Progression of Left Ventricular Myocardial Dysfunction in Systemic Sclerosis: A Speckle-tracking Strain Echocardiography Study
Bibliographic record
Abstract
OBJECTIVE: Cardiac involvement is a main cause of mortality in systemic sclerosis (SSc). Its detection remains challenging using conventional echocardiography and little is known about its potential progression. This study assessed changes in cardiac performance over time in a prospective cohort of patients with SSc, including echocardiographic speckle-tracking strain analysis. METHODS: The study included 234 patients with SSc [196 women, age 52 ± 14 yrs, 165 limited SSc, time since diagnosis 5.2 yrs, interquartile range (IQR) 2.9-11.3]. Clinical variables, laboratory tests, pulmonary function tests, and echocardiographic measures were recorded at baseline and followup (median 2.3 yrs, IQR 1.3-3.9). Additionally, left ventricular (LV) systolic function was assessed with global longitudinal strain (GLS) by echocardiographic speckle-tracking analysis. RESULTS: At followup, GLS had significantly worsened (-21% ± 2 vs -19% ± 2, p < 0.001) while LV ejection fraction had not changed (62% ± 7 vs 61% ± 8, p = 0.124). In particular, 39 patients showed a significant deterioration of GLS as defined by a ≥ 15% decrease, which was accompanied by a concomitant worsening of proximal muscle weakness, lung fibrosis, renal function, LV diastolic function, and right ventricular systolic function. Baseline variables associated with ≥ 15% deterioration in GLS were proximal muscle weakness (OR 3.437, 95% CI 1.13-10.43, p = 0.020), decreased DLCO (OR 3.621, 95% CI 1.25-10.51, p = 0.049), and LV diastolic dysfunction (OR 2.378, 95% CI 1.07-5.27, p = 0.033). CONCLUSION: In patients with SSc, progression of LV systolic dysfunction was demonstrated by GLS but not by LV ejection fraction. Proximal muscle weakness, DLCO, and LV diastolic dysfunction may identify patients at risk for progressive LV systolic dysfunction and in need of closer cardiac monitoring.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".