Reducing hyperglycaemia post-kidney and liver transplant: a quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: In-hospital glycaemic management can reduce post-transplant morbidity, but is not always part of transplant care. OBJECTIVE: We aimed to reduce the mean number of postoperative days in hyperglycaemia (≥2 blood glucose >12 mmol/L in 24 hours) in kidney and liver transplant recipients by 30%. We also aimed to reduce the mean number of days between transplant admission to endocrine consult by 2.0 days. DESIGN, SETTING, PARTICIPANTS: We conducted a quality improvement project in liver and kidney transplant recipients admitted to an academic transplant unit in Canada between 1 March 2019 and 1 May 2021. INTERVENTION: We developed a bedside algorithm to monitor post-transplant capillary blood glucose; the algorithm also included thresholds for nursing-initiated inpatient endocrinology consultation. MAIN OUTCOME AND MEASURES: We examined outcome (postoperative days in hyperglycaemia, days to inpatient endocrine consultation), process (nursing documentation of postoperative blood sugars) and balancing measures (nursing workload, postoperative days in hypoglycaemia) following implementation of our algorithm. We used Plan-Do-See-Act cycles to study three iterations of our algorithm, and used box plots to present outcomes before and after algorithm implementation. RESULTS: In the pre-intervention period, 21 transplant recipients spent a mean of 4.1 (SD 2.4) postoperative days in hyperglycaemia before endocrine consultation. The mean number of days between hospital admission to endocrine consult was 10.7 (SD 13.0) days.In the post-intervention period, we observed a 62% reduction in postoperative days in hyperglycaemia. The mean number of days between admission and endocrine consult was reduced by 6.3 days (59% reduction). CONCLUSIONS: Implementation of a simple, bedside algorithm for postoperative glucose monitoring and detection of hyperglycaemia in transplant patients, reduced the mean number of postoperative days in hyperglycaemia and time to inpatient endocrine consultation. Our algorithm continues to be used in our academic transplant unit.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".