Impact of body mass index on success, complications and failure of extracorporeal shock wave lithotripsy

Beigi, Faramarz Mohammad Ali; Hafshejani, Zahra Keivani; Aghahoseini, Mohammadreza; Shirani, Majid

doi:10.15171/jrip.2019.41

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Kanao et al [ 61 ] created one of the first nomograms to predict stone-free rates based on 435 patients. While the nomogram considered stone size, stone location, and stone number, critics argued that their approach was inadequate [ 49 ] because it did not consider stone density and skin-to-stone distance (SSD).…”

Section: Discussionmentioning

confidence: 99%

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Noble,

Hamilton,

Gerber

2024

PLoS ONE

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Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.

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Section: Discussionmentioning

confidence: 99%

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Noble,

Hamilton,

Gerber

2024

PLoS ONE

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Machine Learning Models for Predicting the Type and Outcome of Ureteral Stones Treatments

Rashidi,

Langarizadeh,

Sayadi

et al. 2023

Advanced Biomedical Research

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Background:Kidney and ureter stones are the third pathologies in urological diseases. Less invasive treatments such as transureteral lithotripsy and extracorporeal shock wave lithotripsy are used to treat ureteral stones. Data mining has provided the possibility of improving decision-making in choosing the optimal treatment. In this paper predictive models for the detection of ureter stone treatment (first model) and its outcome (second model) is developed based on the patient’s demographic, clinical, and laboratory factors.Methods and Material:In this cross-sectional study a questionnaire was used to identify the most effective features in the predictive models, and Information on 440 patients was collected. The models were constructed using machine learning techniques (Multilayer perceptron, Classification, and regression tree, k-nearest neighbors, Support vector machine, Naïve Bayes classifier, Random Forest, and AdaBoost) in the Bigpro1 analytical system.Results:Among the Holdout and K-fold cross-validation methods used, the Holdout method showed better performance. From the data-based balancing methods used in the second model, the Synthetic Minority oversampling technique showed better performance. Also, the AdaBoost algorithm had the best performance. In this algorithm, accuracy, sensitivity, specificity, precision, F- measure, and Area under the carve in the first model were 89%, 87%, 91%, 90%, 89%, and 94% respectively, and in the second model were 81%, 81%, 82%, 84%, 82%, and 85% respectively.Conclusions:The results were promising and showed that the data mining techniques could be a powerful assistant for urologists to predict a surgical outcome and also to choose an appropriate surgical treatment for removing ureter stones.

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Impact of body mass index on success, complications and failure of extracorporeal shock wave lithotripsy

Cited by 2 publications

References 14 publications

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Machine Learning Models for Predicting the Type and Outcome of Ureteral Stones Treatments

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