ArticlePloS one2025
Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering approach.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
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6 authors.
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Abstract
backgroundAcute 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.
methodsThis 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.
resultsAmong 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.
conclusionOur 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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