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Published on: December 23, 2022
Development of a nomogram model to predict postoperative urinary retention risk after transabdominal preperitoneal
Jiwei Wang1, Kanghu Li2, Guangyuan Wang2
1Department of General Surgery, Digestive Disease Hospital, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
Objective:
To explore independent risk factors and to establish a predictive model for postoperative urinary retention (POUR) following transabdominal preperitoneal inguinal hernia repair (TAPP).
Methods:
Between January 2017 and December 2023, 598 patients with inguinal hernia who underwent TAPP at the General Surgery Department of Zunyi Medical University Affiliated Liupanshui Hospital were enrolled in the study. Participants were randomly divided into training and validation sets (7:3 ratio). The training set was further divided into POUR and non-POUR groups (38 and 381 cases, respectively). Independent risk factors for POUR were screened by logistic regression analysis. A nomogram predictive model was established based on the independent risk factors. The model's predictive ability was evaluated.
Results:
Logistic regression revealed recurrent hernia (P = 0.013, OR = 0.279, 95% confidence interval [CI]: 0.105-0.805), BPH (P = 0.002, OR = 5.09, 95% CI: 2.658-96.282), preoperative catheterization (P = 0.004, OR = 0.087, 95% CI: 0.012-0.370), operation time (P < 0.001, OR = 0.015, 95% CI: 1.006-1.023), and use of narcotic analgesics (P = 0.042, OR = 2.224, 95% CI: 1.021-5.222) as independent risk factors for POUR after TAPP. Discriminant analysis showed the training set AUC was 0.763 (95% CI: 0.684-0.842, P < 0.001) while that for the validation set was 0.812 (95% CI: 0.677-0.947, P < 0.001). The Hosmer-Lemeshow test was performed to assess calibration of the training (P = 0.696) and validation (P = 0.194) sets.
Conclusion:
Recurrent hernia, BPH, preoperative catheterization, operation time, and use of narcotic analgesics were identified as independent risk factors for POUR after TAPP. The nomogram model for risk prediction based on these factors has strong predictive performance and application prospect.
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