Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.
Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.
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DOI：
10.1097/j.pain.0000000000002153
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发表时间：
2021-05-01
期刊：
影响因子：
7.4
通讯作者：
Smith SB
中科院分区：
文献类型：
--
作者：
Gaynor SM;Bortsov A;Bair E;Fillingim RB;Greenspan JD;Ohrbach R;Diatchenko L;Nackley A;Tchivileva IE;Whitehead W;Alonso AA;Buchheit TE;Boortz-Marx RL;Liedtke W;Park JJ;Maixner W;Smith SB
慢性疼痛的传统分类和预后方法主要集中在解剖学基础上的临床特征，而不是基于对临床疼痛和未来疼痛轨迹感知的潜在生物心理社会因素。利用监督聚类方法对来自口腔面部疼痛：前瞻性评估和风险评估（OPPERA）研究的颞下颌疾病（TMD）病例和对照组进行队列研究，我们最近开发并验证了一种快速算法（ROPA），以实用地将慢性疼痛患者分为临床疼痛报告、生物心理社会概况、功能限制和合并症三组。目前的目的是在另外两个队列中检验这种聚类程序的普遍性：一个是慢性重叠疼痛患者队列（复杂持续性疼痛（CPPC）研究），另一个是在杜克创新疼痛疗法（DIPT）寻求治疗的现实世界临床人群。在每个队列中，我们应用ROPA进行聚类预测，它只需要四个输入变量：压力疼痛阈值（PPT）和焦虑、抑郁和躯体化量表。在CPPC和DIPT中，我们区分了三个集群，其中一个具有更严重的临床特征和心理困扰。我们观察到与观察到的群集解决方案有很强的一致性，表明ROPA方法可以在患者负担最小的情况下对临床人群进行可靠的分型。ROPA聚类算法是一种独立于解剖诊断的快速有效的分层工具。ROPA有望根据病理生理机制而不是结构或解剖诊断对患者进行分类。因此，这种对患者进行分类的方法将有助于对慢性疼痛患者进行个性化的止痛药治疗。
Traditional classification and prognostic approaches for chronic pain conditions focus primarily on anatomically based clinical characteristics not based on underlying biopsychosocial factors contributing to perception of clinical pain and future pain trajectories. Using a supervised clustering approach in a cohort of temporomandibular disorder (TMD) cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into three groups that differed in clinical pain report, biopsychosocial profiles, functional limitations, and comorbid conditions. The present aim was to examine the generalizability of this clustering procedure in two additional cohorts: a cohort of patients with chronic overlapping pain conditions (Complex Persistent Pain Conditions (CPPC) study), and a real-world clinical population of patients seeking treatment at Duke Innovative Pain Therapies (DIPT). In each cohort, we applied ROPA for cluster prediction, which requires only four input variables: pressure pain threshold (PPT) and anxiety, depression, and somatization scales. In both CPPC and DIPT, we distinguished three clusters, including one with more severe clinical characteristics and psychological distress. We observed strong concordance with observed cluster solutions, indicating the ROPA method allows for reliable subtyping of clinical populations with minimal patient burden. The ROPA clustering algorithm represents a rapid and valid stratification tool independent of anatomic diagnosis. ROPA holds promise in classifying patients based on pathophysiological mechanisms rather than structural or anatomical diagnoses. As such, this method of classifying patients will facilitate personalized pain medicine for patients with chronic pain.
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影响因子：
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作者：
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通讯作者：
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