Invasive Assessment of Coronary Artery Disease in Clonal Hematopoiesis of Indeterminate Potential
Bibliographic record
Abstract
BACKGROUND: Clonal hematopoiesis of indeterminate potential (CHIP) occurs due to acquired mutations in bone marrow progenitor cells. CHIP confers a 2-fold risk of atherosclerotic cardiovascular disease. However, there are limited data regarding specific cardiovascular phenotypes. The purpose of this study was to define the coronary artery disease phenotype of the CHIP population-based on coronary angiography. METHODS: We recruited 1142 patients from the Vanderbilt University Medical Center cardiac catheterization laboratory and performed DNA sequencing to determine CHIP status. Multivariable logistic regression models and proportional odds models were used to assess the association between CHIP status and angiography phenotypes. RESULTS: We found that 18.4% of patients undergoing coronary angiography had a CHIP mutation. Those with CHIP had a higher risk of having obstructive left main (odds ratio, 2.44 [95% CI, 1.40–4.27]; P =0.0018) and left anterior descending (odds ratio, 1.59 [1.12–2.24]; P =0.0092) coronary artery disease compared with non-CHIP carriers. We additionally found that a specific CHIP mutation, ten eleven translocase 2 (TET2 ), has a larger effect size on left main stenosis compared with other CHIP mutations. CONCLUSIONS: This is the first invasive assessment of coronary artery disease in CHIP and offers a description of a specific atherosclerotic phenotype in CHIP wherein there is an increased risk of obstructive left main and left anterior descending artery stenosis, especially among TET2 mutation carriers. This serves as a basis for understanding enhanced morbidity and mortality in CHIP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".