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Lookup NU author(s): Dr Rob GeraghtyORCiD, Alistair Rogers
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2024 European Association of Urology. Background and objective: Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes. Few ML studies have examined percutaneous nephrolithotomy (PCNL) outcomes. Our objective was to build, streamline, temporally validate, and use ML models for prediction of PCNL outcomes (intensive care admission, postoperative infection, transfusion, adjuvant treatment, postoperative complications, visceral injury, and stone-free status at follow-up) using a comprehensive national database (British Association of Urological Surgeons PCNL). Methods: This was an ML study using data from a prospective national database. Extreme gradient boosting (XGB), deep neural network (DNN), and logistic regression (LR) models were built for each outcome of interest using complete cases only, imputed, and oversampled and imputed/oversampled data sets. All validation was performed with complete cases only. Temporal validation was performed with 2019 data only. A second round used a composite of the most important 11 variables in each model to build the final model for inclusion in the shiny application. We report statistics for prognostic accuracy. Key findings and limitations: The database contains 12 810 patients. The final variables included were age, Charlson comorbidity index, preoperative haemoglobin, Guy's stone score, stone location, size of outer sheath, preoperative midstream urine result, primary puncture site, preoperative dimercapto-succinic acid scan, stone size, and image guidance (https://endourology.shinyapps.io/PCNL_Demographics/). The areas under the receiver operating characteristic curve was >0.6 in all cases. Conclusions and clinical implications: This is the largest ML study on PCNL outcomes to date. The models are temporally valid and therefore can be implemented in clinical practice for patient-specific risk profiling. Further work will be conducted to externally validate the models. Patient summary: We applied artificial intelligence to data for patients who underwent a keyhole surgery to remove kidney stones and developed a model to predict outcomes for this procedure. Doctors could use this tool to advise patients about their risk of complications and the outcomes they can expect after this surgery.
Author(s): Geraghty RM, Thakur A, Howles S, Finch W, Fowler S, Rogers A, Sriprasad S, Smith D, Dickinson A, Gall Z, Somani BK
Publication type: Article
Publication status: Published
Journal: European Urology Focus
Year: 2024
Volume: 10
Issue: 2
Pages: 290-297
Print publication date: 01/03/2024
Online publication date: 01/02/2024
Acceptance date: 21/01/2024
Date deposited: 20/02/2024
ISSN (print): 0302-2838
ISSN (electronic): 2405-4569
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.euf.2024.01.011
DOI: 10.1016/j.euf.2024.01.011
PubMed id: 38307805
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