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Use of Internally Validated Machine and Deep Learning Models to Predict Outcomes of Percutaneous Nephrolithotomy using data from the BAUS PCNL audit

Background: Machine (ML) and Deep learning (DL) are subsets of artificial intelligence that use data to build algorithms. These can be used to predict specific outcomes. To date there have been a few small studies on post-PCNL outcomes. Objective: We aimed to build and internally validate ML/DL models for post-PCNL transfusion and infection using a comprehensive national database. Design: Machine Learning study using prospective national database. Eight machine learning models for 11 outcomes using 43 predictors. Models were complete-case analyses. Setting: National database Participants: Patients undergoing PCNL in the UK between 2014-2019. Outcome Measurements: Diagnostic accuracy statistics including overall accuracy, area-under-the-curve (AUC), sensitivity and specificity. Results and Limitations: 4412 patients were included, with 3088 in the training set and 1324 in the test set. The models predicted need for transfusion and post-operative infection with a very high degree of accuracy (99%) and high AUC (0.99-1.00). Unfortunately, the remainder of the outcomes did not achieve the same high levels. These two outcomes were therefore included in the provisional web-based application: https://endourology.shinyapps.io/PCNL_Prediction_tool/ Conclusions: This is the largest machine learning study on post-PCNL outcomes to date. These models can predict the need for post-PCNL transfusion and post-PCNL infection at an individual level with excellent accuracy. Further work will be done on model tuning and external validation. Patient Summary: We used a national database of people having a major kidney stone operation (PCNL). Using this data, we built and tested 8 machine learning models for 11 different outcomes from the operation. Using this method, we can give individual predictions for the likely need for a blood transfusion and development of an infection. We have developed an app to allow surgeons to calculate an individual patients risk prior to surgery.