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
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...
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BMC
2025-01-01
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| Series: | Journal of Cardiothoracic Surgery |
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| Online Access: | https://doi.org/10.1186/s13019-024-03310-9 |
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| author | Xiong-Fei Zhang Chang-Guo Peng Hua-Jing Guo Zhi-Ming Zhang |
| author_facet | Xiong-Fei Zhang Chang-Guo Peng Hua-Jing Guo Zhi-Ming Zhang |
| author_sort | Xiong-Fei Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-855045daceb4434688b36bba9fbce1a1 |
| institution | Kabale University |
| issn | 1749-8090 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cardiothoracic Surgery |
| spelling | doaj-art-855045daceb4434688b36bba9fbce1a12025-01-26T12:51:53ZengBMCJournal of Cardiothoracic Surgery1749-80902025-01-0120111110.1186/s13019-024-03310-9Development and validation of a prediction model for chronic post-surgical pain risk: a single-center prospective study of video-assisted thoracoscopic lung cancer surgeryXiong-Fei Zhang0Chang-Guo Peng1Hua-Jing Guo2Zhi-Ming Zhang3Department of Anaesthesiology, The First Affiliated Hospital of Jinan UniversityDepartment of Anaesthesiology, The First People’s Hospital of Changde CityDepartment of Anaesthesiology, The First People’s Hospital of Changde CityDepartment of Anesthesiology, The First People’s Hospital of ChenzhouAbstract 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.https://doi.org/10.1186/s13019-024-03310-9Chronic post-surgical pain (CPSP)Non-small cell lung cancerPersonalized pain managementPrediction modelVideo-assisted thoracoscopic surgery (VATS) |
| spellingShingle | Xiong-Fei Zhang Chang-Guo Peng Hua-Jing Guo Zhi-Ming Zhang 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 Journal of Cardiothoracic Surgery Chronic post-surgical pain (CPSP) Non-small cell lung cancer Personalized pain management Prediction model Video-assisted thoracoscopic surgery (VATS) |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Chronic post-surgical pain (CPSP) Non-small cell lung cancer Personalized pain management Prediction model Video-assisted thoracoscopic surgery (VATS) |
| url | https://doi.org/10.1186/s13019-024-03310-9 |
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