Evidence map›Paper›PMID 40610745›Full record

ArticleCommunications medicine2025

Clustering analysis of multi-site electronic health records reveals distinct subphenotypes in stage-1 acute kidney injury.

Deyi Li, Ho Yin Chan, Alan S L Yu, John A Kellum, Dana Y Fuhrman, Elizabeth A Chrischilles, Lindsay G Cowell, Sravani Chandaka, Jacob Kean, Kathleen M McTigue and 6 more

Abstract read
In one paragraph

Article in Communications medicine, 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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0citing papers 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

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

16 authors.

Deyi LiDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Ho Yin ChanDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Alan S L YuDivision of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, KS, USA.ORCID http://orcid.org/0000-0002-1776-2533
John A KellumDepartment of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0003-1995-2653
Dana Y FuhrmanDepartment of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Elizabeth A ChrischillesDepartment of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA, USA.
Lindsay G CowellDepartment of Health Data Science and Biostatistics, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA.
Sravani ChandakaDepartment of Population Health, University of Kansas Medical Center, Kansas City, KS, USA.
Jacob KeanDepartment of Population Health Sciences, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
Kathleen M McTigueDepartment of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Abu Saleh Mohammad MosaDepartment of Biomedical Informatics, Biostatistics, and Medical Epidemiology, University of Missouri School of Medicine, Columbia, MO, USA.ORCID http://orcid.org/0000-0002-8956-1466
Bradley TaylorCTSI Center for Biomedical Informatics, Medical College of Wisconsin, Milwaukee, WI, USA.ORCID http://orcid.org/0000-0002-6414-4172
Lemuel R WaitmanDepartment of Biomedical Informatics, Biostatistics, and Medical Epidemiology, University of Missouri School of Medicine, Columbia, MO, USA.
Mahanaz SyedDepartment of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Yong HuBig Data Decision Institute, Jinan University, Guangzhou, Guangdong, China.ORCID http://orcid.org/0000-0002-3837-1750
Mei LiuDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA. mei.liu@ufl.edu.ORCID http://orcid.org/0000-0002-8036-2110

Funding

Identifying Personalized Risk of Acute Kidney Injury with Machine LearningR01DK116986 · NIDDK · UNIVERSITY OF KANSAS MEDICAL CENTER · PI LIU, MEI · 2019 to 2021
$1.5M
Personalized Machine Learning for Acute Kidney Injury Prediction and PrognosisR01DK137881 · NIDDK · UNIVERSITY OF FLORIDA · PI MEI LIU · 2024 to 2026
$817k
National Science Foundation (NSF) 2444044NIDDK NIH HHS R01 DK116986NIDDK NIH HHS R01 DK137881U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R01DK116986
6 · The paper itself

Abstract

backgroundAcute Kidney Injury (AKI) can adversely affect multiple organ systems, including the heart, brain, and immune system. Stage 1 AKI (AKI-1), although mild in clinical presentation, constitutes a substantial subset of AKI patients with heterogeneous outcomes, warranting further investigation into its subphenotypes.

methodsWe performed clustering analysis on seven-day serum creatinine (SCr) trajectories preceding AKI-1 onset in 53,565 AKI-1 patients (aged 18-89 years; 55.57% male) across eight academic hospitals. Each AKI-1 patient was matched to a non-AKI counterpart to evaluate how different AKI-1 subphenotypes influence clinical indicators and outcomes.

resultsThree distinct AKI-1 subphenotypes are identified. Patients in Subphenotype C (n = 5,378; 10.0%) exhibit a higher proportion of abnormal values across clinical indicators compared to those in Subphenotypes A (n = 27,049; 50.5%) and B (n = 21,138; 39.5%). Subphenotype C is associated with significantly higher odds ratios (ORs) for in-hospital, 30-day, and one-year all-cause mortality relative to Subphenotypes A and B. Conversely, Subphenotype B exhibits a higher susceptibility to developing chronic kidney disease (CKD) within one year after discharge following AKI-1, compared to both Subphenotypes A and C, after adjustment for baseline SCr levels. All AKI-1 subphenotypes are associated with significantly elevated risks of all-cause mortality and the need for dialysis or renal replacement therapy (RRT) compared to their respective non-AKI counterparts.

conclusionsThis study reveals substantial heterogeneity in clinical indicators and outcomes within AKI-1. Future research focusing on these subphenotypes may pave the way for more personalized and targeted interventions for patients with AKI-1.

Identifiers

PMID40610745
PMCPMC12229304

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