Machine learning progressive CKD risk prediction model is associated with CKD-mineral bone disorder

• Published in: Bone Reports 2024-09, Vol.22, p.101787, Article 101787
• Main Authors: Aoki, Joseph, Khalid, Omar, Kaya, Cihan, Kothari, Tarush, Silberman, Mark, Skordis, Con, Hughes, Jonathan, Hussong, Jerry, Salama, Mohamed E.
• Format: Article
• Language: English
• Subjects: Calcium; Chronic kidney disease; Chronic kidney disease-metabolic bone disorder; Machine learning; Parathyroid hormone; Phosphate
• ISSN: 2352-1872; 2352-1872
• DOI: 10.1016/j.bonr.2024.101787

Recently, we developed the machine learning (ML)-based Progressive CKD Risk Classifier (PCRC), which accurately predicts CKD progression within 5 years. While its performance is robust, it is unknown whether PCRC categorization is associated with CKD-mineral bone disorder (CKD-MBD), a critical, yet under-recognized, downstream consequence. Therefore, we aimed to 1) survey real-world testing utilization data for CKD-MBD and 2) evaluate ML-based PCRC categorization with CKD-MBD. The cohort study utilized deidentified data from a US laboratory outpatient network, composed of 330,238 outpatients, over 5 years. The main outcomes were: 1) Laboratory testing utilization of eGFR, urine albumin creatinine ratio (UACR), parathyroid hormone (PTH), calcium, phosphate; and 2) PCRC categorization and biochemical abnormalities associated with CKD-MBD over 5 years. We identified significant under-utilization of laboratory testing for UACR, phosphate and PTH, which ranged from −40 % to −100 % against the minimum standard-of-care. At five years, the CKD progression group, as predicted by the PCRC, was associated with 15.5 % increase in phosphate (P value