Predictive Modeling Is a Reliable Indicator in Determining Excessive Renal Mobility Single-Center Randomized Study

Basit Öğe Kaydı
dc.contributor.authorDogan, Cagri
dc.contributor.authorOzgur, Cihan
dc.contributor.authorSahin, Mehmet Fatih
dc.contributor.authorSiddikoglu, Duygu
dc.contributor.authorTopkac, Erdem Can
dc.contributor.authorYazici, Cenk Murat
dc.date.accessioned2025-01-27T20:55:47Z
dc.date.available2025-01-27T20:55:47Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractPurpose: Excessive kidney mobility is an underestimating challenge for surgeons during retrograde intrarenal surgery (RIRS) and extracorporeal shock wave lithotripsy (ESL). There is no technique approved as a gold standard procedure for reducing excessive kidney mobility. The study aimed to uncover predictive factors for determining excessive renal mobility by utilizing clinicodemographic characteristics and noncontrast computed tomography (NCCT) data. Materials and Methods: The patients were categorized into two groups based on the presence of excessive renal mobility. Patients were scanned with a 16-channel, multislice NCCT, and images were captured utilizing a 16 x 1.25 mm collimation, 5 mm slice thickness. Many parameters including the origin angle of the renal artery, renal artery, vein length, diameter, the area and length of the psoas muscle, and perirenal and pararenal fatty tissue were measured on the images and analyzed. The data were analyzed using multivariate logistic regression, and the receiver operating characteristic curve model and we used predictive modeling based on three significant parameters. Results: Between May 2023 and May 2024, a total of 140 patients with and without excessive renal mobility enrolled into study. After multivariate analysis, increasing renal vein length and renal artery origin angle results in higher renal motility (odds ratio [OR]: 0.982; 95% confidence interval [CI]: 0.966-0.998; p = 0.030 and OR: 0.973; 95% CI: 0.948-0.999; p = 0.044; respectively). It also observed that an increase in tidal volume led to a reduction in renal mobility (OR: 1.015; 95% CI: 1.007-1.024; p = 0.001). Predictive modeling was designed based on these outcomes. This predictive modeling accurately estimates the presence of excessive renal mobility with improved 59% specificity and 65% sensitivity (p < 0.001, area under the curve 0.757; CI: 0.671-0.843). Conclusion: Physicians may predict the presence of excessive renal mobility via the predictive modeling mentioned in the current article. They may perform manipulations to reduce kidney mobility prior to ESL and RIRS.
dc.identifier.doi10.1089/end.2024.0481
dc.identifier.issn0892-7790
dc.identifier.issn1557-900X
dc.identifier.pmid39791221
dc.identifier.scopus2-s2.0-85214487692
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1089/end.2024.0481
dc.identifier.urihttps://hdl.handle.net/20.500.12428/26179
dc.identifier.wosWOS:001394804500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMary Ann Liebert, Inc
dc.relation.ispartofJournal of Endourology
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectrenal mobility
dc.subjectpredictive modeling
dc.subjectRIRS
dc.subjectESL
dc.subjectrenal artery origin angle
dc.titlePredictive Modeling Is a Reliable Indicator in Determining Excessive Renal Mobility Single-Center Randomized Study
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu