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Development and validation of a machine learning-based risk prediction model for hyperkalemia in patients with
1Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
Insights
Logistic regression (LR) effectively predicts hyperkalemia risk in chronic kidney disease (CKD) patients. This model offers high accuracy and clinical utility for managing CKD complications.
Area of Science:
- Nephrology
- Medical Informatics
- Biostatistics
Background:
- Hyperkalemia is a serious complication in chronic kidney disease (CKD) patients.
- Accurate prediction of hyperkalemia is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and identify the optimal predictive model for hyperkalemia in CKD patients.
- To compare the performance of various machine learning models for hyperkalemia prediction.
Main Methods:
- Clinical data from 1056 CKD patients (343 hyperkalemia, 713 normal potassium) were analyzed.
- Feature selection identified 20 key variables; five machine learning models (LR, DT, GBM, SVM, KNN) were trained and validated.
- Model performance was evaluated using AUC, Brier score, calibration curves, and decision curve analysis (DCA).
Main Results:
- The logistic regression (LR) model demonstrated superior performance, achieving AUCs of 0.899 (training) and 0.868 (test) with F1 scores of 0.819 and 0.725, respectively.
- LR model showed excellent calibration and clinical utility via DCA, outperforming other models.
- A nomogram based on the LR model was developed to aid clinical decision-making.
Conclusions:
- The logistic regression model is identified as the optimal predictive tool for hyperkalemia risk in CKD patients.
- The developed LR model and nomogram offer significant potential for clinical application in managing CKD patients.
- This study highlights the value of machine learning in predicting adverse events in chronic kidney disease.
Abstract:
To develop an optimal predictive model for hyperkalemia in patients with chronic kidney disease (CKD). Clinical data of CKD patients were collected from Shandong Provincial Hospital Affiliated to Shandong First Medical University between January 2017 and December 2023, including 343 hyperkalemia cases and 713 cases with normal potassium levels. The data were divided into training and test sets at a 7:3 ratio. Important features were screened via univariate analysis, collinearity diagnosis, and LASSO regression, identifying 20 feature variables for hyperkalemia. Five machine learning models were established: logistic regression (LR), decision tree (DT), gradient boosting machine (GBM), support vector machine (SVM), and K-nearest neighbors (KNN). Models were compared using the area under the curve (AUC), Brier score, calibration curve, decision curve analysis (DCA), and overfitting control. The LR model was identified as the optimal model, showing excellent performance in predicting hyperkalemia. The AUCs were 0.899 (training set) and 0.868 (test set), with corresponding F1 scores of 0.819 and 0.725. Calibration and DCA curves demonstrated high predictive accuracy and clinical benefit. Additionally, the nomogram based on the LR model could assist in clinical decision-making. Our findings suggest that the LR model is the optimal predictive model for the risk of hyperkalemia in CKD patients.
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