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已发表论文
腹腔镜肾部分切除术后患者深静脉血栓形成风险预测列线图的开发和验证
Authors Wu C, Li Y, Wang X, Pan H, Jiang D
Received 10 April 2025
Accepted for publication 29 August 2025
Published 12 January 2026 Volume 2026:18 533674
DOI https://doi.org/10.2147/CMAR.S533674
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Yong Teng
Chunyan Wu,1 Yujia Li,2,3 Xianglian Wang,2,3 Hanlin Pan,1 Duoyun Jiang2,3
1School of Nursing, Yongzhou Vocational Technical College, Yongzhou, Hunan, 425100, People’s Republic of China; 2Department of Urinary Surgery, The Central Hospital of Yongzhou, Yongzhou, Hunan, 425000, People’s Republic of China; 3Department of Urinary Surgery, Yongzhou Hospital Affiliated to University of South China, Yongzhou, Hunan, 425000, People’s Republic of China
Correspondence: Duoyun Jiang, Department of Urinary Surgery, The Central Hospital of Yongzhou, 151 Xiaoshui West Road, Lingling District, Yongzhou, Hunan, 425100, People’s Republic of China, Tel +8615807463629, Email a15116844468@126.com
Objective: To construct a nomogram model for individualized prediction of risk of deep vein thrombosis (DVT) in renal cell carcinoma (RCC) patients after laparoscopic partial nephrectomy (PN).
Methods: A retrospective study was conducted on 556 RCC patients admitted to our hospital from January 2015 to January 2025. Patients were randomly divided into a modeling group (n=389) and a validation group (n=167) at a ratio of 7:3. The modeling group was further subdivided into DVT and non-DVT groups based on postoperative DVT occurrence. Clinical data were collected. Logistic regression was used to analyze the influencing factors. R software was used to construct the nomogram. ROC curves were generated to evaluate the discrimination ability of the nomogram. Decision curve analysis (DCA) was performed to assess its clinical utility.
Results: Of 556 patients, 100 (17.98%) developed DVT, including 70 (17.99%) in the modeling group. Logistic regression identified age, operation time, time in bed, pneumoperitoneum pressure, duration of indwelling drains, total cholesterol (TC), and D-dimer as significant risk factors (P < 0.05). The area under the curve (AUC) was 0.889 for the modeling group and 0.866 for the validation group. Hosmer-Lemeshow test indicated good calibration (modeling group: χ²=7.120, P=0.714; validation group: χ²=7.058, P=0.724). DCA showed clinical utility for predicted probabilities between 0.08 and 0.96.
Conclusion: Age, operation time, time in bed, pneumoperitoneum pressure, duration of indwelling drains, TC, and D-dimer are risk factors for DVT in patients after laparoscopic PN. The constructed nomogram demonstrates good predictive performance and can be used for individualized risk assessment of postoperative DVT in RCC patients.
Keywords: renal cell carcinoma, partial nephrectomy, deep vein thrombosis, risk factors, nomogram
1School of Nursing, Yongzhou Vocational Technical College, Yongzhou, Hunan, 425100, People’s Republic of China; 2Department of Urinary Surgery, The Central Hospital of Yongzhou, Yongzhou, Hunan, 425000, People’s Republic of China; 3Department of Urinary Surgery, Yongzhou Hospital Affiliated to University of South China, Yongzhou, Hunan, 425000, People’s Republic of China
Correspondence: Duoyun Jiang, Department of Urinary Surgery, The Central Hospital of Yongzhou, 151 Xiaoshui West Road, Lingling District, Yongzhou, Hunan, 425100, People’s Republic of China, Tel +8615807463629, Email a15116844468@126.com
Objective: To construct a nomogram model for individualized prediction of risk of deep vein thrombosis (DVT) in renal cell carcinoma (RCC) patients after laparoscopic partial nephrectomy (PN).
Methods: A retrospective study was conducted on 556 RCC patients admitted to our hospital from January 2015 to January 2025. Patients were randomly divided into a modeling group (n=389) and a validation group (n=167) at a ratio of 7:3. The modeling group was further subdivided into DVT and non-DVT groups based on postoperative DVT occurrence. Clinical data were collected. Logistic regression was used to analyze the influencing factors. R software was used to construct the nomogram. ROC curves were generated to evaluate the discrimination ability of the nomogram. Decision curve analysis (DCA) was performed to assess its clinical utility.
Results: Of 556 patients, 100 (17.98%) developed DVT, including 70 (17.99%) in the modeling group. Logistic regression identified age, operation time, time in bed, pneumoperitoneum pressure, duration of indwelling drains, total cholesterol (TC), and D-dimer as significant risk factors (P < 0.05). The area under the curve (AUC) was 0.889 for the modeling group and 0.866 for the validation group. Hosmer-Lemeshow test indicated good calibration (modeling group: χ²=7.120, P=0.714; validation group: χ²=7.058, P=0.724). DCA showed clinical utility for predicted probabilities between 0.08 and 0.96.
Conclusion: Age, operation time, time in bed, pneumoperitoneum pressure, duration of indwelling drains, TC, and D-dimer are risk factors for DVT in patients after laparoscopic PN. The constructed nomogram demonstrates good predictive performance and can be used for individualized risk assessment of postoperative DVT in RCC patients.
Keywords: renal cell carcinoma, partial nephrectomy, deep vein thrombosis, risk factors, nomogram