Predictive Scores for Identifying Chronic Opioid Dependence After General Anesthesia Surgery - Sun M; Chen WM; Lu Z; Lv S; Fu N; Yang Y; Wang Y; Miao M; Wu SY; Zhang J (Journal of Pain Research)
Mingyang Sun,1,2,* Wan-Ming Chen,3,4,* Zhongyuan Lu,1,2 Shuang Lv,1,2 Ningning Fu,1,2 Yitian Yang,1,2 Yangyang Wang,1,2 Mengrong Miao,1,2 Szu-Yuan Wu,5– 11 Jiaqiang Zhang1,2 1Department of Anesthesiology and Perioperative Medicine, People’s Hospital of Zhengzhou University, Henan Provincial People’s Hospital, Zhengzhou, Henan, People’s Republic of China; 2Academy of Medical Sciences of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 3Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, Taipei, Taiwan; 4Artificial Intelligence Development Center, Fu Jen Catholic University, Taipei, Taiwan; 5Department of Food Nutrition and Health Biotechnology, College of Medical and Health Science, Asia University, Taichung, Taiwan; 6Big Data Center, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan, Taiwan; 7Division of Radiation Oncology, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan, Taiwan; 8Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung, Taiwan; 9Cancer Center, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, Yilan, Taiwan; 10Centers for Regional Anesthesia and Pain Medicine, Taipei Municipal Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; 11Department of Management, College of Management, Fo Guang University, Yilan, Taiwan*These authors contributed equally to this workCorrespondence: Szu-Yuan Wu, College of Medical and Health Science, Asia University, Taichung, Taiwan; Director, Big Data Center, Lo-Hsu Medical Foundation, LotungPoh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan, Email szuyuanwu5399@gmail.com Jiaqiang Zhang, Department of Anesthesiology and Perioperative Medicine, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, No. 7 Weiwu Road, Jinshui District, Zhengzhou, Henan, 450003, People’s Republic of China, Email jiaqiang197628@163.comPurpose: To address the prevalence and risk factors of postoperative chronic opioid dependence, focusing on the development of a predictive scoring system to identify high-risk populations.Methods: We analyzed data from the Taiwan Health Insurance Research Database spanning January 2016 to December 2018, encompassing adults undergoing major elective surgeries with general anesthesia. Patient demographics, surgical details, comorbidities, and preoperative medication use were scrutinized. Wu and Zhang’s scores, a predictive system, were developed through a stepwise multivariate model, incorporating factors significantly linked to chronic opioid dependence. Internal validation was executed using bootstrap sampling.Results: Among 111,069 patients, 1.6% developed chronic opioid dependence postoperatively. Significant risk factors included age, gender, surgical type, anesthesia duration, preoperative opioid use, and comorbidities. Wu and Zhang’s scores demonstrated good predictive accuracy (AUC=0.83), with risk categories (low, moderate, high) showing varying susceptibility (0.7%, 1.4%, 3.5%, respectively). Internal validation confirmed the model’s stability and potential applicability to external populations.Conclusion: This study provides a comprehensive understanding of postoperative chronic opioid dependence and introduces an effective predictive scoring system. The identified risk factors and risk stratification allow for early detection and targeted interventions, aligning with the broader initiative to enhance patient outcomes, minimize societal burdens, and contribute to the nuanced management of postoperative pain.Keywords: chronic opioid dependence, postoperative care, predictive scores, general anesthesia, surgical risk stratification