ArticleRenal failure2025
Development of BK polyomavirus-associated nephropathy risk prediction in kidney transplant recipients.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for infection after kidney transplantation: a systematic review.Frontiers in medicine · 2026Pooled it
- Do BKPyV genomic features underlie clinical divergence between kidney and hematopoietic transplant recipients?Virulence · 2026Article
- Updates in the Diagnosis and Treatment of BK Viraemia in Kidney Transplant Recipients: Current and Future Insights.Journal of clinical medicine · 2025Review
- Predicting VRE Infection After Liver Transplantation With a Time-Updated Colonization Score.Transplant infectious disease : an official journal of the Transplantation SocietyArticle
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the development of potential prevention therapies for BK polyomavirus (BKPyV)-associated nephropathy (BKPyVAN), risk prediction models are needed to identify kidney transplant recipients at high risk for BKPyVAN.
methodsThis single-center retrospective study aimed to develop a risk prediction model and an integer-based risk score for BKPyVAN development, defined as plasma BKPyV-DNA >10,000 copies/mL and/or biopsy-proven BKPyVAN, within 1-year post-transplant, using donor and recipient characteristics at the time of transplantation. We randomly split patients into development and validation cohorts and applied logistic regression with backward selection to identify significant variables. Model performance was evaluated using the area under the receiver-operating characteristic curve (AUC) and calibration plots.
resultsThis study included 560 patients, of whom 75 (13%) patients had BKPyVAN. Age >50 years, male sex, and prior kidney transplant were selected for the final model. The total integer score ranged from 0 to 4 points, with 1 point assigned for age >50 years and male sex, and 2 points for prior kidney transplant. The AUC was 0.65 in both development and validation cohorts. Calibration plots showed an incremental increase in risk with higher total scores. The integer score indicated that patients with a total score of 2 or higher (i.e. males aged >50 years or those with prior kidney transplants) have a predicted risk of 20% or greater.
conclusionAlthough the AUC was suboptimal, the results suggest that our model may still be valuable for identifying high-risk patients.
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