Lymphoceles: impact on kidney transplant recipients, graft, and healthcare system.
Bibliographic record
Abstract
INTRODUCTION: Following kidney transplantation, lymphoceles can impact patient and graft outcomes, while resulting in significant hospital resource utilization. We aimed to characterize the incidence, risk factors, outcomes, and clinical management of lymphoceles among kidney transplant recipients and review impact on health system utilization at a high-volume center. MATERIALS AND METHODS: We conducted a single-center, observational cohort study on adults transplanted between January 1, 2005 and December 31, 2017. Incidence, risk factors, and clinical outcomes were assessed using the Kaplan-Meier product-limit method, multivariable logistic regression model, and Cox proportional hazards model, respectively. RESULTS: Lymphoceles developed in 72 of 1881 patients (3.8%). Multivariate analysis demonstrated that a longer time on dialysis before transplant [HR 1.09 (95% CI: 1.02, 1.17)], laparoscopic donor nephrectomy [HR 2.31 (95% CI: 1.04, 5.12)], and depleting induction therapy [HR 0.39 (95% CI: 0.18, 0.87)] were significant risk factors for lymphocele development. Lymphoceles independently increased the likelihood of hospital readmission [HR 3.96 (95% CI: 2.99, 5.25)] but had no significant effect on the likelihood of graft failure or death with graft function. Of 72 cases, 44 received a radiological or surgical intervention. Fifteen of 44 lymphoceles required further intervention due to re-accumulation or complications. CONCLUSION: Patients with longer dialysis times, kidneys from laparoscopic donor nephrectomy, and depleting induction therapy were associated with an increased risk for developing symptomatic lymphoceles. Our center's treatment for symptomatic lymphoceles did not result in significant graft dysfunction, but significantly higher healthcare resource utilization was noted.
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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 | 0.000 |
| 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 | 0.000 |
| 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".