Surgical Complications in Kidney Transplant Recipients in the Modern Era
Bibliographic record
Abstract
Background Kidney transplantation increases the quality of life of patients with end stage renal disease (ESRD). However, surgical complications (SC) of transplantation pose risks to clinical outcomes and increase healthcare burden. Literature on the topic is fragmented, with no studies examining multiple types of SC. We examined trends in perigraft collections, vascular, urological and wound-related SC within one month of kidney transplant, and their associated clinical outcomes in a Canadian cohort of kidney transplant recipients. Methodology We conducted a single centre, retrospective cohort study examining adult patients (≥18 years) who received a kidney transplant between January 1st, 2005 and December 31st, 2015 with a one year follow up period (n = 1,303). All non-kidney or simultaneous multi-organ transplants were excluded. Univariable and multivariable Cox proportional hazard models were fitted to analyze the relationship between SC (exposure levels: 0, 1, and >1 SC within 1 month of transplant) and clinical outcomes. Results The incidence rate of SC within 1 year post-transplant was 10.7 (95% C.I.: 10.0, 11.9). The most common SC were perigraft collections, of which hematomas were most prevalent. Risk factors associated with having SC included older recipient age (p= 0.02), deceased donor (p= 0.02), and delayed graft function (p< 0.001). Univariable analyses showed significant relationships between having more than 1 SC within a month of transplant and increased risk for readmission (p= 0.04) as well as reduced estimated glomerular filtration rate (eGFR) (p= 0.03) at 1 year post-transplant. Multivariable analyses resulted in no clear significant relationships between SC and clinical outcomes including graft and patient survival. Conclusions The incidence of SC at TGH is comparable to that reported in the literature, which ranges from 3 to 20%. Although common, SC were not independently associated with adverse graft and patient outcomes. The impact of SC on morbidity outcomes requires further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".