Clinical nutrition ESPEN · 2021
Explainable machine learning model to predict refeeding hypophosphatemia.
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Choi Tae Yang, Chang Min-Yung, Heo Sungtaik, Jang Ji Young
Аннотация
BACKGROUND & AIMS: Refeeding syndrome (RFS) is a disease that occurs when feeding is restarted and metabolism changes from catabolic to anabolic status. RFS can manifest variously, ranging from asymptomatic to fatal, therefore it may easily be overlooked. RFS prediction using explainable machine learning can improve diagnosis and treatment. Our study aimed to propose a machine learning model for RFS prediction, specifically refeeding hypophosphatemia, to evaluate its performance compared with conventional regression models, and to explain the machine learning classification through Shapley additive explanations (SHAP) values.
METHODS: A retrospective study was conducted including 806 patients, with 2 or more days of nothing-by-mouth prescription, and with phosphate (P) level measurements within 5 days of refeeding were selected. We divided the patients into hypophosphatemia (n = 367) and non-hypophosphatemia groups (n = 439) at a P level of 0.8 mmol/L. Among the features examined within 48 h after admission, we reviewed laboratory test results and electronic medical records. Logistic, Lasso, and ridge regressions were used as conventional models, and performances were compared with our extreme gradient boosting (XGBoost) machine learning model using the area under the receiver operating characteristic curve. Our model was explained using the SHAP value.
RESULTS: The areas under the curve were 0.950 (95% confidence interval: 0.924-0.975) for our XGBoost machine learning model and surpassed the performance of conventional regression models; 0.760 (0.707-0.813) for logistic regression, 0.751 (0.694-0.807) for Lasso regression, and 0.758 (0.701-0.809) for ridge regression. According to the SHAP values in the order of importance, low initial P, recent weight loss, high creatinine, diabetes mellitus with insulin use, low haemoglobin A1c, furosemide use, intensive care unit admission, blood urea nitrogen level of 19-65, parenteral nutrition, magnesium below or above the normal range, low potassium, and older age were features to predict refeeding hypophosphatemia.
CONCLUSIONS: The machine learning model for predicting RFS has a substantially higher effectiveness than conventional regression methods. Creating an accurate risk assessment tool based on machine learning for early identification of patients at risk for RFS can enable careful nutrition management planning and monitoring in the intensive care unit, towards reducing the incidence of RFS-related morbidity and mortality.
METHODS: A retrospective study was conducted including 806 patients, with 2 or more days of nothing-by-mouth prescription, and with phosphate (P) level measurements within 5 days of refeeding were selected. We divided the patients into hypophosphatemia (n = 367) and non-hypophosphatemia groups (n = 439) at a P level of 0.8 mmol/L. Among the features examined within 48 h after admission, we reviewed laboratory test results and electronic medical records. Logistic, Lasso, and ridge regressions were used as conventional models, and performances were compared with our extreme gradient boosting (XGBoost) machine learning model using the area under the receiver operating characteristic curve. Our model was explained using the SHAP value.
RESULTS: The areas under the curve were 0.950 (95% confidence interval: 0.924-0.975) for our XGBoost machine learning model and surpassed the performance of conventional regression models; 0.760 (0.707-0.813) for logistic regression, 0.751 (0.694-0.807) for Lasso regression, and 0.758 (0.701-0.809) for ridge regression. According to the SHAP values in the order of importance, low initial P, recent weight loss, high creatinine, diabetes mellitus with insulin use, low haemoglobin A1c, furosemide use, intensive care unit admission, blood urea nitrogen level of 19-65, parenteral nutrition, magnesium below or above the normal range, low potassium, and older age were features to predict refeeding hypophosphatemia.
CONCLUSIONS: The machine learning model for predicting RFS has a substantially higher effectiveness than conventional regression methods. Creating an accurate risk assessment tool based on machine learning for early identification of patients at risk for RFS can enable careful nutrition management planning and monitoring in the intensive care unit, towards reducing the incidence of RFS-related morbidity and mortality.