ArticlePloS one2025
Comparative analysis of outcomes in high KDPI spectrum kidney transplants using unsupervised machine learning algorithm.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- An Intelligent Multi-Task Supply Chain Model Based on Bio-Inspired Networks.Biomimetics (Basel, Switzerland) · 2026Article
- The Impact of Utilizing a Balancing Blindfold During Training on the Backward Running Technique in Experienced and Novice Male Handball Players.Biomimetics (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98-100%) compared to those with moderately high KDPI scores (85-97%), employing a novel case-matching approach using machine learning.
methodsWe conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98-100% kidneys with recipients of KDPI 85-97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender.
resultsA total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98-100% cohort and 4.6 years for the KDPI 85-97% cohort. The five-year allograft survival was 51.7% for the KDPI 98-100% group versus 58% for the KDPI 85-97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56).
conclusionTransplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.