Incidence and Risk Factors for Leukopenia in Kidney Transplant Recipients Receiving Valganciclovir for Cytomegalovirus Prophylaxis
Bibliographic record
Abstract
CONTEXT: Valganciclovir is used not only for cytomegalovirus prophylaxis after kidney transplantation but can also induce leukopenia, thereby making patients more susceptible to other infections. The epidemiology of leukopenia in patients on valganciclovir remains poorly understood. OBJECTIVE: To determine the incidence and risk factors for leukopenia in patients receiving valganciclovir for cytomegalovirus prophylaxis after kidney transplantation. METHODS: In this single-center, retrospective, cohort study, we included kidney recipients transplanted from January 1, 2003, to December 31, 2010, to determine the incidence and risk factors for leukopenia in patients who received valganciclovir for cytomegalovirus prophylaxis. The Kaplan-Meier product limit method was used to graphically assess time to leukopenia, and risk factors were assessed using Cox proportional hazards models. RESULTS: A total of 542 kidney transplant recipients were included in the study cohort. The cumulative incidence of leukopenia at 6 months posttransplant was 39.3% (11.0% for neutropenia). Low baseline white blood cell count (hazard ratio [HR] 2.34 [95% confidence interval [CI], 1.37-4.00]) and high baseline body mass index (HR 1.05 [95% CI, 1.02-1.09]) were independently associated with an increased risk of leukopenia, while higher Cockcroft-Gault creatinine clearance (HR 0.87 [95% CI, 0.78-0.97]) was significantly associated with a decreased risk of leukopenia. CONCLUSIONS: These data suggest that recipient baseline white blood cell count, baseline body mass index, and kidney function are clinical predictors of new-onset leukopenia after kidney transplantation. Our results may inform the approach to cytomegalovirus prophylaxis to reduce the risk of valganciclovir-induced leukopenia in kidney transplant recipients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".