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.

The authors have declared no competing interest.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study used data from a previously published national audit (https://doi.org/10.1016/j.eururo.2012.01.003). Access to this dataset was applied for and granted by the British Association of Urological Surgeons. As an audit it does not require Ethical approval as per Health Research Authority (UK).

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

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All data produced in the present study are available upon application to the British Association of Urological Surgeons