Sternum-Sparing Left Ventricular Assist Device Insertion Reduces Perioperative Transfusions and Blood Loss: A Single-Centre Canadian Experience
Bibliographic record
Abstract
Background: Left ventricular assist devices (LVADs) improve survival and quality of life, as either destination therapy or a bridge to transplantation. Although less-invasive hemisternotomy approaches for LVAD implantation are well studied, only a paucity of data is available in the literature on sternum-sparing bilateral minithoracotomy (BMT). Our centre has one of Canada's most extensive experiences with the BMT approach. Herein, we compared LVAD implantation via BMT with patients who received full median sternotomy or hemisternotomy. Methods: A single-centre retrospective review of data from Foothills Medical Centre (Calgary, Canada) was performed. Patients underwent LVAD insertion from 2012 to 2019, receiving either BMT (n = 11) or sternotomy (full median sternotomy or upper hemisternotomy with left minithoracotomy; n = 38). Intraoperative and early postoperative outcomes were assessed. Results: Patients who received BMT had significantly fewer transfusions of red blood cells, fresh frozen plasma, and platelets. The BMT group had lower chest-tube output in the first 12 hours. No significant differences occurred in ventilation time, intensive care unit length of stay, mortality, stroke, or reoperation for bleeding. Conclusions: Outcomes suggest that sternum-sparing LVAD implantation is a feasible alternative to sternotomy, leading to less postoperative blood loss and transfusion in the early postoperative period. Less transfusion is particularly valuable in this patient population, to reduce antigen-related sensitization prior to transplantation. Additional study is needed to assess potential benefits related to right heart function, postoperative mobility, and re-entry for transplantation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".