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, A. (University Hospital Southampton. Department of Urology)
Geraghty, R.M. (Freeman Hospital (Newcastle, Regne Unit))
Veeratterapillay, R. (Freeman Hospital (Newcastle, Regne Unit))
Rogers, Alistair (Freeman Hospital (Newcastle, Regne Unit))
Kallidonis, P. (University of Patras. Department of Urology)
Villa, L. (IRCCS Ospedale San Raffaele. Urology)
Boeri, Luca (IRCCS Fondazione Ca Granda Ospedale Maggiore Policlinico (Milà, Itàlia))
Montanari, E. (IRCCS Fondazione Ca' Granda-Ospedale Maggiore Policlinico. University of Milan)
Atis, G. (Istanbul Medeniyet University. Department of Urology)
Emiliani, Esteban (Institut d'Investigació Biomèdica Sant Pau)
Sener, T.E. (Marmara University. Department of Urology)
Al Jaafari, F. (Victoria Hospital)
Fitzpatrick, J. (Freeman Hospital (Newcastle, Regne Unit))
Shaw, M. (Freeman Hospital (Newcastle, Regne Unit))
Harding, C. (Freeman Hospital (Newcastle, Regne Unit))
Somani, B.K. (University Hospital Southampton. Department of Urology)

Date:	2021
Abstract:	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.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Kidney stones ; Urosepsis ; Ureteroscopy ; Laser lithotripsy ; Urolithiasis ; Nephrolithiasis ; Kidney calculi ; Predictor factors
Published in:	Journal of clinical medicine, Vol. 10 Núm. 17 (january 2021) , p. 3888, ISSN 2077-0383

DOI: 10.3390/jcm10173888
PMID: 34501335

10 p, 927.8 KB

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