Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering approach

• Published in: PloS one Vol. 20; no. 7; p. e0310749
• Main Authors: Al-Mamun, Mohammad A., Jeun, Ki Jin, Brothers, Todd, Asare, Ernest O., Shawwa, Khaled, Ahmed, Imtiaz
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.07.2025; Public Library of Science (PLoS)
• Subjects: Acidosis; Acute Kidney Injury - epidemiology; Acute Kidney Injury - pathology; Acute renal failure; Aged; Algorithms; Analgesics; Atelectasis; Cardiovascular diseases; Cholesterol; Chronic kidney failure; Chronic pain; Cluster Analysis; Clustering; Codes; Comorbidity; Complications and side effects; Development and progression; Diagnosis; Disease Progression; Electronic Health Records; Electronic medical records; Expenditures; Female; Gastrointestinal tract; Graph matching; Health aspects; Heart diseases; Hemodialysis; Hospitalization; Hospitals; Humans; Identification and classification; Ischemia; Kidney diseases; Kidneys; Lactic acidosis; Male; Middle Aged; Mortality; Osteoarthritis; Patients; Phenotypes; Renal Insufficiency, Chronic - epidemiology; Renal Insufficiency, Chronic - etiology; Renal Insufficiency, Chronic - pathology; Retrospective Studies; Risk Factors; Variables; United States; West Virginia; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0310749

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. 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. 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. 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.