A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case-Control YAU Endourology Study from Nine European Centres
Por:
Pietropaolo, A, Geraghty, RM, Veeratterapillay, R, Rogers, A, Kallidonis, P, Villa, L, Boeri, L, Montanari, E, Atis, G, Emiliani, E, Sener, TE, Al Jaafari, F, Fitzpatrick, J, Shaw, M, Harding, C, Somani, BK
Publicada:
1 sep 2021
Resumen:
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.
Filiaciones:
Pietropaolo, A:
Univ Hosp Southampton, Dept Urol, Southampton SO16 6YD, Hants, England
Geraghty, RM:
Freeman Rd Hosp, Dept Urol, Freeman Rd, Newcastle Upon Tyne NE1 7DN, Tyne & Wear, England
Veeratterapillay, R:
Freeman Rd Hosp, Dept Urol, Freeman Rd, Newcastle Upon Tyne NE1 7DN, Tyne & Wear, England
Rogers, A:
Freeman Rd Hosp, Dept Urol, Freeman Rd, Newcastle Upon Tyne NE1 7DN, Tyne & Wear, England
Kallidonis, P:
Univ Patras, Dept Urol, Patras 26504, Greece
Villa, L:
IRCCS Osped San Raffaele, Urol, I-20019 Milan, Italy
Boeri, L:
Univ Milan, IRCCS Fdn Ca Granda Osped Maggiore Policlin, Dept Urol, I-20019 Milan, Italy
Montanari, E:
Univ Milan, IRCCS Fdn Ca Granda Osped Maggiore Policlin, Dept Urol, I-20019 Milan, Italy
Atis, G:
Istanbul Medeniyet Univ, Fac Med, Dept Urol, TR-34720 Istanbul, Turkey
Emiliani, E:
Fundacio Puigvert, Dept Urol, Barcelona 08001, Spain
Sener, TE:
Marmara Univ, Dept Urol, TR-34720 Istanbul, Turkey
Al Jaafari, F:
Victoria Hosp, Kirkcaldy KY1 2ND, Scotland
Fitzpatrick, J:
Freeman Rd Hosp, Dept Urol, Freeman Rd, Newcastle Upon Tyne NE1 7DN, Tyne & Wear, England
Shaw, M:
Freeman Rd Hosp, Dept Urol, Freeman Rd, Newcastle Upon Tyne NE1 7DN, Tyne & Wear, England
Harding, C:
Freeman Rd Hosp, Dept Urol, Freeman Rd, Newcastle Upon Tyne NE1 7DN, Tyne & Wear, England
Somani, BK:
Univ Hosp Southampton, Dept Urol, Southampton SO16 6YD, Hants, England
Green Published, gold
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