Publication:
A new scoring system to predict febrile urinary tract infection after retrograde intrarenal surgery

dc.contributor.coauthorSenel, Cagdas
dc.contributor.coauthorErkan, Anil
dc.contributor.coauthorKeten, Tanju
dc.contributor.coauthorOzercan, Ali Yasin
dc.contributor.coauthorTatlici, Koray
dc.contributor.coauthorBasboga, Serdar
dc.contributor.coauthorSaracli, Sinan
dc.contributor.coauthorGuzel, Ozer
dc.contributor.coauthorTuncel, Altug
dc.contributor.departmentKUH (Koç University Hospital)
dc.contributor.kuauthorAykanat, İbrahim Can
dc.contributor.schoolcollegeinstituteKUH (KOÇ UNIVERSITY HOSPITAL)
dc.date.accessioned2025-03-06T21:01:27Z
dc.date.issued2024
dc.description.abstractThe current study aimed to determine the risk factors and define a new scoring system for predicting febrile urinary tract infection (F-UTI) following retrograde intrarenal surgery (RIRS) by using machine learning methods. We retrospectively analyzed the medical records of patients who underwent RIRS and 511 patients were included in the study. The patients were divided into two groups: Group 1 consisted of 34 patients who developed postoperative F-UTI, and Group 2 consisted of 477 patients who did not. We applied feature selection to determine the relevant variables. Consistency subset evaluator and greedy stepwise techniques were used for attribute selection. Logistic regression analysis was conducted on the variables obtained through feature selection to develop our scoring system. The accuracy of discrimination was assessed using the receiver operating characteristic curve. Five of the 19 variables, namely diabetes mellitus, hydronephrosis, administration type, a history of post-ureterorenoscopy (URS) UTI, and urine leukocyte count, were identified through feature selection. Binary logistic regression analysis showed that hydronephrosis, a history of post-URS UTI, and urine leukocyte count were significant independent predictors of F-UTI following RIRS. These three factors demonstrated good discrimination ability, with an area under curve value of 0.837. In the presence of at least one of these factors, 32 of 34 patients (94.1%) who developed postoperative F-UTI were successfully predicted. This new scoring system developed based on hydronephrosis, a history of post-URS UTI, and urine leukocyte count can successfully discriminate patients at risk of F-UTI development after RIRS.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.identifier.doi10.1007/s00240-024-01685-x
dc.identifier.issn2194-7228
dc.identifier.issue1
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85212857762
dc.identifier.urihttps://doi.org/10.1007/s00240-024-01685-x
dc.identifier.urihttps://hdl.handle.net/20.500.14288/27989
dc.identifier.volume53
dc.identifier.wos1382990900001
dc.keywordsRetrograde intrarenal surgery
dc.keywordsInfection
dc.keywordsComplication
dc.keywordsScoring system
dc.keywordsMachine learning
dc.keywordsFeature selection
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofUrolithiasis
dc.subjectUrology and nephrology
dc.titleA new scoring system to predict febrile urinary tract infection after retrograde intrarenal surgery
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorAykanat, İbrahim Can
local.publication.orgunit1KUH (KOÇ UNIVERSITY HOSPITAL)
local.publication.orgunit2KUH (Koç University Hospital)
relation.isOrgUnitOfPublicationf91d21f0-6b13-46ce-939a-db68e4c8d2ab
relation.isOrgUnitOfPublication.latestForDiscoveryf91d21f0-6b13-46ce-939a-db68e4c8d2ab
relation.isParentOrgUnitOfPublication055775c9-9efe-43ec-814f-f6d771fa6dee
relation.isParentOrgUnitOfPublication.latestForDiscovery055775c9-9efe-43ec-814f-f6d771fa6dee

Files