{"title":"慢性术后疼痛风险预测模型的建立和验证:视频胸腔镜肺癌手术的单中心前瞻性研究。","authors":"Xiong-Fei Zhang, Chang-Guo Peng, Hua-Jing Guo, Zhi-Ming Zhang","doi":"10.1186/s13019-024-03310-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objectives: </strong>This study aimed to develop and validate a risk prediction model for CPSP in patients undergoing VATS for lung cancer.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p><p><strong>Clinical registration number: </strong>Registration website: https://www.chictr.org.cn/ . Registration date: 2022/5/21.</p><p><strong>Registration number: </strong>ChiCTR2200060196.</p>","PeriodicalId":15201,"journal":{"name":"Journal of Cardiothoracic Surgery","volume":"20 1","pages":"85"},"PeriodicalIF":1.5000,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756054/pdf/","citationCount":"0","resultStr":"{\"title\":\"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.\",\"authors\":\"Xiong-Fei Zhang, Chang-Guo Peng, Hua-Jing Guo, Zhi-Ming Zhang\",\"doi\":\"10.1186/s13019-024-03310-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objectives: </strong>This study aimed to develop and validate a risk prediction model for CPSP in patients undergoing VATS for lung cancer.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p><p><strong>Clinical registration number: </strong>Registration website: https://www.chictr.org.cn/ . Registration date: 2022/5/21.</p><p><strong>Registration number: </strong>ChiCTR2200060196.</p>\",\"PeriodicalId\":15201,\"journal\":{\"name\":\"Journal of Cardiothoracic Surgery\",\"volume\":\"20 1\",\"pages\":\"85\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2025-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756054/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cardiothoracic Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13019-024-03310-9\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cardiothoracic Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13019-024-03310-9","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
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.
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.
期刊介绍:
Journal of Cardiothoracic Surgery is an open access journal that encompasses all aspects of research in the field of Cardiology, and Cardiothoracic and Vascular Surgery. The journal publishes original scientific research documenting clinical and experimental advances in cardiac, vascular and thoracic surgery, and related fields.
Topics of interest include surgical techniques, survival rates, surgical complications and their outcomes; along with basic sciences, pediatric conditions, transplantations and clinical trials.
Journal of Cardiothoracic Surgery is of interest to cardiothoracic and vascular surgeons, cardiothoracic anaesthesiologists, cardiologists, chest physicians, and allied health professionals.