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Machine Learning-Based Prediction of Hemoglobin Variability in Patients with Chronic Kidney Disease Receiving
Pattanan Buranasaksathien1, Wanjak Pongsittisak1, Padoemwut Teerawongsakul1
1Department of Internal Medicine, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand.
Background:
Anemia is a common complication of chronic kidney disease (CKD) and is predominantly managed with erythropoiesis-stimulating agents (ESAs). Hemoglobin response to ESA therapy varies substantially among patients and remains difficult to predict owing to multiple interacting clinical and treatment-related factors. Machine learning approaches may help model hemoglobin dynamics using routinely collected clinical data.
Purpose:
To develop and evaluate machine learning models for predicting weekly hemoglobin changes in patients with CKD receiving ESA therapy.
Patients And Methods:
A retrospective cohort study was conducted using electronic medical records from a tertiary care center in Thailand (January 2012 to December 2022). Adult patients with CKD stages 3B-5 and an estimated glomerular filtration rate (eGFR) below 45 mL/min/1.73 m2 receiving ESA therapy were included. Four machine learning algorithms (Decision Tree, Random Forest, XGBoost, and Support Vector Machine) were trained using data from 80% of patients and evaluated on an independent test set comprising the remaining 20% of patients, with all longitudinal observations from each patient kept within the same partition. Performance was assessed using root mean square error (RMSE) and Pearson correlation coefficient.
Results:
A total of 834 patients contributing 10,335 clinical visits and 9,935 time-series observations were included. The median age was 71 years and 58.6% were female. RMSE values were 0.121, 0.115, 0.117, and 0.115 for Decision Tree, Random Forest, XGBoost, and Support Vector Machine, respectively. Pearson correlation coefficients ranged from 0.457 to 0.553 (all p < 0.001).
Conclusion:
Machine learning models trained on routinely collected clinical data showed feasibility for predicting short-term hemoglobin variability in patients with CKD receiving ESA therapy. Larger multicenter and external validation studies are needed to refine predictive accuracy and evaluate clinical utility.
