Individualized prediction model of tacrolimus dose/weight-adjusted trough concentration based on machine learning approach
10.19405/j.cnki.issn1000-1492.2025.02.023
- Author:
Hui Jiang
1
;
Liang Tang
2
;
Xin Wang
2
;
Fan Jiang
1
;
Deguang Wang
3
;
Xiaofeng Lan
1
;
Xiang Xie
1
Author Information
1. Dept of Ultrasound , he Second Hospital of Anhui Medical Universty, Hefei 230601
2. Dept of Urology , The Second Hospital of Anhui Medical Universty, Hefei 230601
3. Dept of Nephrology , The Second Hospital of Anhui Medical Universty, Hefei 230601
- Publication Type:Journal Article
- Keywords:
kidney transplantation;
tacrolimus;
doppler ultrasound;
partial dependence plot;
machine learning;
individualized drug therapy
- From:
Acta Universitatis Medicinalis Anhui
2025;60(2):344-350
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To utilize machine learning(ML) algorithms to develop accurate and effective prediction models for TAC dose/weight-adjusted trough concentration(C0/D).
Methods:Data were collected on 264 TAC blood concentration monitoring data from 72 patients undergoing kidney transplantation. The effects of population statistical data, clinical features, combined medication, and ultrasound feature parameters on TAC C0/D were analyzed. Features with a significance level less than 0.05 in the univariate analysis of TAC C0/D were selected for inclusion in the random forest(RF) algorithm to identify significant features. These features were interpreted using partial dependency plots. Five ML algorithms, including RF, support vector regression(SVR), extreme gradient boosting(XGBoost), decision trees(DT) and artificial neural networks(ANN), were employed to establish the TAC C0/D prediction model. Hyper-parameter tuning was performed on the RF model that performed the best.
Results :Ten characteristic variables with importance scores>5 were retained and included in the ML model: transglutaminase, red blood cell count, blood urea nitrogen, weight, serum creatinine, renal segmental arterial resistance index, renal aortic resistance index, hematocrit, renal pelvic Young′s modulus value, and time after transplantation. The partial dependence plots showed that all 10 important variables screened were positively correlated with TAC C0/D. The tuned RF model outperformed the other models with aR2of 0.81, aRMSEof 43.93, and aMAEof 29.97.
Conclusion :The ML models demonstrate good performance in predicting TAC C0/D and provide innovative interpretations using partial dependence plot. The optimized RF model shows optimal performance and offers a novel tool for individualized medication adjustment for TAC in renal transplant patients.
- Full text:2026012216123286576基于机器学习的他克莫司剂量...量调整谷浓度个体化预测模型_蒋卉.pdf
