Using machine learning models for predicting monthly iPTH levels in hemodialysis patients

Chih Chieh Hsieh, Chin Wen Hsieh, Mohy Uddin, Li Ping Hsu, Hao Huan Hu, Shabbir Syed-Abdul

Research output: Contribution to journalArticlepeer-review

Abstract

Background and Objective: Intact parathyroid hormone (iPTH), also known as active parathyroid hormone, is an important indicator commonly for monitoring secondary hyperparathyroidism (SHPT) in patients undergoing hemodialysis. The aim of this study was to use machine learning (ML) models to predict monthly iPTH levels in patients undergoing hemodialysis. Methods: We conducted a retrospective study on patients undergoing regular hemodialysis. Patients’ blood examinations data was collected from Taiwan Society of Nephrology – Kidney Dialysis, Transplantation (TSN-KiDiT) registration system, and patients’ medications data was collected from Pingtung Christian Hospital (PTCH), Taiwan. We used five different ML models to classify patients into three distinct categories based on their iPTH levels: iPTH < 150, iPTH ≥ 150 & iPTH < 600, and iPTH ≥ 600(pg/ml). Results: We ultimately included 1,351 patients in our study and processed the data in four different ways. These methods varied based on the duration of the data (either using data from just one month or continuously over three months) and the number of features used (either all 52 features or only 20 most important features identified by SHapley Additive exPlanations (SHAP) analysis). The XGBoost model, using data from a continuous three-month period and all available features, yielded the best Weighted AUROC (0.922). Conclusions: ML is highly effective in predicting iPTH levels in hemodialysis patients, notably accurate for those with iPTH over 600 pg/ml. This method enables early identification of high-risk patients, reducing reliance on retrospective blood test assessments. Future research should focus on advancing explainable AI methods to foster clinicians' trust, and developing adaptable ML frameworks that could seamlessly integrate with existing healthcare systems.

Original languageEnglish
Article number108541
JournalComputer Methods and Programs in Biomedicine
Volume260
DOIs
Publication statusPublished - Mar 2025

Keywords

  • Hemodialysis
  • Machine learning
  • Nephrology
  • Secondary hyperparathyroidism
  • Taiwan

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

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