Coronary artery calcium score on chest computed tomography in liver transplant candidates: A retrospective study of diagnostic performance and risk stratification
Bibliographic record
Abstract
• Qualitative CAC scoring on chest CT is feasible in liver transplant candidates. • CAC 0–1 strongly predicts absence of significant coronary artery disease. • Qualitative CAC shows high sensitivity (91%) and specificity (64%) vs angiography. • MACE occurred even with CAC 0–1, showing multifactorial perioperative risk. • Qualitative CAC may refine pre-LT cardiac risk and reduce invasive CAG use. Cardiac risk assessment in liver transplantation (LT) candidates is challenging as non-invasive tests have low sensitivity for detecting significant coronary artery disease (CAD), often necessitating coronary angiography (CAG). This study examined less invasive qualitative coronary artery calcium (CAC) scoring, major adverse cardiac events (MACE), coronary angiography findings, and health system outcomes after LT. We conducted a retrospective cohort study of adult LT recipients. Two radiologists independently classified CAC on chest CT as absent (0), mild (1), moderate (2), or severe (3). The primary endpoint was MACE (myocardial infarction, cardiac arrest, cardiac death). Secondary endpoints included CAD severity on CAG, and health system outcomes. Logistic regression and log-transformed linear regression were used. Among 982 LT patients, 477 underwent chest CT and 394 CAG. Median age was 61 years; MELDNa 15. CAC distribution was CAC0 185 (38.8%), CAC1 110 (23.1%), CAC2 96 (20.1%), CAC3 86 (18.0%). CAC correlated with CAG (sensitivity 91%, specificity 64%). MACE occurred in 8 (1.7%). CAC severity was not associated with MACE or health system outcomes. In LT candidates, low CAC indicated low CAD risk. However, MACE occurred without CAD, underscoring multifactorial perioperative cardiac risk and the potential value of chest CT in comprehensive pre-transplant evaluation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".