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已发表论文
基于多参数分析的经会阴前列腺穿刺术后疼痛风险预测列线图的开发及外部验证
Authors Xu Y, Li X, Zhang Y, Xue P
Received 27 June 2025
Accepted for publication 18 November 2025
Published 5 December 2025 Volume 2025:18 Pages 6545—6561
DOI https://doi.org/10.2147/JPR.S543761
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Jinlei Li
Yang Xu,1,* Xin Li,2,* Yang Zhang,1 Peng Xue1
1Department of Urology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People’s Hospital of Lianyungang, Lianyungang, Jiangsu, 222000, People’s Republic of China; 2Department of Urology, Zhengzhou University Affiliated Zhengzhou Central Hospital, Zhengzhou, HeNan, 450000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Peng Xue, Department of Urology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People’s Hospital of Lianyungang, Lianyungang, Jiangsu, 222000, People’s Republic of China, Tel +86-0515-85605248, Email xuepeng@njmu.edu.cn
Purpose: To explore factors associated with moderate-to-severe pain (NRS > 4) in patients undergoing ultrasound-guided transperineal prostate biopsy (TPB), and to establish and validate a nomogram model for risk assessment.
Patients and Methods: This study included 520 patients who underwent ultrasound-guided TPB at the First People’s Hospital of Lianyungang City from September 2022 to December 2024. A training group (n = 400) and a validation group (n = 120) were established based on the admission time. Data collection included demographics, admission comorbidities, laboratory tests, imaging examinations, biopsy data, anxiety scores, and pain scores. Binary logistic regression was used to identify factors influencing moderate-to-severe pain (NRS > 4). A nomogram-based risk assessment model was constructed, with a validation model created to verify the training group. Additionally, a web-based dynamic nomogram risk assessment model was developed, and 120 patients from external hospitals were included for external validation.
Results: Univariate analysis identified factors with statistical significance. Based on binary Logistic regression analysis, prostate volume, anxiety score, history of diabetes, biopsy time, and number of biopsy needles were risk factors, while age was a protective factor (P < 0.05). The nomogram-based risk assessment model demonstrated favorable predictive accuracy, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.940 [95% CI: 0.914– 0.967] in the training group and 0.893 [95% CI: 0.834– 0.951] in the internal validation group. External validation further confirmed robust predictive capability (AUC = 0.888 [95% CI: 0.825– 0.951]). Additionally, decision curve analysis indicated clinically meaningful net benefits.
Conclusion: This nomogram-based risk stratification tool offers a robust framework for personalized perioperative pain management in patients undergoing TPB. Furthermore, external validation further supports the model’s applicability.
Keywords: biopsy, prostate tumor, cancer, pain, nomogram
1Department of Urology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People’s Hospital of Lianyungang, Lianyungang, Jiangsu, 222000, People’s Republic of China; 2Department of Urology, Zhengzhou University Affiliated Zhengzhou Central Hospital, Zhengzhou, HeNan, 450000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Peng Xue, Department of Urology, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People’s Hospital of Lianyungang, Lianyungang, Jiangsu, 222000, People’s Republic of China, Tel +86-0515-85605248, Email xuepeng@njmu.edu.cn
Purpose: To explore factors associated with moderate-to-severe pain (NRS > 4) in patients undergoing ultrasound-guided transperineal prostate biopsy (TPB), and to establish and validate a nomogram model for risk assessment.
Patients and Methods: This study included 520 patients who underwent ultrasound-guided TPB at the First People’s Hospital of Lianyungang City from September 2022 to December 2024. A training group (n = 400) and a validation group (n = 120) were established based on the admission time. Data collection included demographics, admission comorbidities, laboratory tests, imaging examinations, biopsy data, anxiety scores, and pain scores. Binary logistic regression was used to identify factors influencing moderate-to-severe pain (NRS > 4). A nomogram-based risk assessment model was constructed, with a validation model created to verify the training group. Additionally, a web-based dynamic nomogram risk assessment model was developed, and 120 patients from external hospitals were included for external validation.
Results: Univariate analysis identified factors with statistical significance. Based on binary Logistic regression analysis, prostate volume, anxiety score, history of diabetes, biopsy time, and number of biopsy needles were risk factors, while age was a protective factor (P < 0.05). The nomogram-based risk assessment model demonstrated favorable predictive accuracy, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.940 [95% CI: 0.914– 0.967] in the training group and 0.893 [95% CI: 0.834– 0.951] in the internal validation group. External validation further confirmed robust predictive capability (AUC = 0.888 [95% CI: 0.825– 0.951]). Additionally, decision curve analysis indicated clinically meaningful net benefits.
Conclusion: This nomogram-based risk stratification tool offers a robust framework for personalized perioperative pain management in patients undergoing TPB. Furthermore, external validation further supports the model’s applicability.
Keywords: biopsy, prostate tumor, cancer, pain, nomogram