多因素机器学习算法集成疼痛机制可以预测3周非甾体抗炎药加扑热息痛治疗疼痛性膝骨关节炎患者的疗效。

IF 3.4 2区医学 Q1 ANESTHESIOLOGY

European Journal of Pain Pub Date : 2025-09-29 DOI:10.1002/ejp.70140

Rocco Giordano, Lars Arendt-Nielsen, Emma Hertel, Anne Estrup Olesen, Kristian Kjær-Staal Petersen

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引用次数: 0

摘要

研究表明，疼痛致敏、表观遗传机制、炎症和心理因素可能是治疗结果的预测因素。推荐抗炎治疗，但疗效因患者而异。本研究旨在利用机器学习预测3周非甾体抗炎药加扑热息痛治疗的镇痛反应，治疗前评估疼痛敏感性、炎症、microRNA和心理因素。方法：101例患者接受3周的非甾体抗炎药联合扑热息痛治疗。治疗前采用袖带测量法、医院焦虑抑郁量表、疼痛灾难化量表、EQ-5D-3L量表采集疼痛敏感性，并采血。在治疗前后，通过膝关节损伤和骨关节炎结局评分疼痛亚量表评估疼痛缓解。使用Olink分析炎症生物标志物，使用下一代RNA测序分析microRNA。使用潜在成分（DIABLO）进行生物标志物发现的数据整合分析，以整合治疗前数据并解释镇痛效果。结果：DIABLO模型识别了4个领域的30个显著变量。经交叉验证，模型的精确查全率曲线下面积为85%，灵敏度为83%，特异性为87%，平衡准确率为85%。结论：本研究利用基于疼痛致敏、表观遗传学、炎症反应和心理因素的机器学习算法来预测骨关节炎患者的镇痛反应。该研究表明，将多个因素纳入模型可以提高其性能，从而能够识别将从治疗中受益的患者，推进个性化疼痛管理。意义声明：在本研究中，一种基于疼痛致敏、表观遗传机制、炎症反应和心理因素的机器学习算法预测骨关节炎患者的镇痛反应，准确率为84%。试验注册：ClinicalTrials.gov标识符：NCT02967744。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Multifactorial Machine Learning Algorithm Integration of Pain Mechanisms Can Predict the Efficacy of 3-Week NSAID Plus Paracetamol in Patients With Painful Knee Osteoarthritis

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Multifactorial Machine Learning Algorithm Integration of Pain Mechanisms Can Predict the Efficacy of 3-Week NSAID Plus Paracetamol in Patients With Painful Knee Osteoarthritis

Background

Studies demonstrate that pain sensitization, epigenetic mechanisms, inflammation, and psychological factors might be predictive of treatment outcomes. Anti-inflammatory therapy is recommended, but efficacy varies among patients. This study aimed to utilise machine learning to predict the analgesic responses of 3-week NSAID plus paracetamol therapy using pre-treatment assessments of pain sensitivity, inflammation, microRNA, and psychological factors.

Methods

Patients (n = 101) underwent 3-week combined NSAID plus paracetamol therapy. Pain sensitivity using cuff algometry, Hospital Anxiety and Depression Scale, Pain Catastrophizing Scale, EQ-5D-3L scale, and blood samples were collected before therapy. Pain relief was assessed by the Knee Injury and Osteoarthritis Outcome Score pain subscale, before and after therapy. Inflammatory biomarkers were analysed using Olink, and microRNA using Next-Generation RNA Sequencing. Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) was utilised to integrate the pre-treatment data and explain the analgesic effect.

Results

DIABLO model identified 30 significant variables across the 4 domains. After cross-validation, model performance showed an area under the precision-recall curve of 85%, sensitivity of 83%, specificity of 87%, and balanced accuracy of 85%.

Conclusions

This study utilises a machine learning algorithm, based on pain sensitization, epigenetics, inflammatory response, and psychological factors, to predict analgesic response in osteoarthritis patients. The study demonstrates that incorporating multiple factors into a model enhances its performance, enabling the identification of patients who will benefit from therapy, advancing personalised pain management.

Significance Statement

In this study, a machine learning algorithm, based on pain sensitization, epigenetic mechanisms, inflammatory response, and psychological factors, predicts analgesic response in osteoarthritis patients with 84% accuracy.

Trial Registration

ClinicalTrials.gov identifier: NCT02967744

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来源期刊

European Journal of Pain 医学-临床神经学

CiteScore

7.50

自引率

5.60%

发文量

163

审稿时长

4-8 weeks

期刊介绍： European Journal of Pain (EJP) publishes clinical and basic science research papers relevant to all aspects of pain and its management, including specialties such as anaesthesia, dentistry, neurology and neurosurgery, orthopaedics, palliative care, pharmacology, physiology, psychiatry, psychology and rehabilitation; socio-economic aspects of pain are also covered. Regular sections in the journal are as follows: • Editorials and Commentaries • Position Papers and Guidelines • Reviews • Original Articles • Letters • Bookshelf The journal particularly welcomes clinical trials, which are published on an occasional basis. Research articles are published under the following subject headings: • Neurobiology • Neurology • Experimental Pharmacology • Clinical Pharmacology • Psychology • Behavioural Therapy • Epidemiology • Cancer Pain • Acute Pain • Clinical Trials.