Predictive Scores for Identifying Chronic Opioid Dependence After General Anesthesia Surgery

Area of Science:

• Anesthesiology and Pain Management
• Health Services Research
• Pharmacology

Background:

• Postoperative chronic opioid dependence is a significant clinical challenge.
• Identifying at-risk patients is crucial for effective pain management strategies.

Purpose of the Study:

• To determine the prevalence and risk factors of postoperative chronic opioid dependence.
• To develop and validate a predictive scoring system for identifying high-risk surgical patients.

Main Methods:

• Analysis of a large cohort (111,069 patients) from the Taiwan Health Insurance Research Database (2016-2018).
• Utilized a stepwise multivariate model to develop the Wu and Zhang predictive scores.
• Included patient demographics, surgical factors, comorbidities, and preoperative medication use.
• Internal validation performed using bootstrap sampling.

Main Results:

• 1.6% of patients developed postoperative chronic opioid dependence.
• Key risk factors identified: age, gender, surgery type, anesthesia duration, preoperative opioid use, and comorbidities.
• The Wu and Zhang scores showed good predictive accuracy (AUC=0.83).
• Risk stratification identified varying susceptibility (low 0.7%, moderate 1.4%, high 3.5%).

Conclusions:

• The study introduces a validated scoring system for predicting postoperative chronic opioid dependence.
• Identified risk factors and stratification enable early detection and targeted interventions.
• This contributes to improved patient outcomes and reduced societal burden in pain management.