Evidence map›Paper›PMID 42898365›Full record

ArticleKidney international reports2026

Claims-Based Phenotyping of Acute Kidney Transplant Rejection in a National Medicare Cohort.

Cameron Ekanayake, Miko Yu, Karthik Natarajan, Syed A Husain, Chetan Sree Muppavarapu, Jesse Schold, Sumit Mohan

Abstract read
In one paragraph

Article in Kidney international reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Cameron EkanayakeDivision of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York, USA.
Miko YuDivision of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York, USA.
Karthik NatarajanDepartment of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA.
Syed A HusainColumbia University Renal Epidemiology Group, New York, New York, USA.
Chetan Sree MuppavarapuColumbia University Renal Epidemiology Group, New York, New York, USA.
Jesse ScholdDepartment of Surgery, University of Colorado, Aurora, Colorado, USA.
Sumit MohanDivision of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York, USA.

Funding

Continuous Therapeutic Drug Monitoring of Antibiotics in CRRTR01DK126739 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MOHAN, SUMIT · 2021 to 2024
$2.8M
Towards rapid measurement of antibiotics in critical care settingR01EB032910 · NIBIB · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI CREMERS, SERGE, LIN, QIAO · 2022 to 2025
$2.7M
India - Factors of CKDu in Uddanam Study (India-FOCUS)U01DK130058 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Vivekanand Jha, Sumit Mohan · 2021 to 2026
$2.6M
5/14 APOL1 Long-term Kidney Transplantation Outcomes Network (APOLLO) Clinical CenterU01DK116066 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Sumit Mohan, Deirdre Sawinski · 2017 to 2026
$2.5M
Using Decision Making Science to Optimize Deceased Donor Kidney AllocationK23DK133729 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI HUSAIN, SYED ALI · 2022 to 2025
$678k
NIBIB NIH HHS R01 EB032910NIDDK NIH HHS K23 DK133729NIDDK NIH HHS R01 DK126739NIDDK NIH HHS U01 DK116066NIDDK NIH HHS U01 DK130058
6 · The paper itself

Abstract

Introduction: National transplant registries record acute rejection (AR) without the granularity needed to study rejection heterogeneity and prognosis at scale. Whether AR can be stratified by type and severity in national claims data using a claims-based algorithm, and whether these phenotypes correlate with graft outcomes, remains unclear. Methods: We conducted a cohort study using the US Renal Data System (USRDS) data including adult kidney transplant recipients from 2012-2021. AR within 12 months posttransplant was identified using International Classification of Diseases codes for transplant complications with evidence of antirejection treatment within 2 weeks. Episodes were categorized by type and severity based on treatment regimen. Biopsy-associated AR was defined by renal biopsy within 14 days before the treatment. Death-censored graft survival was assessed using Kaplan-Meier methods and multivariable Cox proportional hazards models. Results: Among 92,845 recipients, 5712 AR episodes were identified (69.4% mild acute-cellular rejection [ACR], 8.8% severe ACR, 19.7% antibody-mediated rejection [AMR], and 2.1% mixed [ACR + AMR]). Among 3052 biopsy-confirmed episodes, 3-year death-censored graft survival differed meaningfully across phenotypes (87.9% mild ACR, 80.7% severe ACR, 73.5% AMR, and 68.4% mixed [ACR + AMR] [ Conclusion: Claims-based phenotyping can identify clinically meaningful AR subtypes and prognostic gradients at national scale, enabling more granular national rejection surveillance.

Indexed as

acute rejectionadministrative claims dataallograft survivalantibody mediated rejectioncellular rejectionkidney transplantationphenotypeUnited States renal data system

Identifiers

PMID42898365
PMCPMC13677584

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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.