Incidence Rate of Post-Kidney Transplant Infection: A Retrospective Cohort Study Examining Infection Rates at a Large Canadian Multicenter Tertiary-Care Facility
Bibliographic record
Abstract
BACKGROUND: Reducing post-operative infections among kidney transplant patients is critical to improve long-term outcomes. With shifting disease demographics and implementation of new transplantation protocols, frequent evaluation of infection rate and type is necessary. OBJECTIVE: Our objectives were to assess the incidence and types of post-operative infections in kidney transplant recipients at a large tertiary-care facility and determine sample sizes needed for future intervention trials. DESIGN: Retrospective cohort study. SETTING: The Ottawa Hospital, Ottawa, Ontario. PATIENTS: Adult kidney transplant patients, N = 142. MEASUREMENTS: Demographic data, transplant protocol, infections up to 2 years following transplantation. METHODS: Infections within 2 years following transplantation in all kidney transplant recipients between January 2011 and December 2012 were reviewed. Sample sizes were determined using all-cause infection rates and infection-free survival data. RESULTS: Of 142 patients, 44 (31.0%) had at least one infection. The incidence of infection was 36.2 per 100 patient-years by 2 years post-transplant. A total of 32 (22.5%) patients had 56 infection-related hospitalizations with 73.2% occurring in the first year. In the first 2 years, urinary tract infections had the highest incidence (18.1 per 100 patient-years) followed by skin (3.9 per 100 patient-years), cytomegalovirus (3.9 per 100 patient-years), and bacteremia (3.9 per 100 patient-years). Results indicate that 206 patients per study arm would be needed to show a 30% reduction in the 2-year incidence of infection post-transplantation. LIMITATIONS: Infection rates may be slightly underestimated due to the relatively short 2-year follow-up; however, the highest infection-risk period was captured within this time frame. CONCLUSIONS: Infections post-kidney transplant are still common, particularly urinary tract infections. They are associated with significant morbidity and hospitalization. Given the feasible sample sizes calculated in this study, intervention trials are indicated to further reduce infection rates within the first 2 years post-kidney 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".