Objective To construct and verify a nomogram model for predicting social urinary control recovery in patients undergoing robot-assisted radical prostatectomy (RARP) immediately after extubation.
Methods A retrospective analysis was conducted on the clinical data of 64 patients diagnosed with prostate cancer and treated by a single surgeon. The immediate urinary control status of the patients after removal of the catheter was evaluated, and LASSO regression was used for feature screening. Multiple Logistic regression was performed on the selected features to determine independent risk factors and establish a predictive model. And the discriminability, calibration, and clinical practicality of the model were evaluated using receiver operating curve (ROC), Hosmer Lemeshow test and calibration curve, and clinical decision curve (DCA) analysis.
Results The variables in the outcome prediction model include D'Amico grading and distance of the levator muscle. The area under the ROC curve (AUC) was 0.742 (95%CI, 0.500 to 0.913, P < 0.001), indicating that the model had good discriminability. The calibration curve indicated that the model had good calibration ability. The DCA curve showed good clinical practicality.
Conclusion The clinical predictive model developed inthis study can predict the recovery of immediate social urinary control in patients with RARP after surgery, which can further quantify the probability of achieving immediate social continence.
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