A prediction model for postoperative urinary retention after thoracic surgery.

Area of Science:

• Thoracic Surgery
• Urology
• Medical Informatics

Background:

• Postoperative urinary retention (POUR) is a common complication after general thoracic surgery (GTS).
• POUR leads to patient discomfort and can delay hospital discharge.
• Predicting and preventing POUR is crucial for improving patient outcomes in GTS.

Purpose of the Study:

• To develop a predictive model for postoperative urinary retention (POUR) in general thoracic surgery (GTS) patients.
• To prospectively validate the accuracy of the developed predictive model.
• To identify key risk factors associated with the occurrence of POUR.

Main Methods:

• Retrospective development of a predictive model using the Society of Thoracic Surgeons GTS Database.
• Multivariable logistic regression analysis to identify risk factors for POUR.
• Prospective validation of the model in a separate GTS patient cohort using ROC analysis.

Main Results:

• The predictive model was developed from 1484 GTS patients; 19% experienced POUR.
• Identified risk factors include older age, male sex, higher creatinine, COPD, diagnosis, procedure, and PCA use.
• The model demonstrated favorable predictive accuracy with similar c-statistics in development (0.77) and validation (0.72) cohorts.

Conclusions:

• Postoperative urinary retention (POUR) impacts nearly 20% of patients undergoing major GTS.
• A validated predictive model can aid in identifying high-risk patients.
• Targeted prophylactic measures can help prevent POUR in susceptible GTS patients.