Clinical trial NCT06903624: Cannabis in Postoperative Pain Management
Official title: Exploring the Impact of Cannabis on Postoperative Pain Management and Analgesic Consumption
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Postoperative Pain
Keywords: Cannabis
Postoperative pain
Analgesics
Opioids
Lead sponsor: Assuta Medical Center
Sponsor class: OTHER
First posted: 2025-03-31
Enrollment: 70000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients that underwent painfull surgical procedures will be cosidered in the study.

Brief 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.

Eligibility criteria: Inclusion Criteria:

* Patients aged 18 and over.
* Patients who underwent surgery under general anesthesia.

Exclusion Criteria:

* Minimally painful surgical procedures, including wrist and ankle tendon surgeries, minor rectal surgeries (e.g., fistula repair, rectal polyp removal), and minor gynecological procedures (e.g., vaginal procedures, transvaginal tape \[TVT\] insertion and transurethral procedures).
* Surgeries associated with potential neurological complications, such as craniotomy.
* Procedures involving percutaneous stent placement, including ureteral stent insertion.
* Incomplete pain assessment records
* Patients with severe cognitive impairments, affecting their ability to accurately report pain levels.
* Patients unable to express VAS scale.

Primary outcomes:
measure: Pain Intensity
timeFrame: 7 days
Secondary outcomes:
measure: Analgesic Consumption
timeFrame: 7 days
Other outcomes:
Locations:
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