Academic Journal
Development and validation of a prediction model for chronic post-surgical pain risk: a single-center prospective study of video-assisted thoracoscopic lung cancer surgery
| العنوان: | Development and validation of a prediction model for chronic post-surgical pain risk: a single-center prospective study of video-assisted thoracoscopic lung cancer surgery |
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| المؤلفون: | Xiong-Fei Zhang, Chang-Guo Peng, Hua-Jing Guo, Zhi-Ming Zhang |
| المصدر: | Journal of Cardiothoracic Surgery, Vol 20, Iss 1, Pp 1-11 (2025) |
| بيانات النشر: | BMC, 2025. |
| سنة النشر: | 2025 |
| المجموعة: | LCC:Surgery LCC:Anesthesiology |
| مصطلحات موضوعية: | Chronic post-surgical pain (CPSP), Non-small cell lung cancer, Personalized pain management, Prediction model, Video-assisted thoracoscopic surgery (VATS), Surgery, RD1-811, Anesthesiology, RD78.3-87.3 |
| الوصف: | Abstract Background Chronic post-surgical pain (CPSP) is a common complication following video-assisted thoracoscopic surgery (VATS) that significantly impacts the quality of life of patients. Although multiple risk factors have been identified, no systematically validated prediction model exists to guide clinical decision-making. Objectives This study aimed to develop and validate a risk prediction model for CPSP in patients undergoing VATS for lung cancer. Methods This prospective cohort study included clinical data from 400 patients with non-small cell lung cancer who underwent VATS from June 2022 to June 2023. Patients were randomly assigned to a training cohort and an internal test cohort and assessed for sleep quality, psychological status, and pain levels. A nomogram prediction model was established based on variables significantly associated with CPSP in the training cohort. The model was internally validated in the internal test cohort to evaluate its discrimination, calibration, and clinical utility. Results Independent risk factors for CPSP included female gender, severe acute pain post-surgery, lymph node dissection, and cold pain sensation, while paravertebral nerve block was identified as a protective factor. The AUC values were 0.878 in training cohort and 0.805 in internal test cohort, respectively, indicating that the model performed well in identifying patients at risk for CPSP. The calibration curves in both cohorts showed a good fit, indicating that the model’s predictions were reliable. And the DCA curve showed that using our model to guide decisions resulted in a higher net benefit compared to a strategy of not screening or treating all patients. Conclusion An effective risk prediction model for CPSP was successfully developed and validated in this study. This model can aid physicians in conducting more accurate assessments of CPSP risk in patients prior to surgery and in offering personalized strategies for managing CPSP. Clinical registration number Registration website: https://www.chictr.org.cn/ . Registration date: 2022/5/21. Registration number: ChiCTR2200060196. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1749-8090 |
| Relation: | https://doaj.org/toc/1749-8090 |
| DOI: | 10.1186/s13019-024-03310-9 |
| URL الوصول: | https://doaj.org/article/855045daceb4434688b36bba9fbce1a1 |
| رقم الانضمام: | edsdoj.855045daceb4434688b36bba9fbce1a1 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17498090 |
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| DOI: | 10.1186/s13019-024-03310-9 |