Trials / Completed
CompletedNCT07008443
An Integrated Artificial Intelligence Approach for Predicting Analgesic Time Based on Nalbuphine Versus Morphine as Adjuvants to Bupivacaine in Ultrasound-Guided Supraclavicular Block
- Status
- Completed
- Phase
- Phase 4
- Study type
- Interventional
- Enrollment
- 60 (actual)
- Sponsor
- Alzahraa Ahmed Abbas · Academic / Other
- Sex
- All
- Age
- 21 Years – 60 Years
- Healthy volunteers
- Not accepted
Summary
This study investigated the effect of adding nalbuphine or morphine to bupivacaine for supraclavicular brachial plexus block in upper limb surgeries. Sixty adult patients were randomized into three groups: control (bupivacaine + saline), nalbuphine, and morphine. The primary objective was to compare the duration of analgesia between the groups. A secondary goal was to assess whether artificial intelligence (AI), specifically the k-nearest neighbor (KNN) algorithm, could predict analgesic duration based on patient clinical and demographic data. The study concluded that both nalbuphine and morphine significantly prolonged analgesic duration and that the AI model showed high predictive accuracy.
Detailed description
This prospective, randomized, double-blind clinical trial was conducted at Al-Zahraa and Damietta University Hospitals to evaluate the effectiveness of nalbuphine and morphine as adjuvants to bupivacaine in ultrasound-guided supraclavicular brachial plexus block. Sixty ASA I-II adult patients scheduled for upper limb surgeries were enrolled and divided equally into three groups. Group C received 0.5% bupivacaine with saline; Group N received bupivacaine with nalbuphine (50 μg/kg); Group M received bupivacaine with morphine (50 μg/kg). The primary outcome was analgesic duration, measured from block performance until the first request for postoperative analgesia. Secondary outcomes included onset and duration of sensory and motor block, total postoperative analgesic consumption, pain scores, and complications. In parallel, a machine learning model using the K-Nearest Neighbor (KNN) algorithm was developed to predict analgesic duration from demographic and hemodynamic parameters. Exploratory data analysis and clustering methods confirmed the complex relationship between variables. The KNN model demonstrated high predictive accuracy (correlation coefficient \~0.95). The study concluded that both adjuvants extended analgesic duration and that AI models can assist in personalizing analgesic strategies based on patient profiles.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| DRUG | Bupivacaine + saline | 25 ml of 0.5% bupivacaine combined with 5 ml of normal saline, administered via ultrasound-guided supraclavicular brachial plexus block as a control intervention |
| DRUG | Bupivacaine + nalbuphine | 25 ml of 0.5% bupivacaine combined with nalbuphine at a dose of 50 µg/kg, administered via ultrasound-guided supraclavicular brachial plexus block |
| DRUG | Bupivacaine + morphine | 25 ml of 0.5% bupivacaine combined with morphine at a dose of 50 µg/kg, administered via ultrasound-guided supraclavicular brachial plexus block |
Timeline
- Start date
- 2024-01-01
- Primary completion
- 2025-04-01
- Completion
- 2025-04-01
- First posted
- 2025-06-06
- Last updated
- 2025-06-06
Locations
1 site across 1 country: Egypt
Source: ClinicalTrials.gov record NCT07008443. Inclusion in this directory is not an endorsement.