Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy
Abstract Background The flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construct a machine learning algorithm predictive...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-025-02987-9 |
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| Summary: | Abstract Background The flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. Methods This study retrospectively collected the clinical data of patients who underwent F-URL at the Second Affiliated Hospital of Zhengzhou University from January 2016 to March 2024. The patients were divided into a non-fungal infection group and a fungal infection group based on whether a fungal infection occurred within three months post-surgery. The patient data from January 2016 to December 2023 were used as training data, and the patient data from January 2024 to March 2024 were used as testing set. The training data was randomly divided into a training set and validation set at a ratio of 90:10. Use LASSO regression to screen clinical features based on the training set. Nine machine learning algorithms, Logistic Regression (LR), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), Random Forest (RF), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient Boosting Machines (GBM), and Neural Network (NNet), were used to construct models. The performance of these nine models was evaluated and the best predictive model was selected based on the validation set, and evaluate the best predictive model’s generalization ability using the testing set. Visualize the constructed optimal machine learning model using the SHapley additive interpretation (SHAP) value method. SHAP force plots were established to show the application of the prediction model at the individual level. Results A total of 13 clinical features were used to construct predictive models: age, diabetes mellitus (DM), history of malignancy, being bedridden, admission white blood cells (WBC), preoperative ureteral stenting, operation time, postoperative fever, postoperative Neu, carbapenem antibiotics use, duration of antibiotic therapy, length of hospital stay (LOS), and postoperative stent duration. Comparing the performance of 9 prediction models, we found that the model constructed using XGBoost algorithm had the best performance. The model constructed using XGBoost algorithm shows good discrimination, generalization and clinical applicability in the testing set. Conclusions The XGBoost model developed in this study has good predictive ability and clinical applicability for evaluating the risk of fungal infection following F-URL. |
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| ISSN: | 1472-6947 |