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.
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.
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.
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.
Journal of clinical medicine. 2021 Aug 29*** epublish ***
Amelia Pietropaolo, Robert M Geraghty, Rajan Veeratterapillay, Alistair Rogers, Panagiotis Kallidonis, Luca Villa, Luca Boeri, Emanuele Montanari, Gokhan Atis, Esteban Emiliani, Tarik Emre Sener, Feras Al Jaafari, John Fitzpatrick, Matthew Shaw, Chris Harding, Bhaskar K Somani
Department of Urology, University Hospital Southampton, Southampton SO16 6YD, UK., Department of Urology, Freeman Hospital, Freeman Road, Newcastle-upon-Tyne NE1 7DN, UK., Department of Urology, University of Patras, 26504 Patras, Greece., IRCCS Ospedale San Raffaele, Urology, 20019 Milan, Italy., Department of Urology, IRCCS Fondazione Ca' Granda-Ospedale Maggiore Policlinico, University of Milan, 20019 Milan, Italy., Department of Urology, Faculty of Medicine, Istanbul Medeniyet University, Istanbul 34720, Turkey., Department of Urology, Fundació Puigvert, 08001 Barcelona, Spain., Department of Urology, Marmara University, Istanbul 34720, Turkey., Victoria Hospital, Kirkcaldy KY1 2ND, UK.