Evidence map›Paper›PMID 41455918›Full record

Trial reportBMC anesthesiology2025

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

Alzahraa A Abbas, Sameh Ghoneim, Mohamed Sharf, Ahmed Farag

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC anesthesiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07008443 (An Integrated Artificial Intelligence Approach for Predicting Analgesic Time Based on Nalbuphine Versus Morphine as Adjuvants to Bupivacaine in Ultrasound-Guided Supraclavicular Block), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07008443 phase4completednot on this map

An Integrated Artificial Intelligence Approach for Predicting Analgesic Time Based on Nalbuphine Versus Morphine as Adjuvants to Bupivacaine in Ultrasound-Guided Supraclavicular Block

TypeinterventionalSponsorAlzahraa Ahmed AbbasRan2024 to 2025Enrolled60ConditionsRegional Anesthesia Block, Upper Limb SurgeryArmsBupivacaine + saline, Bupivacaine + nalbuphine, Bupivacaine + morphine
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alzahraa A AbbasDepartment of Anesthesia, Intensive Care and Pain Management, Damietta Faculty of Medicine for Girls, Al-Azhar University, New Damietta, Egypt. zohramohamed91@gmail.com.
Sameh GhoneimDepartment of Anesthesia, Intensive Care and Pain Management, Faculty of Medicine for Girls, Al-Azhar University, Cairo, Egypt.
Mohamed SharfDepartment of Anesthesia, Intensive Care and Pain Management, Damietta Faculty of Medicine, Al-Azhar University, New Damietta, Egypt.
Ahmed FaragDepartment of Anesthesia, Intensive Care and Pain Management, Faculty of Medicine for Girls, Al-Azhar University, Cairo, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSupraclavicular 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.

methodsA 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.

resultsBoth 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.

conclusionsNalbuphine 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.

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

Indexed as

Anesthetics, LocalBrachial Plexus BlockBupivacaineMachine LearningMorphineNalbuphineAdjuvants, AnesthesiaAdultAnalgesics, OpioidFemaleHumansMaleMiddle AgedPredictive Learning ModelsProspective StudiesUltrasonography, InterventionalAdjuvants, AnesthesiaAnalgesics, OpioidAnesthetics, LocalBupivacaineMorphineNalbuphineAnalgesiaArtificial intelligenceBrachial plexus blockBupivacaineMachine LearningMorphineNalbuphineNerve blockPredictionSupraclavicular blockUltrasound

Identifiers

PMID41455918
PMCPMC12853680

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.