Evaluation of Cardiotoxicity Using Blood Biomarkers in Breast Cancer and Lymphoma Patients Undergoing Curative Treatment
Bibliographic record
Abstract
Objective: To evaluate whether abnormal concentrations in cardiac and inflammatory biomarkers could predict reductions in left ventricular ejection fraction (LVEF) for cancer patients undergoing curative treatment. Materials and Methods: Longitudinal testing was performed for high-sensitivity cardiac troponin I (hs-cTnI), N-terminal pro-B-type natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP) and C-reactive protein (CRP) in HER2+ breast cancer (BC) patients receiving adjuvant trastuzumab treatment (n=22) and in lymphoma patients treated with radiotherapy (n=4). Sex-specific and overall upper limit of normal (ULN) cutoffs were used to identify abnormal results with a reduction in LVEF (<50% and decrease of ≥10% from baseline) indicative of cardiotoxicity. A secondary analysis was performed on the BC patients with normal LVEFs (n=12 with baseline prior to chemotherapy through to 6-months on trastuzumab) with 15 blood collections spaced between 6- and 254-days post-baseline LVEF measurement. Results: A majority of the BC patients had evidence of myocardial injury (hs-cTnI >female ULN=90%) or myocardial dysfunction (NT-proBNP >overall ULN=91%) at any timepoint with fewer patients having abnormal CRP or H-FABP concentrations (H-FABP >ULN=14%; CRP >ULN=45%). Myocardial injury and dysfunction were most evident during the first two cycles of trastuzumab treatment, with myocardial injury also evident during this early timeframe in the female lymphoma patients (3 with hs-cTnI >ULN). In the 12 patients who completed trastuzumab with normal LVEFs (median=60% at 6-months), myocardial injury (hs-cTnI >ULN) and dysfunction (NT-proBNP >ULN) was evident in >50% of patients. Four of the 22 patients did develop cardiotoxicity, but there was no difference in biomarker concentrations between patients with or without cardiotoxicity. Conclusion: The use of the recommended ULN cutoffs identified myocardial injury and dysfunction in a majority of cancer patients in this setting. Biomarker assessments did not relate to cardiac functional imaging studies. Future studies are warranted to assess different cutoffs or biomarker combinations for predicting 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 | 0.000 | 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".