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Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering
Mohammad A Al-Mamun1, Ki Jin Jeun1, Todd Brothers2
1Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, West Virginia, United States of America.
Plos One
|July 11, 2025
Summary
This study developed an algorithm to identify patients at risk of chronic kidney disease (CKD) after acute kidney injury (AKI). The algorithm helps pinpoint specific patient profiles for targeted prevention strategies.
Area of Science:
- Nephrology
- Data Science
- Clinical Informatics
Background:
- Acute kidney injury (AKI) significantly increases the risk of developing chronic kidney disease (CKD), yet many survivors lack adequate follow-up care.
- Identifying patients at high risk for CKD post-AKI is crucial for timely intervention and prevention.
Purpose of the Study:
- To develop and validate a patient profiling algorithm to identify clinical phenotypes associated with AKI progression to CKD.
- To analyze risk factors and comorbidity patterns in patients with Hospital-Acquired AKI (HA-AKI) and Community-Acquired AKI (CA-AKI).
Main Methods:
- Retrospective analysis of electronic health records from 2010-2022.
- Classification of AKI into HA-AKI, CA-AKI, and No-AKI groups.
- Development of a custom algorithm combining network-based community and variable clustering methods to identify patient profiles and risk factors.
Main Results:
- Among 58,876 CKD patients, 10.2% had HA-AKI and 11.5% had CA-AKI.
- Common risk factors across AKI groups included long-term opiate use, atelectasis, ischemic heart disease, and lactic acidosis.
- The HA-AKI cohort exhibited a more complex comorbidity network, with conditions like high cholesterol and chronic pain showing higher network centrality.
Conclusions:
- The developed patient profiling algorithm effectively identifies AKI phenotypes related to CKD progression.
- This approach aids in early identification of CKD risk factors, enabling targeted prevention and potentially reducing healthcare costs.
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