Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: Software Validation and Comparative Analysis with Guy's Stone Score and the CROES Nomogram

Aminsharifi, Alireza; Irani, Dariush; Tayebi, Sona; Kafash, Taher Jafari; Shabanian, Tayebeh; Parsaei, Hossein

doi:10.1089/end.2019.0475

Cited by 60 publications

(42 citation statements)

References 32 publications

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“…Aminsharifi et al [21] analysed data of 146 adult patients who underwent percutaneous nephrolithotomy (PCNL) to validate the efficiency of an ML algorithm for predicting the outcomes after PCNL. This program predicted the PCNL results with an accuracy of up to 95%.…”

Section: Comparison With Other ML Studiesmentioning

confidence: 99%

A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case–Control YAU Endourology Study from Nine European Centres

Pietropaolo

Geraghty

Veeratterapillay

et al. 2021

JCM

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Introduction: With the rise in the use of ureteroscopy and laser stone lithotripsy (URSL), a proportionate increase in the risk of post-procedural urosepsis has also been observed. The aims of our paper were to analyse the predictors for severe urosepsis using a machine learning model (ML) in patients that needed intensive care unit (ICU) admission and to make comparisons with a matched cohort. Methods: A retrospective study was conducted across nine high-volume endourology European centres for all patients who underwent URSL and subsequently needed ICU admission for urosepsis (Group A). This was matched by patients with URSL without urosepsis (Group B). Statistical analysis was performed with ‘R statistical software’ using the ‘randomforests’ package. The data were segregated at random into a 70% training set and a 30% test set using the ‘sample’ command. A random forests ML model was then built with n = 300 trees, with the test set used for internal validation. Diagnostic accuracy statistics were generated using the ‘caret’ package. Results: A total of 114 patients were included (57 in each group) with a mean age of 60 ± 16 years and a male:female ratio of 1:1.19. The ML model correctly predicted risk of sepsis in 14/17 (82%) cases (Group A) and predicted those without urosepsis for 12/15 (80%) controls (Group B), whilst overall it also discriminated between the two groups predicting both those with and without sepsis. Our model accuracy was 81.3% (95%, CI: 63.7–92.8%), sensitivity = 0.80, specificity = 0.82 and area under the curve = 0.89. Predictive values most commonly accounting for nodal points in the trees were a large proximal stone location, long stent time, large stone size and long operative time. Conclusion: Urosepsis after endourological procedures remains one of the main reasons for ICU admission. Risk factors for urosepsis are reasonably accurately predicted by our innovative ML model. Focusing on these risk factors can allow one to create predictive strategies to minimise post-operative morbidity.

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Section: Comparison With Other ML Studiesmentioning

confidence: 99%

A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case–Control YAU Endourology Study from Nine European Centres

Pietropaolo

Geraghty

Veeratterapillay

et al. 2021

JCM

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“…The results obtained are comparable to other studies, and systems were developed based on five different classifiers, namely SVM, LR, DT, RF, and K Means. [12][13][14][15] The best accuracy in predicting the stone-free status of a kidney after the first treatment was 81%, provided by the RF classifier. Previous studies have already proven that using multiple classifier systems perform better than a single classifier.…”

Section: Ai To Predict Stone-free Status After Pcnlmentioning

confidence: 99%

“…Keeping these limitations in mind, studies have been performed to predict the outcomes of PCNL in the management of renal stones using artificial intelligence (AI) models with satisfactory results. [12][13][14][15] In our study, we used various AI (machine learning [ML]) models to develop a decision support system (DSS) for prediction of postoperative outcomes following a PCNL procedure and to compare the results of each of these models. To the best of our knowledge, this is the first report of the use of an AI-based system to evaluate PCNL outcomes in partial and complete staghorn calculi.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence-Based Classifiers to Predict the Outcome Measures and Stone-Free Status Following Percutaneous Nephrolithotomy for Staghorn Calculi: Cross-Validation of Data and Estimation of Accuracy

