Associations of Pre-Transplant Prescription Narcotic Use with Clinical Complications after Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: The impact of narcotic use before kidney transplantation on post-transplant clinical outcomes is not well described. METHODS: We examined integrated national transplant registry, pharmacy records, and Medicare billing claims to follow 16,322 kidney transplant recipients, of whom 28.3% filled a narcotic prescription in the year before transplantation. Opioid analgesic fills were normalized to morphine equivalents (ME) and expressed as mg/kg exposures (approximate quartiles: 0.1-1.7, 1.8-5.4, 5.5-23.7, and ≥ 23.8 mg/kg, respectively). Post-transplant cardiovascular, respiratory, neurological, accidents, substance abuse, and noncompliance events were identified using diagnosis codes on Medicare billing claims. Adjusted associations of ME level with post-transplant complications were quantified by multivariate Cox regression. RESULTS: The incidence of complications at 3 years post-transplant among those with the highest pre-transplant ME exposure compared to no use included: ventricular arrhythmias, 1.1 vs. 0.2% (p < 0.001); cardiac arrest, 4.7 vs. 2.7% (p < 0.05); hypotension, 14 vs. 8% (p < 0.0001); hypercapnia, 1.6 vs. 0.9% (p < 0.05); mental status changes, 5.3 vs. 2.7% (p < 0.001); drug abuse/dependence, 7.0 vs. 1.7% (p < 0.0001); alcohol abuse, 1.8 vs. 0.6% (p = 0.0001); accidents, 0.9 vs. 0.3% (p < 0.05); and noncompliance, 3.5 vs. 2.3% (p < 0.05). In multivariate analyses, transplant recipients with the highest level of pre-transplant narcotic use had approximately 2 to 4 times the risks of post-transplant ventricular arrhythmias, mental status changes, drug abuse, alcohol abuse, and accidents compared with non-users, and 35-45% higher risks of cardiac arrest and hypotension. CONCLUSION: Although associations may reflect underlying conditions or behaviors, high-level prescription narcotic use before kidney transplantation predicts increased risk of clinical complications after transplantation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".