Save
SIU-WJU Article of the Month – October 2018
Urethroplasty: predicting complications in a procedure with low complication rate
SIU Academy®. Zuckerman J. Oct 1, 2018; 234254
Dr. Jack Zuckerman
Dr. Jack Zuckerman
Login now to access Regular content available to all registered users.

To have an exclusive access to the entire content available on SIU Academy, become an SIU Member here.

You may also access SIU content "anytime, anywhere" with the FREE SIU ACADEMY App for iOS and Android.
Abstract
Discussion Forum (0)
Rate & Comment (0)
Introduction and objective

To identify predictors of thirty-day perioperative complications after urethroplasty and create a model to predict patients at increased risk.

Methods

We selected all patients recorded in the National Surgery Quality Improvement Program (NSQIP) from 2005 to 2015 who underwent urethroplasty, determined by Current Procedural Terminology (CPT) codes. The primary outcome of interest was a composite 30-day complication rate. To develop predictive models of urethroplasty complications we used random forest and logistic regression with tenfold cross-validation employing demographic, comorbidity, laboratory, and wound characteristics as candidate predictors. Models were selected based on the receiver operating characteristic (ROC) curve, with the primary measure of performance being the area under curve (AUC).

Results

We identified 1135 patients who underwent urethroplasty and met inclusion criteria. The mean age was 53years with 84% being male. The overall incidence of complications was 8.6% (n = 98). Patients who experienced a complication more commonly had diabetes, a preoperative blood transfusion, preoperative sepsis, lower hematocrit and albumin, as well as a longer operative time (p < 0.05). LASSO logistic and random forest logistic models for predicting urethroplasty com-plications had an AUC (95% CI) 0.73 (0.58–0.87), and 0.48 (0.33–0.68), respectively. The variables that were determined to be most important and included in the predictive models were operative time, age, American Society of Anesthesiologists (ASA) classification and preoperative laboratory values (white blood cell count, hematocrit, creatinine, platelets).

Conclusion

Our predictive models of complications perform well and may allow for improved preoperative counseling and risk stratification in the surgical management of urethral stricture.

Keywords

Urethroplasty | Perioperative complications | Risk stratification | Predictive model | Risk calculator
Code of conduct/disclaimer available in General Terms & Conditions
Anonymous User Privacy Preferences

Strictly Necessary Cookies (Always Active)

MULTILEARNING platforms and tools hereinafter referred as “MLG SOFTWARE” are provided to you as pure educational platforms/services requiring cookies to operate. In the case of the MLG SOFTWARE, cookies are essential for the Platform to function properly for the provision of education. If these cookies are disabled, a large subset of the functionality provided by the Platform will either be unavailable or cease to work as expected. The MLG SOFTWARE do not capture non-essential activities such as menu items and listings you click on or pages viewed.


Performance Cookies

Performance cookies are used to analyse how visitors use a website in order to provide a better user experience.



Google Analytics is used for user behavior tracking/reporting. Google Analytics works in parallel and independently from MLG’s features. Google Analytics relies on cookies and these cookies can be used by Google to track users across different platforms/services.


Save Settings