Hameed

Shah

Naik

et al. 2021

Journal of Endourology

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Objective: To develop a decision support system (DSS) for the prediction of the postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL) to serve as a promising tool to provide counseling before an operation. Materials and Methods: The overall procedure includes data collection and prediction model development. Pre-/postoperative variables of 100 patients with staghorn calculus, who underwent PCNL, were collected. For feature vector, variables and categories including patient history variables, kidney stone parameters, and laboratory data were considered. The prediction model was developed using machine learning techniques, which include dimensionality reduction and supervised classification. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running the leave-one-patient-out cross-validation approach on the data set. Results: The system provided favorable accuracy (81%) in predicting the outcome of a treatment procedure. Performance in predicting the stone-free rate with the Minimum Redundancy Maximum Relevance feature (MRMR) treatment extracting top 3 features using Random Forest (RF) was 67%, with MRMR treatment extracting top 5 features using RF was 63%, and with MRMR treatment extracting top 10 features using Decision Tree was 62%. The statistical significance using standard error between the best area under the curves (AUCs) obtained from the Linear Discriminant Analysis (LDA) and MRMR. The results obtained from the LDA approach (0.81 AUC) was statistically significant ( p = 0.027, z = 2.21) from the MRMR (0.64 AUC) ( p = 0.05). Conclusion:The promising results of the developed DSS could be used in assisting urologists to provide counseling, predict a surgical outcome, and ultimately choose an appropriate surgical treatment for removing kidney stones.

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Section: Introductionmentioning

confidence: 99%

“…In predicting post-lithotripsy outcomes with machine learming, there were only three studies published, until now [12,13,14] . Alireza et al [12] rst used machine learning method for the predicting post-PCNL outcomes compared to current scoring systems. They found the machine learning-based software was superior in predicting SFS after PCNL, with an AUC of 0.915 compared to 0.615 ( GSS ) and 0.621 ( CROES nomograms ) ( P < 0.01).…”

Section: Introductionmentioning

confidence: 99%

Predicting Stone-Free Status of Percutaneous Nephrolithtomy With Machine Learning System: Comparative Analysis With Guy’s Stone Score and S.T.O.N.E Score System

Zhao

et al. 2021

Preprint

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Purpose Using machine learning methods (MLMs) to predict stone-free status after percutaneous nephrolithotomy (PCNL). We compared the performance of this system with Guy’s stone score and S.T.O.N.E score system. Materials and Methods Data from 222 patients (90 females, 41%) who underwent PCNL at our center were used. Twenty-six parameters, including individual variables, renal and stone factors, surgical factors were used as input data for MLMS. We evaluate the efficacy of four different techniques: Lasso-logistic (LL), random forest (RF), support vector machine (SVM) and Naive Bayes. Model performance was evaluated using area under curve (AUC) and compared with Guy’s stone score and S.T.O.N.E score system. Results Overall stone free rate was 50% (111/222). To predict the stone-free status, all receiver operating characteristic curves of the four MLMs were above the curve for the Guy’s stone score. The AUCs of LL, RF, SVM and Naive Bayes were 0.879, 0.803, 0.818, 0.803 respectively. Those values were higher than the AUC of the Guy’s score system, 0.800. The accuracies of the MLMs (0.803–0.818%) were also superior to S.T.O.N.E score system (0.788%). Among the MLMs, Lasso-logistic showed the most favorable AUC. Conclusion Machine learning methods can predict stone-free rate with AUCs no inferior to those of Guy’s stone score and S.T.O.N.E score system.

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Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: Software Validation and Comparative Analysis with Guy's Stone Score and the CROES Nomogram

Cited by 60 publications

References 32 publications

A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case–Control YAU Endourology Study from Nine European Centres

A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case–Control YAU Endourology Study from Nine European Centres

Application of Artificial Intelligence-Based Classifiers to Predict the Outcome Measures and Stone-Free Status Following Percutaneous Nephrolithotomy for Staghorn Calculi: Cross-Validation of Data and Estimation of Accuracy

Predicting Stone-Free Status of Percutaneous Nephrolithtomy With Machine Learning System: Comparative Analysis With Guy’s Stone Score and S.T.O.N.E Score System

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