Longitudinal strain by speckle tracking and echocardiographic parameters as predictors of adverse cardiovascular outcomes in chronic Chagas cardiomyopathy
Bibliographic record
Abstract
To analyze the prognostic value of left ventricular global longitudinal strain (LV-GLS) and other echocardiographic parameters to predict adverse outcomes in chronic Chagas cardiomyopathy (CCM). Prospective cohort study conducted in 177 consecutive patients with different CCM stages. Transthoracic echocardiography measurements were obtained following the American Society of Echocardiography recommendations. By speckle-tracking echocardiography, LV-GLS was obtained from the apical three-chamber, apical two-chamber, and apical four-chamber views. The primary composite outcome (CO) was all-cause mortality, cardiac transplantation, and a left ventricular assist device implantation. After a median follow-up of 42.3 months (Q1 = 38.6; Q3 = 52.1), the CO incidence was 22.6% (95% CI 16.7-29.5%, n = 40). The median LV-GLS value was - 13.6% (Q1 = - 18.6%; Q3 = - 8.5%). LVEF, LV-GLS, and E/e' ratio with cut-off points of 40%, - 9, and 8.1, respectively, were the best independent CO predictors. We combined these three echocardiographic markers and evaluated the risk of CO according to the number of altered parameters, finding a significant increase in the risk across the groups. While in the group of patients in which all these three parameters were normal, only 3.2% had the CO; those with all three abnormal parameters had an incidence of 60%. We observed a potential incremental prognostic value of LV-GLS in the multivariate model of LVEF and E/e' ratio, as the AUC increased slightly from 0.76 to 0.79, nevertheless, this difference was not statistically significant (p = 0.066). LV-GLS is an important predictor of adverse cardiovascular events in CCM, providing a potential incremental prognostic value to LVEF and E/e' ratio when analyzed using optimal cut-off points, highlighting the potential utility of multimodal echocardiographic tools for predicting adverse outcomes in CCM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".