Evidence map›Paper›PMID 41291098›Full record

ArticleScientific reports2025

Impact of IgG monitoring and machine learning based prediction on outcomes of ABO incompatible kidney transplantation in blood type O recipients.

Youngmin Ko, Jin-Myung Kim, Hye Eun Kwon, Sung Shin, Joo Hee Jung, Young Hoon Kim, Juhan Lee, Dae-Hyun Ko, Hyunwook Kwon

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Youngmin KoDivision of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Jin-Myung KimDivision of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Hye Eun KwonDivision of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Sung ShinDivision of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Joo Hee JungDivision of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Young Hoon KimDivision of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Juhan LeeDepartment of Surgery, The Research Institute for Transplantation, Yonsei University College of Medicine, Seoul, South Korea.
Dae-Hyun KoDepartment of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea. daehyuni1118@amc.seoul.kr.
Hyunwook KwonDivision of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea. hwkwon@amc.seoul.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ABO-incompatible kidney transplantation (ABO-i KT) facilitates transplantation across blood types; however, antibody-mediated rejection (ABMR) remains a major concern, particularly in blood type O recipients. This retrospective study evaluated the effect of immunoglobulin G (IgG) monitoring and machine learning (ML)-based IgG prediction on post-transplant outcomes in 408 ABO-i KT recipients treated between 2014 and 2020. In blood type O recipients, the introduction of IgG monitoring (Era 2) was associated with a significantly lower incidence of ABMR (P = 0.041) and acute rejection (P = 0.037) compared with Immunoglobulin M (IgM)-only monitoring (Era 1). A higher initial IgM titer was identified as a risk factor for ABMR. To address the absence of IgG data in the IgM-only cohort, an ML model was developed using 610 cases to predict pre-transplant IgG titers based on IgM levels, number of plasmapheresis sessions, and ABO blood type. The model demonstrated good predictive performance (mean absolute error [MAE] = 0.593, R

Indexed as

ABO Blood-Group SystemBlood Group IncompatibilityGraft RejectionImmunoglobulin GKidney TransplantationMachine LearningAdultFemaleHumansImmunoglobulin MMaleMiddle AgedRetrospective StudiesRisk FactorsABO Blood-Group SystemImmunoglobulin GImmunoglobulin M

Identifiers

PMID41291098
PMCPMC12748742

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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.