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

Çağri Doğan1, Cihan Özgür2, Mehmet Fatih Sahin1

  • 1Faculty of Medicine, Department of Urology, Namik Kemal University, Tekirdag, Turkey.

Journal of Endourology
|January 10, 2025
PubMed
Summary
This summary is machine-generated.

Excessive kidney mobility complicates surgery. New predictive modeling using renal vein length and renal artery angle can help surgeons anticipate and manage kidney movement during procedures like RIRS and ESL.

Keywords:
ESLRIRSpredictive modelingrenal artery origin anglerenal mobility

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Area of Science:

  • Nephrology
  • Urology
  • Surgical Imaging

Background:

  • Excessive kidney mobility poses a significant challenge during retrograde intrarenal surgery (RIRS) and extracorporeal shock wave lithotripsy (ESL).
  • Currently, no standardized technique exists to effectively manage or reduce excessive renal mobility.
  • Identifying predictive factors for renal mobility is crucial for surgical planning and patient outcomes.

Purpose of the Study:

  • To identify clinicodemographic and noncontrast computed tomography (NCCT) predictors of excessive renal mobility.
  • To develop a predictive model for estimating the likelihood of excessive renal mobility.
  • To aid surgeons in anticipating and managing kidney mobility during urological procedures.

Main Methods:

  • A study involving 140 patients categorized by the presence or absence of excessive renal mobility.
  • NCCT scans were analyzed for parameters including renal artery/vein dimensions, psoas muscle characteristics, and surrounding fatty tissue.
  • Multivariate logistic regression and receiver operating characteristic (ROC) curve analysis were employed to develop a predictive model.

Main Results:

  • Increased renal vein length and renal artery origin angle were significantly associated with higher renal mobility (p < 0.05).
  • Increased tidal volume was found to reduce renal mobility (p = 0.001).
  • The developed predictive model demonstrated 59% specificity and 65% sensitivity in estimating excessive renal mobility (AUC = 0.757, p < 0.001).

Conclusions:

  • A novel predictive model can estimate the presence of excessive renal mobility using specific NCCT parameters.
  • This model allows physicians to anticipate kidney mobility issues before procedures like RIRS and ESL.
  • The findings enable proactive surgical planning and potential interventions to mitigate risks associated with kidney mobility.