Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy · 2024
Machine learning-based prediction of vancomycin concentration after abdominal administration in patients with peritoneal dialysis-related peritonitis.
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Lv Bo, Liu Wenxiu, Lu Ying, Wang Zhi, Shi Aiming
Аннотация
INTRODUCTION: Peritonitis is a serious complication of peritoneal dialysis (PD), in which insufficient control of antibacterial drug concentrations poses a significant risk for poor outcomes. Predicting antibacterial drug concentrations is crucial in clinical practice. The limitations imposed by compartment models have presented a considerable challenge.
METHODS: In this study, we employed machine learning as model-free methods to circumvent the constraints of compartment models. We collected data from 68 observations from 38 patients with peritoneal dialysis-related peritonitis who were treated with vancomycin from the EHR system. This data included information about drug administration, demographic details, and experimental indicators as predictors. We constructed models using Genetic Adaptive Supporting Vector Regression (GA-SVR), KNN-regression, GBM, XGBoost, and a stacking ensemble model. Additionally, we used RMSE loss and partial-dependence profiles to elucidate the effects of these predictors.
RESULTS: GA-SVAR outperformed other large-scale models. In 10-fold cross-validation, the RMSE ratio and R-squared values for direct concentration prediction were 23.5% and 0.633, respectively. The ROC AUC for predicting concentrations below 15 and exceeding 20 μg/mL were 0.890 and 0.948, respectively. Notably, the most influential predictors included times of drug administration and weight. These predictors were also influenced by residual kidney function.
CONCLUSION: To assist in controlling vancomycin concentrations for patients with PD-related peritonitis in clinical practice, we developed GA-SVR and a corresponding explainer model. Our study improves the controlling of vancomycin in clinical settings by enhancing our understanding of vancomycin concentration in patients with PD-related peritonitis.
METHODS: In this study, we employed machine learning as model-free methods to circumvent the constraints of compartment models. We collected data from 68 observations from 38 patients with peritoneal dialysis-related peritonitis who were treated with vancomycin from the EHR system. This data included information about drug administration, demographic details, and experimental indicators as predictors. We constructed models using Genetic Adaptive Supporting Vector Regression (GA-SVR), KNN-regression, GBM, XGBoost, and a stacking ensemble model. Additionally, we used RMSE loss and partial-dependence profiles to elucidate the effects of these predictors.
RESULTS: GA-SVAR outperformed other large-scale models. In 10-fold cross-validation, the RMSE ratio and R-squared values for direct concentration prediction were 23.5% and 0.633, respectively. The ROC AUC for predicting concentrations below 15 and exceeding 20 μg/mL were 0.890 and 0.948, respectively. Notably, the most influential predictors included times of drug administration and weight. These predictors were also influenced by residual kidney function.
CONCLUSION: To assist in controlling vancomycin concentrations for patients with PD-related peritonitis in clinical practice, we developed GA-SVR and a corresponding explainer model. Our study improves the controlling of vancomycin in clinical settings by enhancing our understanding of vancomycin concentration in patients with PD-related peritonitis.