Evidence map›Paper›PMID 39523797›Full record

ArticleKidney research and clinical practice2026

Study protocol for a consortium linking health medical records, biospecimens, and biosignals in Korean patients with acute kidney injury (LINKA cohort).

Donghwan Yun, Seung Seok Han, Jeonghwan Lee, Yaerim Kim, Kwangsoo Kim, Kyubok Jin, Ji Eun Kim, Shin Young Ahn, Gang-Jee Ko, Seokwoo Park and 10 more

Abstract read
In one paragraph

Article in Kidney research and clinical practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

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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

20 authors.

Donghwan YunDepartment of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Seung Seok HanDepartment of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Jeonghwan LeeDepartment of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
Yaerim KimDepartment of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea.
Kwangsoo KimDepartment of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea.
Kyubok JinDepartment of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea.
Ji Eun KimDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea.
Shin Young AhnDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea.
Gang-Jee KoDepartment of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea.
Seokwoo ParkDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Sejoong KimDepartment of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Hee-Yeon JungDepartment of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea.
Jang-Hee ChoDepartment of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea.
Sun-Hee ParkDepartment of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea.
Eun Sil KohDepartment of Internal Medicine, The Catholic University of Korea, Yeouido St. Mary's Hospital, Seoul, Republic of Korea.
Sungjin ChungDepartment of Internal Medicine, The Catholic University of Korea, Yeouido St. Mary's Hospital, Seoul, Republic of Korea.
Jung Pyo LeeDepartment of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
Dong Ki KimDepartment of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Sung Gyun KimDepartment of Internal Medicine, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea.
Jung Nam AnDepartment of Internal Medicine, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) may transition into acute kidney disease (AKD) or chronic kidney disease (CKD), leading to subacute and chronic deterioration, respectively. Despite extensive research on AKI, a significant gap exists in understanding the specific biomarkers and development of individualized treatments prior to progression to AKD and CKD.

methodsAs a consortium linking health medical records, biospecimens, and biosignals, eight Korean tertiary hospitals participated in the establishment of a retrospective and prospective cohort, each comprising approximately 1,500 patients with AKI receiving continuous kidney replacement therapy (CKRT). Other information included AKI-related information, CKRT prescriptions, and patient outcomes. Follow-up timeframes were set at baseline, 1 week, 3 months, and 1 year after the initiation of CKRT. Human biospecimens will be collected from the prospective cohort. An artificial intelligence model was developed using the retrospective cohort to predict the prognosis of AKD and its subsequent sequelae and to formulate patient-individualized treatments, with validation planned in a prospective cohort. Follow-up studies are scheduled to identify biomarkers related to outcomes using biospecimens. Finally, based on the results and literature review, decision-making on the prevention and management of diseases, as well as the development of treatment guidelines, are being planned.

conclusionThis study will provide scientific evidence on clinical insights and appropriate management targets for AKI and AKD, which will form the basis for relevant treatment guidelines. Additionally, these findings may facilitate a more personalized approach to patient care, enabling clinicians to tailor treatments based on individual biomarker profiles and predictive models.

Indexed as

Acute kidney injuryArtificial intelligenceChronic kidney failureContinuous renal replacement therapyMulticenter study

Identifiers

PMID39523797
PMCPMC12990225

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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.