The internal mammary artery – use as a free graft in coronary artery bypass grafting – evidence, technical considerations and controversies
Bibliographic record
Abstract
BackgroundIn-situ internal mammary artery (IMA) grafting remains the gold standard in coronary artery bypass grafting (CABG), particularly for left anterior descending artery revascularisation. However, the role of free-IMA grafts-especially free right IMA (RIMA) and select cases of free left IMA (LIMA)-has expanded in response to anatomical and technical constraints. This narrative review synthesises current evidence on free-IMA use during CABG.MethodsA structured literature search was conducted using PubMed (1946-2025) and Embase (1974-2025), supplemented by Web of Science, Google Scholar, and thesis repositories. Studies were included if they reported outcomes related to free-IMA grafting, regardless of pump status or harvesting technique. Of 74 eligible studies, 9 chosen studies specifically reported free-RIMA outcomes and were analysed in detail.ResultsFree-RIMA grafting demonstrated excellent long-term patency (up to 96%) and favourable survival outcomes when used as composite or direct aorto-coronary grafts. Multi-arterial grafting (MAG) and total arterial grafting (TAG) strategies incorporating free-IMA conduits were associated with reduced major adverse cardiac events (MACE) and improved freedom from repeat revascularisation. Despite these benefits, uptake of free-IMA techniques remains low in Europe and North America, often limited by institutional preferences and operator experience.ConclusionCurrent evidence supports the selective use of free-IMA grafts in CABG, particularly when in-situ deployment is not feasible. Prospective studies are needed to validate long-term outcomes beyond 10 years, compare free-IMA with radial artery grafts, and define optimal arterial configurations for durable revascularisation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".