Predictive Scores for Identifying Chronic Opioid Dependence After General Anesthesia Surgery.

Abstract

Purpose: 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.