Bibliographic record
Abstract
Introduction: Perioperative hyperglycemia occurs frequently and is associated withperioperative morbidity and mortality following vascular surgery. We sought to examine currentglycemic surveillance and control patterns at the McGill University Health Centre (MUHC) andthe impact of perioperative hyperglycemia on outcomes following vascular surgery. We alsoexamined the literature on the use of one glycemic control intervention, intensive insulin therapy,for pre-existing studies performing this intervention on patients undergoing lower extremityvascular surgery.Methods: Current glycemic control patterns at the MUHC were evaluated by retrospective datacollection on patients who underwent open infrainguinal vascular surgery. Patient baselinecharacteristics, intra-operative factors, efficacy of glycemic control, and post-operative outcomeswere assessed using univariate and multivariate analysis.A systematic review was then performed to determine the evidence for the use of intensiveinsulin therapy to reduce the risk of complications following open lower extremity vascularsurgery.Results: 38.9% of patients experienced perioperative hyperglycemia defined as glucose ³ 10mmol/L during their hospital admission. Only 3.9% of patients within the cohort underwent anyintraoperative glycemic surveillance, despite the fact that 43.9% of patients were diabetic. 16.8%patients remained hyperglycemic for at least 40% of their measurements during theirhospitalization. Multivariable logistic regression including the covariates of age, sex,hypertension, smoking status, diabetic status, presence of chronic kidney disease, dialysis,Rutherford stage, coronary artery disease and perioperative hyperglycemia demonstrated asignificant relationship between perioperative hyperglycemia and 30-day mortality (OR 25.00,595% CI 2.469 – 250.00, p = 0.006), major adverse cardiac events (OR 2.08, 95% CI 1.008 -4.292, p = 0.048), major adverse limb events (OR 2.24, 95% CI 1.020 – 4.950, p = 0.045), acutekidney injury (OR 7.58, 95% CI 3.021 – 19.231, p <0.001), reintervention (OR 2.06, 95% CI1.117 - 3.802, p = 0.021) and intensive care unit admission (OR 3.38, 95% CI 1.225 – 9.345, p =0.019).A systematic literature review identified two studies using intensive insulin therapy during andimmediately following vascular surgery. Protocols for insulin infusion varied significantly andmany patients did not achieve normoglycemia. Studies were also underpowered.Conclusion: Perioperative glycemic monitoring and control is sub-optimal following lowerextremity revascularization and is associated with significant morbidity and mortality in ourcohort. Pre-existing literature on the use of intensive insulin therapy in the perioperative periodin patients undergoing vascular surgery are underpowered and too variable to allow conclusionson the safety and efficacy of intensive insulin therapy to be drawn.More consistent monitoring and the use of more effective glycemic control protocols at theMcGill University Health Centre, such as the use of an intensive insulin protocol, might providea yet unexplored avenue for reducing patient morbidity and mortality following lower extremityopen vascular surgery. Further studies on the use of intensive insulin therapy in patientsundergoing lower extremity revascularization or major amputation are needed to properly assessthis intervention
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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".