Effect of Preadmission Diabetes Intervention (PREHAB) on Postoperative Patient Outcomes in Cardiac Surgery
Bibliographic record
Abstract
People living with diabetes are at an increased risk of developing heart disease. In patients undergoing coronary artery bypass grafting (CABG), a preexisting diagnosis of diabetes has been identified as a risk factor for postoperative sternal wound infections and prolonged hospital stay. At the University of Ottawa Heart Institute (UOHI) the wait time for elective cardiac surgery is four months. There is literature that supports the use of cardiac prehabilitation to improve postoperative outcomes such as length of stay, functional capacity and perioperative complications. However, there is no published evidence on whether pre-admission diabetes interventions lead to the same results. The aim of this study is to introduce a novel prehabilitation program for diabetes. Target Population: All elective cardiac surgery patient screened for diabetes with an HBA1C of greater than 6.5%. Primary Outcomes: The primary outcome is improvement in glycemic control from screening to time of elective cardiac surgery in those patients who completed prehabilitation program. Secondary Outcomes: The secondary outcomes measured are effect of prehabilitation on length of stay and surgical site infections. Prehabilitation Methodology: In those who have an elevated HBA1C a screening phone call by cardiac rehabilitation nurse. A two hour prehabilitation session that cover topics such as nutrition, exercise and mental health. Group medical appointment led by a diabetes nurse. Results: 24 patients have completed the full prehabilitation program. The mean HBA1C from screening to time of surgery dropped from 7.64% to 7.3% which was not statistically significant. One surgical site infection developed in this group compared to 3 infections in the group that did not attend. Conclusions: Wait times for elective cardiac surgery is an opportune time to intervene and optimize patients’ medical fitness for surgery and improving patient self-management of chronic medical conditions like diabetes. Disclosure A. Arnaout: Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc., Medtronic. Advisory Panel; Self; Novo Nordisk Inc.. Research Support; Self; Allergan, Sanofi. S. Goge: Speaker's Bureau; Self; AstraZeneca.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".