SIU-WJU Article of the Month – October 2018
Urethroplasty: predicting complications in a procedure with low complication rate
SIU Academy®. Zuckerman J. 10/01/18; 234254 Topic: Strictures
Dr. Jack Zuckerman
Dr. Jack Zuckerman
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Abstract
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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
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
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