Effect of nalbuphine and morphine as adjuvants to bupivacaine in ultrasound-guided supraclavicular block: a randomized controlled trial with machine learning-based predictive analysis

TL;DR

This study compares the effects of nalbuphine and morphine as additives to bupivacaine in a common anesthesia technique and uses machine learning to predict analgesic duration.

Contribution

The novel use of a K-Nearest Neighbor machine learning model to predict analgesic duration in regional anesthesia.

Findings

Nalbuphine and morphine significantly prolonged analgesia compared to the control group.

The KNN model accurately predicted analgesic duration with a high correlation (R = 0.95).

Abstract

Supraclavicular brachial plexus block is a commonly used regional anesthesia technique for upper extremity surgeries. The addition of adjuvants to local anesthetics enhances the duration and quality of anesthesia. Recently, Machine Learning (ML) has emerged as a tool for predictive modeling in medicine, including pain management. This study investigates the predictive capability of a K-Nearest Neighbor (KNN) ML model for analgesic duration using different drug combinations.

A prospective randomized controlled trial was conducted on 60 patients scheduled for upper limb surgeries under ultrasound-guided supraclavicular brachial plexus block. Patients were divided into three groups: Control (bupivacaine + saline), Nalbuphine (bupivacaine + nalbuphine), and Morphine (bupivacaine + morphine). The analgesic duration, sensory and motor block onset and duration, and postoperative analgesic use were recorded. A ML model, K-Nearest Neighbor (KNN), was developed to predict analgesic time based on demographic and hemodynamic parameters.

Both nalbuphine and morphine significantly prolonged the duration of analgesia compared to the control group. The KNN model demonstrated a strong correlation (R = 0.95) between the observed and predicted analgesic duration, indicating high predictive accuracy.

Nalbuphine and morphine significantly extended the analgesic duration of bupivacaine in ultrasound-guided supraclavicular brachial plexus block. ML models, such as KNN, offer effective tools for predicting analgesic outcomes and can assist anesthesiologists in making informed decisions regarding drug combinations for enhanced patient care.

ClinicalTrials.gov (NCT07008443). Registered in June 2025. Retrospectively registered. ClinicalTrials.gov is a primary registry in the WHO International Clinical Trials Registry Platform (ICTRP) network.

The online version contains supplementary material available at 10.1186/s12871-025-03537-6.

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