ArticleFrontiers in immunology2026
Development and clinical implementation of a local automated kidney allocation platform integrating virtual crossmatch.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Highly sensitized kidney transplant candidates possess preformed anti-human leukocyte antigen (HLA) antibodies that substantially reduce their likelihood of receiving a compatible deceased-donor organ. Manual virtual crossmatch (VXM) workflows are sequential and labor-intensive, often limiting histocompatibility laboratories to screening only a subset of eligible candidates during time-sensitive donor evaluations and potentially overlooking compatible recipients. Methods: We designed and developed an automated kidney allocation system (KAS) that links the hospital information system with HLA Fusion antibody data to perform candidate scoring and VXM prediction across the entire eligible active waitlist for every deceased donor workup. We conducted a single-center implementation study consisting of a cross-sectional analysis of 219 active kidney transplant candidates and a retrospective comparison of 67 deceased donor match runs processed by the automated KAS and the historical manual VXM workflow. The primary outcome was identification of potentially compatible highly sensitized patient (HSP) events. Analyse. Results: Of 219 candidates, 98 (44.7%) were highly sensitized (cPRA ≥80%). Elevated prevalence of HSP was associated with prior transplantation, pregnancy, and transfusion. HSP candidates waited far longer than non-sensitized peers (median 7.10 vs. 3.81 years; p < 0.001). Across 67 retrospective match runs, the automated KAS identified 13 potentially compatible HSP events compared with 4 under the manual workflow, more than threefold higher (rate ratio 3.25; 95% CI 1.06-9.97; p = 0.039). Automated runs completed in 17 ± 0.3 minutes, compared with 152 ± 1.6 minutes for manual workups (p<0.0001). Conclusions: A locally developed automated KAS substantially improved compatible donor identification for highly sensitized kidney transplant candidates while reducing VXM turnaround from hours to minutes. By enabling comprehensive evaluation of every eligible candidate during each deceased donor workup, automated allocation represents a practical and potentially scalable strategy that could improve equity and operational efficiency.
Indexed as
Identifiers
What Socratic 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 Socratic graph.