Abstract 14372: Case Series: Non-Lipid Plaque Formation as the Mechanism for Ponatinib-Induced Vascular Toxicity Using Operator-Assisted Virtual Histology Optical Coherence Tomography
Bibliographic record
Abstract
Introduction: The third-generation tyrosine kinase inhibitor ponatinib approved for treating chronic myeloid leukemia (CML) rendered drug resistant with a threonine-315-isoleucine (T315I) mutation is associated with increased incidence of vascular adverse events (VAEs). This case series sought to elucidate the mechanism of these VAEs. Methods: Two patients with CML and two patients with Philadelphia chromosome positive acute lymphocytic leukemia (Ph+ ALL) on at least one year of ponatinib therapy were imaged with optical coherence tomography (OCT) after presenting with angina. An additional four patients from an institutional OCT database were matched across clinical parameters (age, sex, diabetes, prior coronary artery disease (CAD) history, prior congestive heart failure (CHF) history, hypertension, and family history of CAD) to serve as controls. Plaque composition was assessed using operator-assisted virtual histology OCT (vOCT). Results: Clinical presentations included 2 cases of NSTEMI, 1 case of unstable angina, and 1 case of stable angina pectoris among the 4 ponatinib patients. Atherosclerosis was composed primarily of calcium and fibrous tissue with minimal lipid presence (0 ± 0°) compared to the matched controls. The matched controls had a mean non-zero lipid content of 48.43 ± 18.36°, ( P < 0.05) (Table 1). Three of the four patients had cardiovascular risk factors. Conclusions: Ponatinib causes VAEs through non-lipid plaque formation driving a symptomatology consistent with an oxygen supply-demand mismatch. This mechanism is distinct from traditional pathways of excess lipid and plaque instability causing coronary artery disease and merits further investigation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".