NCT06903624

Cannabis in Postoperative Pain Management

Exploring the Impact of Cannabis on Postoperative Pain Management and Analgesic Consumption

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 70,000 (actual)
• Sponsor: Assuta Medical Center · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

Postoperative pain management is critical for surgical recovery, affecting patient outcomes, hospitalization duration, and quality of life. Variability in pain perception and medication needs among surgical patients poses a challenge in clinical practice. Identifying predictive factors for pain severity and analgesic use could enhance personalized pain management strategies. Cannabis, containing cannabinoids with analgesic and anti-inflammatory properties, has garnered attention as a potential pain management option for surgical patients. The effectiveness of cannabis varies, depending on surgery type, severity, and individual pain tolerance. Some studies suggest cannabis users may experience heightened pain sensitivity and require more analgesics, while others highlight its potential to reduce opioid use. Despite growing interest, the use of cannabis in surgery remains controversial due to a lack of large-scale clinical trials evaluating its safety and efficacy in this setting. Some research indicates cannabis use could lower pain levels post-surgery and reduce opioid needs. However, other studies raise safety concerns, and conflicting findings have yet to establish its role conclusively. Given these uncertainties, healthcare professionals must carefully monitor cannabis use in surgical patients. Patients should inform providers of any cannabis use before surgery to ensure appropriate pain management and minimize risks. This study aims to analyze pain intensity and analgesic usage patterns across various surgeries using real-world medical data. Machine learning models will predict high analgesic needs, focusing on cannabis users. This research seeks to optimize postoperative pain treatment and personalize clinical strategies.

Detailed description

Study Design This retrospective cohort study analyzes anonymized medical records of surgical patients who underwent surgery between January 2017 and January 2025 at the Assuta hospitals network. Data Source Electronic health records from a hospital database, including postoperative pain scores, analgesic administration, and patient demographics. Pain levels will be assessed during hospitalization for up to one-week post-surgery. In the cannabis use research group, participants will be asked to report their daily use for at least the past six months. The study will utilize MDClone, a healthcare data analytics platform, to extract and analyze anonymized electronic health records. MDClone enables the generation of synthetic, privacy-preserving patient data, ensuring compliance with ethical and regulatory standards while allowing for robust statistical analysis. Variables for Analysis * Demographics: Age, sex, BMI, Hospital stay, Operation duration, type of anesthesia, region of residence, marital status. * Medical History: Comorbidities, history of trauma, psychiatric conditions, prior surgeries. * Surgical Data: Type of procedure, intraoperative factors, postoperative complications. * Pain Management: Pain scores (e.g., VAS), opioid and non-opioid analgesic doses, use of regional anesthesia. * Psychosocial Factors: psychiatric medication use (e.g., antidepressants). * Hospital Course: Length of stay, ICU admissions The study population The expected number of participants is 70,000 participants from the five medical canters in the Assuta network. Statistical analysis include: 1. Descriptive Analysis - Baseline characteristics will be summarized using means, medians, and proportions. 2. Comparative Analysis - Pain levels and analgesic use across different surgical types, comorbidities and between cannabis users vs. non-users will be compared using t-tests, chi-square tests, or non-parametric equivalents. 3. Machine Learning Models - Supervised learning algorithms (e.g., logistic regression, random forests, gradient boosting) will be employed to predict high analgesic requirements based on preoperative and intraoperative variables. 4. Validation \& Model Performance - ROC-AUC, sensitivity, and specificity will be used to assess model accuracy.

Conditions

• Postoperative Pain

Timeline

• Start date: 2016-01-01
• Primary completion: 2025-02-25
• Completion: 2025-03-24
• First posted: 2025-03-31
• Last updated: 2025-04-03