Never events and hospital-acquired conditions after kidney transplant
Bibliographic record
Abstract
INTRODUCTION: Never events (NE) and hospital-acquired conditions (HAC) after surgery have been designated as quality metrics in health-care by the Centres for Medicare and Medicaid Services (CMS). METHODS: The Nationwide Inpatient Sample (NIS) database 2002-2012 was used to identify patientswho underwent kidney transplant. Multivariate analysis using logistic regression was used to identify outcomes and risk factors of HAC and NE after transplantation; however, we were limited by using a retrospective database missing some important variables specified for the kidney transplant, such as some operative factors, donor factors, and cold and warm ischemia times. RESULTS: Among 35 058 patients who underwent kidney transplant, there were 11 NEs, all of which were due to retained foreign bodies. Among HAC after surgery, falling was the most common (44.9%), followed by poor glycemic control (21.7%), vascular catheter-associated infection (21%), and catheter-associated urinary tract infection (8%). HAC and NE after surgery lead to a significant increase in mortality (adjusted odds ratio [AOR] 2.49; p=0.04), hospitalization length (13 vs. 7 days; p<0.01), and total hospital charges ($231 801 vs. $146 717; p<0.01). A significantly higher risk of HAC or NE was seen for patients who had more loss of function before surgey (AOR 3.25; p<0.01) and patients expected to have higher postoperative mortality before operation (AOR 1.62; p=0.03). CONCLUSIONS: Despite the limitations of the study, we found HAC and NE significantly increase mortality, hospitalization length, and total hospital charges of kidney transplant patients. Quality improvement initiatives should target HAC and NE in order to successfully reduce or prevent these events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".