在患有腰背痛（LBP）症状的患者中，一旦排除了特定的病因（骨折、感染、炎症性关节炎、癌症、马尾综合征和神经根病变），许多临床医生便会诊断为非特异性腰背痛。因此，目前的非特异性腰背痛治疗方法是通用的。有必要对非特异性腰背痛进行基于数据和证据的多维疼痛相关因素评估的分类，以便在大样本中进行研究。《腰背痛预测性证据驱动智能分类工具》（PREDICT-LBP）项目是一项前瞻性横断面研究，将比较300名患有非特异性腰背痛（年龄在18-55岁之间，男女各半）的女性和男性与100名没有腰背痛史的参照组。参与者将来自公众和当地医疗机构。将收集脊柱组织数据（通过磁共振成像[ MRI] 测定椎间盘组成和形态、椎体脂肪含量以及腰背肌肉形状和组成）、中枢神经系统适应性数据（通过 MRI 测定疼痛阈值、疼痛的时间累积、大脑静息态功能连接、结构连接和区域体积）、心理社会因素（如抑郁、焦虑）和其他肌骨疼痛症状。将通过降维、聚类验证和模糊c均值聚类方法、分类模型和相关敏感性分析，对非特异性腰背痛患者进行亚组分类。该项目是首个个性化的非特异性腰背痛诊断方法，具有在临床实践中广泛应用的潜力。该项目将为评估非特异性腰背痛潜在亚组的特定治疗方法的临床试验提供证据。该分类工具可能会改善患者预后，并降低经济成本。 版权所有：© 2023 Belavy et al. 本文为开放获取文章，根据知识共享署名许可协议发布，允许在任何媒体上无限制复制、分发和传播，前提是原作者和出处得到适当的标记。

In patients presenting with low back pain (LBP), once specific causes are excluded (fracture, infection, inflammatory arthritis, cancer, cauda equina and radiculopathy) many clinicians pose a diagnosis of non-specific LBP. Accordingly, current management of non-specific LBP is generic. There is a need for a classification of non-specific LBP that is both data- and evidence-based assessing multi-dimensional pain-related factors in a large sample size. The "PRedictive Evidence Driven Intelligent Classification Tool for Low Back Pain" (PREDICT-LBP) project is a prospective cross-sectional study which will compare 300 women and men with non-specific LBP (aged 18-55 years) with 100 matched referents without a history of LBP. Participants will be recruited from the general public and local medical facilities. Data will be collected on spinal tissue (intervertebral disc composition and morphology, vertebral fat fraction and paraspinal muscle size and composition via magnetic resonance imaging [MRI]), central nervous system adaptation (pain thresholds, temporal summation of pain, brain resting state functional connectivity, structural connectivity and regional volumes via MRI), psychosocial factors (e.g. depression, anxiety) and other musculoskeletal pain symptoms. Dimensionality reduction, cluster validation and fuzzy c-means clustering methods, classification models, and relevant sensitivity analyses, will classify non-specific LBP patients into sub-groups. This project represents a first personalised diagnostic approach to non-specific LBP, with potential for widespread uptake in clinical practice. This project will provide evidence to support clinical trials assessing specific treatments approaches for potential subgroups of patients with non-specific LBP. The classification tool may lead to better patient outcomes and reduction in economic costs.Copyright: © 2023 Belavy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.