Liew Bernard X W, Palacios-Ceña María, Scutari Marco, Fuensalida-Novo Stella, Guerrero-Peral Angel, Ordás-Bandera Carlos, Pareja Juan A, Fernández-de-Las-Peñas César
School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, United Kingdom.
Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Universidad Rey Juan Carlos, Alcorcón, Spain.
J Pain. 2023 Mar;24(3):426-436. doi: 10.1016/j.jpain.2022.10.003. Epub 2022 Oct 13.
Tension type headache (TTH) is a prevalent but poorly understood pain disease. Current understanding supports the presence of multiple associations underlying its pathogenesis. Our aim was to compare competing multivariate pathway models that explains the complexity of TTH. Headache features (intensity, frequency, or duration - headache diary), headache-related disability (Headache Disability Inventory-HDI), anxiety/depression (Hospital Anxiety and Depression Scale), sleep quality (Pittsburgh Sleep Quality Index), widespread pressure pain thresholds (PPTs) and trigger points (TrPs) were collected in 208 individuals with TTH. Four latent variables were formed from the observed variables - Distress (anxiety, depression), Disability (HDI subscales), Severity (headache features), and Sensitivity (all PPTs). Structural equation modelling (SEM) and Bayesian network (BN) analyses were used to build and compare a theoretical (model) and a data-driven (model) latent variable model. The model (root mean square error of approximation [RMSEA] = 0.035) provided a better statistical fit than model (RMSEA = 0.094). The only path common between model and model was the influence of years with pain on TrPs. The model revealed that the largest coefficient magnitudes were between the latent variables of Distress and Disability (β=1.524, P = .006). Our theoretical model proposes a relationship whereby psycho-physical and psychological factors result in clinical features of headache and ultimately affect disability. Our data-driven model proposes a more complex relationship where poor sleep, psychological factors, and the number of years with pain takes more relevance at influencing disability. Our data-driven model could be leveraged in clinical trials investigating treatment approaches in TTH. PERSPECTIVE: A theoretical model proposes a relationship where psycho-physical and psychological factors result in clinical manifestations of headache and ultimately affect disability. A data-driven model proposes a more complex relationship where poor sleep, psychological factors, and number of years with pain takes more relevance at influencing disability.
紧张型头痛（TTH）是一种常见但了解不足的疼痛性疾病。目前的认识支持其发病机制存在多种关联。我们的目的是比较解释TTH复杂性的相互竞争的多变量通路模型。收集了208名TTH患者的头痛特征（强度、频率或持续时间——头痛日记）、头痛相关残疾（头痛残疾评定量表-HDI）、焦虑/抑郁（医院焦虑抑郁量表）、睡眠质量（匹兹堡睡眠质量指数）、广泛压痛阈值（PPTs）和触发点（TrPs）。从观察变量中形成了四个潜在变量——痛苦（焦虑、抑郁）、残疾（HDI子量表）、严重程度（头痛特征）和敏感性（所有PPTs）。使用结构方程模型（SEM）和贝叶斯网络（BN）分析来构建和比较一个理论（模型）和一个数据驱动（模型）的潜在变量模型。模型（近似均方根误差[RMSEA]=0.035）比模型（RMSEA=0.094）提供了更好的统计拟合。模型和模型之间唯一共同的路径是疼痛年限对TrPs的影响。模型显示，最大的系数幅度存在于痛苦和残疾的潜在变量之间（β=1.524，P=.006）。我们的理论模型提出了一种关系，即心理-生理和心理因素导致头痛的临床特征并最终影响残疾。我们的数据驱动模型提出了一种更复杂的关系，即睡眠不佳、心理因素和疼痛年限在影响残疾方面更具相关性。我们的数据驱动模型可用于研究TTH治疗方法的临床试验。观点：一个理论模型提出了一种关系，即心理-生理和心理因素导致头痛的临床表现并最终影响残疾。一个数据驱动模型提出了一种更复杂的关系，即睡眠不佳、心理因素和疼痛年限在影响残疾方面更具相关性。
