Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering approach Article Swipe
Mohammad A. Al‐Mamun
,
Ki Jin Jeun
,
Todd Brothers
,
Ernest O. Asare
,
Khaled Shawwa
,
Imtiaz Ahmed
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0310749
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0310749
Background Acute kidney injury (AKI) can lead to an approximate ninefold increased risk for developing chronic kidney disease (CKD). Despite this, many AKI survivors lack proper nephrology follow-up, highlighting the urgent need to identify patient profiles before onset CKD. Thus, we aimed to develop a patient profiling algorithm to identify clinical phenotypes from AKI to CKD progression. Methods This retrospective study utilized electronic health records data from 2010 to 2022. We classified AKI into three groups: Hospital Acquired AKI (HA-AKI), Community Acquired AKI (CA-AKI), and No-AKI. We developed a custom patient profiling algorithm by combining network-based community and variable clustering methods to examine risk factors among three groups. The top three clusters were presented using comorbidities and medical procedures network graphs, and matched between two methods to find similarities and dissimilarities. Results Among 58,876 CKD patients, 10.2% (5,981) and 11.5% (6,762) had HA-AKI and CA-AKI, respectively. The No-AKI group had a higher comorbidity burden compared to AKI groups, with average comorbidities of 2.84 vs. 2.04. Commonly risk factors observed in both AKI cohorts included long-term opiate analgesic use, atelectasis, history of ischemic heart disease, and lactic acidosis. The comorbidity network in HA-AKI patients was more complex compared to CA-AKI and No-AKI groups with higher number of diagnosis (64 vs 62 vs 55). The HA-AKI cohort had several conditions with higher degree (mean number of edges connected to each diagnosis) and betweenness centrality (bridges connecting different diagnosis clusters) including high cholesterol (34, 91.10), chronic pain (33, 103.38), tricuspid insufficiency (38, 113.37), osteoarthritis (34, 56.14), and removal of GI tract components (37, 68.66) compared to the CA-AKI cohort. Conclusion Our proposed patient profiling algorithm successfully identifies AKI phenotypes toward CKD progression, offering a promising approach to identify early risk factors for CKD in improving targeted prevention strategies and reducing healthcare expenditures.
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- Language
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- OA Status
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412345214Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0310749Digital Object Identifier
- Title
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Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering approachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-11Full publication date if available
- Authors
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Mohammad A. Al‐Mamun, Ki Jin Jeun, Todd Brothers, Ernest O. Asare, Khaled Shawwa, Imtiaz AhmedList of authors in order
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https://doi.org/10.1371/journal.pone.0310749Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1371/journal.pone.0310749Direct OA link when available
- Concepts
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Medicine, Acute kidney injury, Comorbidity, Internal medicine, Kidney disease, Algorithm, Retrospective cohort study, Intensive care medicine, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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