Baseline left atrial reservoir strain provides independent and incremental prognostic value in predicting chemotherapy related cardiotoxicity
Bibliographic record
Abstract
Abstract Background Currently, few baseline imaging biomarkers can predict chemotherapy induced cardiotoxicity (CTx). The early identification of CTx is critically important as it determines the prospect of LV function recovery (1). Purpose We aimed to determine if baseline left atrial (LA) reservoir strain (LAS) is predictive of CTx. Methods We performed retrospective analysis of the SUCCOUR study, an international multicentre randomized controlled trial. CTx was defined as an asymptomatic drop of >10% in left ventricular ejection fraction (LVEF) compared to baseline to <55%. LAS analysis was performed using semi-automated speckling tracking of the LA in the four and two chamber views using EchoPAC. Analysis was performed at one year follow up. Cox proportional hazard analysis, c-statistics and Akaike information criterion (AIC) statistical analysis was performed. Results After excluding 78 with inadequate images, 229 were included in the analysis. Most participants were female (n=215, 94%), with a mean age of 54±12 years. Median follow up was 1.02 years (IQR 0.98–1.07). A smoking history was the commonest cardiac risk factor (n=68, 30%). Breast cancer was the main cancer type (n=205, 90%). The mean baseline 3D LVEF, LV global longitudinal strain (GLS), LAS, and LA volume index (LAVI) were 61±4%, 20.6±2.5%, 28.6±7.9%, and 28±9ml/m2, respectively. Participants that developed CTx had a lower LAS at baseline compared to those that did not (25.5±7.4% vs 29.0±7.9% respectively; p=0.027). At 1-year follow-up, 29 patients (13%) developed CTx. On univariable Cox proportional hazard analysis, higher baseline LAS was associated with a lower risk of CTx (HR 0.95, 95% CI 0.90–0.99; p=0.026) but other parameters including baseline GLS was not (p=0.17). In nested Cox models (Figure 1), adding LAS significantly improved the model's predictive accuracy for CTx compared to other clinical and imaging parameters. When LAS was added to the same models it showed improvements in the c-statistics and AIC score (Figure 1). Conclusion Baseline LAS is an independent and incremental predictor of CTx. LAS could be used for baseline risk stratification in oncology patients who receive potentially cardiotoxic chemotherapy. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): GE Healthcare
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".