Evidence map›Paper›PMID 40891324›Full record

ArticleClinical transplantation2025

A Comprehensive Assessment of Plasma CXCL9 and CXCL10 in Improving Clinical Prediction Models for Kidney Allograft Rejection.

Alvaro Assis de Souza, Dennis A Hesselink, Carolien C H M Maas, Andrew P Stubbs, Marian Clahsen-van Groningen, Carla C Baan, David van Klaveren, Karin Boer

Abstract read
In one paragraph

Article in Clinical transplantation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Alvaro Assis de SouzaDepartment of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0009-0007-0340-3162
Dennis A HesselinkDepartment of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0003-1871-1962
Carolien C H M MaasDepartment of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0002-3867-2465
Andrew P StubbsDepartment of Pathology and Clinical Bioinformatics, Erasmus MC Stubbs Group, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0001-9817-9982
Marian Clahsen-van GroningenDepartment of Pathology, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0003-0565-9560
Carla C BaanDepartment of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0003-2274-2788
David van KlaverenDepartment of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0002-2096-606X
Karin BoerDepartment of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0003-3617-3459

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chemokine levels may predict kidney graft rejection. This study evaluated whether adding early plasma chemokines C-X-C motif ligand 9 (CXCL9) or chemokines C-X-C motif ligand 10 (CXCL10) measurements to a standard-of-care model improves the prediction of the need for antirejection treatment and helps guide biopsy decisions. The benchmark model used recipient and donor age, human leukocyte antigen mismatches, and dialysis need in the first 3 days after transplantation. Plasma CXCL9 or CXCL10 was added, and the extended models were evaluated using likelihood ratio tests (LRTs), area under the receiver operating characteristic curve (ROC-AUC), flexible calibration curves, and net benefit analysis. Model internal validation was performed through bootstrapping. Among 163 consecutively transplanted recipients on standard immunosuppression, 43 (26.4%) required antirejection therapy between Days 3 and 21 posttransplant. The chemokine-extended models outperformed the benchmark (LRT p < 0.01), increasing discriminative ability (delta ROC-AUC of 0.02) and improving calibration. Across the range of risk thresholds for biopsy, the extended models provided better clinical utility, resulting in up to 17 fewer unnecessary biopsies per 100 patients. These findings suggest that adding plasma CXCL9 or CXCL10 to a benchmark model can improve patient care by reducing the number of biopsies in individuals unlikely to require antirejection therapy.

Indexed as

BiomarkersChemokine CXCL10Chemokine CXCL9Graft RejectionKidney Failure, ChronicKidney TransplantationAdultFemaleFollow-Up StudiesGlomerular Filtration RateGraft SurvivalHumansMaleMiddle AgedPrognosisRisk FactorsBiomarkersChemokine CXCL10Chemokine CXCL9CXCL10 protein, humanCXCL9 protein, humanallograft rejectionbiomarkerschemokineskidney transplantationprediction models

Identifiers

PMID40891324
PMCPMC12402972

What Socratic holds

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LicenceCC BY-NC
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Registered trials

None linked

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.