Trastuzumab-Induced Cardiotoxicity: Testing a Clinical Risk Score in a Real-World Cardio-Oncology Population
Bibliographic record
Abstract
BACKGROUND: Trastuzumab has improved survival for women with her2-positive breast cancer, but its use is associated with an increased risk of cardiotoxicity. With increased survivorship, the long-term effects of cancer treatment are an important consideration for clinicians and patients. We reviewed the current literature on predicting trastuzumab-related cardiotoxicity and tested a clinical risk score (crs) in a real-world breast cancer population to assess its utility in predicting permanent cardiotoxicity. METHODS: In this retrospective exploratory cohort study of breast cancer patients referred to a cardio-oncology clinic at a tertiary care centre between October 2008 and August 2014, a crs was calculated for each patient, and a sensitivity analysis was performed. RESULTS: Of the 143 patients included in the study, 62 (43%) experienced a cardiac event, and of those 62 patients, 43 (69%) experienced full recovery of cardiac function. In applying the crs, 119 patients (83%) would be considered at low risk, 14 (10%) at moderate risk, and 10 (7%) at high risk to develop heart failure or cardiomyopathy. When applied to the study population, the high-risk cut-off score had a sensitivity of 0.13 [95% confidence interval (ci): 0.08 to 0.20] and a specificity of 0.94 (95% ci: 0.87 to 0.97). The positive predictive value was 0.07 (95% ci: 0.03 to 0.13), and the negative predictive value was 0.93 (95% ci: 0.87 to 0.96). CONCLUSIONS: The crs demonstrated good specificity and negative predictive value for the development of permanent cardiotoxicity in a real-world population of breast cancer patients, suggesting that intensive cardiac monitoring might not be warranted in low-risk patients, but that high-risk patients might benefit from early referral to cardio-oncology for optimization. Further study using the crs in a larger breast cancer population is warranted to identify patients at low risk of long-term trastuzumab-related cardiotoxicity.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".