Macrotroponin interference and association with cardiotoxicity in patients receiving cardiotoxic breast cancer therapy: a pilot study
Bibliographic record
Abstract
BACKGROUND: Cancer therapy-related cardiac dysfunction (CTRCD) is an important adverse effect in patients receiving potential cardiotoxic cancer therapies. Interpretation of cardiac troponin results can be affected by presence of macrotroponin, which can complicate CTRCD assessment. We aimed to assess whether macrotroponin is detectable in women with ERBB2 + breast cancer receiving sequential therapy with anthracyclines and trastuzumab. METHODS: A total of 20 serum samples from 12 ERBB2 + breast cancer patients (median age: 55 years, range: 30-69 years) who exhibited a significant increase in high-sensitivity cardiac troponin I (hs-cTnI) from baseline to post-anthracycline (~ 2 months after therapy initiation) and/or 3-months into trastuzumab therapy (~ 5 months after therapy initiation) and/or who had at least one hs-cTnI value above the female-specific 99th percentile (hs-cTnI > 16 ng/L) and had available banked blood for analysis were included in this pilot study. Samples were analyzed using the Abbott STAT High-Sensitive Troponin-I and Roche Elecsys Troponin T hs STAT assays. Macrotroponin was detected by treating the sample with protein G and re-measuring hs-cTn. Macrotroponin presence was defined as a hs-cTnI or hs-cTnT recovery of < 40% or 85%, respectively. RESULTS: Macrotroponin was not identified after anthracycline treatment but was present in four patients 3-months into trastuzumab therapy, two of which had hs-cTnI concentrations above the 99th percentile. None of these patients exhibited a significant reduction in LVEF and/or GLS despite having significant elevations in hs-cTnI. CONCLUSIONS: Clinicians should be cautious of benign hs-cTn elevations resulting from macrotroponin presence, as it can complicate CTRCD assessment.
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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 | 0.002 |
| 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".