Clinical Trials Directory

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

TypeNameDescription
DRUGBupivacaine + saline25 ml of 0.5% bupivacaine combined with 5 ml of normal saline, administered via ultrasound-guided supraclavicular brachial plexus block as a control intervention
DRUGBupivacaine + nalbuphine25 ml of 0.5% bupivacaine combined with nalbuphine at a dose of 50 µg/kg, administered via ultrasound-guided supraclavicular brachial plexus block
DRUGBupivacaine + morphine25 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.