Machine learning-assisted decision-support models to better predict patients with calculous pyonephrosis

Liu, Hailang; Wang, Xinguang; Tang, Kun; Peng, Ejun; Ding, Xia; Chen, Zhiqiang

doi:10.21037/tau-20-1208

“…In the opinion of Shi et al [ 34 ], CRP has a mediocre level of efficacy in determining sepsis brought on by a new coronavirus infection. It was found that CRP was an independent risk factor for the emergence of pyonephrosis and was essential in the construct of the ML-assisted diagnostic model of calculous pyonephrosis designed by Liu et al [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…The sophisticated ML technique enables it to handle intricate situations that conventional models are unable to manage. Wang et al [ 18 ] designed a prediction model by screening characteristics from 22 clinical indicators and retrospectively analyzing the clinical data of 322 patients with renal pelvic effusion complicated by calculi. With an area under the curve (AUC) of 0.981, they revealed that the XGBoost model performed the best in terms of predictions on the training set.…”

Section: Introductionmentioning

confidence: 99%

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

Yang,

Zhong,

Yang

et al. 2024

Urolithiasis

0

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In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.

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“…In the opinion of Shi et al [34], CRP has a mediocre level of e cacy in determining sepsis brought on by a new coronavirus infection. It was found that CRP was an independent risk factor for the emergence of pyonephrosis and was essential in the construct of the ML-assisted diagnostic model of calculous pyonephrosis designed by Liu et al [35].…”

Section: Blood Characteristicsmentioning

confidence: 99%

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

Yang,

Zhong,

Yang

et al. 2024

Preprint

0

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In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.

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“…It can effectively deal with the nonlinear relationship and high-dimensional space in medical data, with high accuracy and good generalization in the field of urinary calculi, which outperform traditional modeling methods [ 12 ]. Machine learning has been applied in biomedical fields such as disease diagnosis, outcome prediction, medical image analysis, and therapeutics [ 13 , 14 ]. Therefore, in this study, we sought to develop machine learning models that can be used to differentiate infection and non-infection stones before necessary surgery is performed on patients with urinary stones to better guide perioperative management and prevent the occurrence of infection stones after surgery.…”

Section: Introductionmentioning

confidence: 99%

A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo

Wu

¹

,

Mo

²

,

Xie

³

et al. 2023

Urolithiasis

5

0

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Preoperative diagnosis of urinary infection stones is difficult, and accurate detection of stone composition can only be performed ex vivo. To provide guidance for better perioperative management and postoperative prevention of infection stones, we developed a machine learning model for preoperative identification of infection stones in vivo. The clinical data of patients with urolithiasis who underwent surgery in our hospital from January 2011 to December 2015 and January 2017 to December 2021 were retrospectively analyzed. A total of 2565 patients were included in the study, and 1168 eligible patients with urinary calculi were randomly divided into training set (70%) and test set (30%). Five machine learning algorithms (Support Vector Machine (SVM), Multilayer Perceptron (MLP), Decision Tree (DT), Random Forest Classifier (RFC), and Adaptive Boost (AdaBoost)) and 14 preoperative variables were used to construct the prediction model. The performance measure was the area under the receiver operating characteristic curve (AUC) of the validation set. The importance of 14 features in each prediction model for predicting infection stones was analyzed. A total of 89 patients (5.34%) with infection stones were included in the validation set. All the five prediction models showed strong discrimination in the validation set (AUC: 0.689–0.772). AdaBoost model was selected as the final model (AUC: 0.772(95% confidence interval, 0.657–0.887); Sensitivity: 0.522; Specificity: 0.902), UC positivity, and urine pH value were two important predictors of infection stones. We developed a predictive model through machine learning that can quickly identify infection stones in vivo with good predictive performance. It can be used for risk assessment and decision support of infection stones, optimize the disease management of urinary calculi and improve the prognosis of patients.

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Machine learning-assisted decision-support models to better predict patients with calculous pyonephrosis

Cited by 7 publications

References 37 publications

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo

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