Risk Factors for Preoperative Hyperglycemia in Surgical Patients with Diabetes: A Case–Control Study
Bibliographic record
Abstract
Background: Patients with diabetes are more likely to undergo a surgical procedure than the rest of the population, and it is well established that preoperative hyperglycemia is associated with adverse surgical outcomes. However, it is currently unknown what factors increase the odds of preoperative hyperglycemia in people with diabetes. Objective: To identify patient characteristics that increase the risk of preoperative hyperglycemia. Methods: This retrospective case–control study compared 100 patients with preoperative hyperglycemia on admission for elective surgery at South Health Campus in Calgary, Alberta (blood glucose > 10.9 mmol/L) with 200 controls who did not have preoperative hyperglycemia on admission for elective surgery (blood glucose ≤ 10.9 mmol/L). Multivariate logistic regression was used to identify risk factors for preoperative hyperglycemia. Results: In the univariate analysis, age, number of comorbidities, increasing glycated hemoglobin (HbA1c), type of diabetes, type of procedure, and diabetes medications (non-insulin, insulin, both, or none) were associated with increased odds of preoperative hyperglycemia (p < 0.05). However, in the adjusted analysis, only increasing HbA1c (odds ratio [OR] 1.69, 95% confidence interval [CI] 1.36–2.12) and type 1 diabetes (OR 4.24, 95% CI 1.11–16.21, relative to type 2 diabetes) were associated with preoperative hyperglycemia. Conclusions: These results can help clinicians to identify patients who may be at increased risk of hyperglycemia before an elective procedure. They also allow for treatment of those who would benefit most from additional guidance with regard to preoperative glucose management. RÉSUMÉ Contexte : Les patients diabétiques sont plus susceptibles que le reste de la population de subir une intervention chirurgicale, et il est bien connu que l’hyperglycémie préopératoire est associée à des résultats chirurgicaux indésirables. Cependant, on ignore actuellement quels facteurs augmentent ce risque chez les personnes atteintes de diabète. Objectif : Déterminer les caractéristiques des patients qui augmentent le risque d’hyperglycémie préopératoire. Méthodes : Cette étude cas-témoins rétrospective a comparé 100 patients présentant une hyperglycémie préopératoire à l’admission pour une intervention chirurgicale non urgente au South Health Campus de Calgary, en Alberta (glycémie > 10,9 mmol/L) avec 200 témoins qui n’en présentaient pas (glycémie ≤ 10,9 mmol/L). La détermination des facteurs de risque d’hyperglycémie préopératoire s’est faite par régression logistique multivariée. Résultats : Dans l’analyse univariée, l’âge, le nombre de comorbidités, l’augmentation du taux d’hémoglobine glyquée (HbA1c), le type de diabète, le type d’intervention et les médicaments contre le diabète (non-insuline, insuline, les deux ou aucun) étaient associés à un risque accru d’hyperglycémie préopératoire (p < 0,05). Cependant, dans l’analyse ajustée, seuls l’augmentation de l’HbA1c (rapport de cotes [RC] 1,69; intervalle de confiance [IC] à 95 % 1,36-2,12) et le diabète de type 1 (RC 4,24; IC à 95 % 1,11-16,21, par rapport au diabète de type 2) étaient associés à une hyperglycémie préopératoire. Conclusions : Ces résultats peuvent aider les cliniciens à repérer les patients qui pourraient présenter un plus grand risque d’hyperglycémie avant une intervention non urgente. Ils permettent également de traiter ceux qui bénéficieraient le plus de conseils supplémentaires en matière de gestion préopératoire de la glycémie.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".