Scleroderma Cohort Study participants with definite SSc and radiographic ILD included. Diastolic function was classified as normal, indeterminate, or abnormal according to 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging guidelines for assessment of LV diastolic function. Associations between clinical features and patient- and physician-reported dyspnea were evaluated using logistic regression. Survival analyses were performed using Kaplan-Meier survival estimates and Cox regression modeling. Results LVDD was identified in 26% of participants, whereas 19% had indeterminate and 55% had normal diastolic function. Those with ILD and LVDD had increased mortality (hazard ratio 2.4, 95% CI 1.0-5.7; P = 0.05). After adjusting for age and sex, those with ILD and LVDD were more likely to have severe dyspnea on the Borg Dyspnoea Scale (odds ratio [OR] 2.6, 95% CI 1.0-6.6; P = 0.05) and numerically more likely to record World Health Organization Function Class II or higher dyspnea (OR 4.2, 95% CI 0.9-20.0; P = 0.08). Older age (95% CI 1.0-6.4; P = 0.05), hypertension (OR 5.0, 95% CI 1.8-13.8; P < 0.01), and ischemic heart disease (OR 4.8, 95% CI 1.5-15.7; P < 0.01) were all associated with LVDD, as was proximal muscle atrophy (OR 5.0, 95% CI 1.9-13.6; P < 0.01) and multimorbidity (Charlson Comorbidity Index scores ≥ 4, OR 3.0, 95% CI 1.1-8.7; P = 0.04). Conclusion LVDD in SSc-ILD is more strongly associated with traditional LVDD risk factors than SSc-specific factors. LVDD is associated with worse dyspnea and survival in those with SSc-ILD.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".