Meta-analysis and predictors of cancer therapy-related right ventricular dysfunction: insights from FAC measurements
Bibliographic record
Abstract
Abstract Introduction Cancer therapy-related right ventricular toxicity (CTR-RVT) is an emerging concept referring to structural and functional changes in the right ventricle (RV) that may develop as a consequence of chemotherapy. Anthracyclines, widely used in the treatment of breast cancer, have been implicated in myocardial injury, however, the relationship between cumulative drug exposure and the severity of RV dysfunction has not been clearly established. Current evidence suggests that CTR-RVT may manifest as reduced RV systolic function, alterations in RV-pulmonary coupling, and an increased risk of pulmonary hypertension. However, there is no consensus regarding standardized diagnostic criteria, the threshold dose for toxicity, or the clinical significance of these changes. Purpose This study aimed to systematically review and meta-analyze RV echocardiographic parameters in patients undergoing chemotherapy for breast cancer subgroup, with an emphasis on identifying clinical predictors of RV dysfunction through mixed-effects meta-regression. Methods A systematic search was conducted in PubMed, Embase, and Cochrane for studies evaluating RV parameters during cancer therapy. Statistical analyses were performed using R statistical software. Pooled mean differences (MD) were estimated using a random-effects model, with a significance level of 0.05. A mixed-effects meta-regression model, employing the restricted maximum likelihood (REML) estimator, was used to assess the role of age, anthracycline dose per kilogram, and follow-up duration as effect modifiers of RV dysfunction. Results In the breast cancer subgroup (Fig. 1), 12 studies were analyzed, comprising a total of 626 patients. The pooled MD in fractional area change (FAC) before and after cancer therapy was -3.33 (95% CI: -5.06 to -1.60), indicating a significant reduction in RV function following treatment with a high heterogeneity (I2=83%; p<0.0001). However, meta-regression analysis identified anthracycline dose per kilogram (β=−0.0349, SE = 0.0094, p=0.0002), follow-up duration (β=0.5743, SE = 0.2293, p=0.0122), and patient age (β=−0.3365, SE = 0.1457, p=0.0209) as significant effect modifiers (QM =33.6338, p <0.0001). These moderators accounted for a high proportion of the heterogeneity (R2 = 99%), suggesting that they effectively explain most of the variability in the model (as demonstrated by the simulation tool in Fig. 2), with minimal residual heterogeneity (τ2 = 0.0865, I2 = 6.83%). Conclusion The meta-regression analysis conducted demonstrated that patients’ age, treatment duration, and anthracycline dose are statistically significant effect modifiers, accounting for most of the heterogeneity observed in the present study. Further research is necessary to elucidate the mechanisms underlying CTR-RVT, integrate early markers with clinical risk factors, and refine predictive models to address the cardiotoxic effects of chemotherapy.Forest plot of FAC in Cancer Therapy FAC Calculator
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".