Department of Veterans Affairs, VA Portland Health Care System, Evidence-based Synthesis Program (ESP) Coordinating Center, Portland, OR, USA.
J Gen Intern Med. 2018 May;33(Suppl 1):71-81. doi: 10.1007/s11606-018-4328-7.
Primary care providers (PCPs) face many system- and patient-level challenges in providing multimodal care for patients with complex chronic pain as recommended in some pain management guidelines. Several models have been developed to improve the delivery of multimodal chronic pain care. These models vary in their key components, and work is needed to identify which have the strongest evidence of clinically-important improvements in pain and function. Our objective was to determine which primary care-based multimodal chronic pain care models provide clinically relevant benefits, define key elements of these models, and identify patients who are most likely to benefit.
To identify studies, we searched MEDLINE® (1996 to October 2016), CINAHL, reference lists, and numerous other sources and consulted with experts. We used predefined criteria for study selection, data abstraction, internal validity assessment, and strength of evidence grading.
We identified nine models, evaluated in mostly randomized controlled trials (RCTs). The RCTs included 3816 individuals primarily from the USA. The most common pain location was the back. Five models primarily coupling a decision-support component-most commonly algorithm-guided treatment and/or stepped care-with proactive ongoing treatment monitoring have the best evidence of providing clinically relevant improvement in pain intensity and pain-related function over 9 to 12 months (NNT range, 4 to 13) and variable improvement in quality of life, depression, anxiety, and sleep. The strength of the evidence was generally low, as each model was only supported by a single RCT with imprecise findings.
Multimodal chronic pain care delivery models coupling decision support with proactive treatment monitoring consistently provide clinically relevant improvement in pain and function. Wider implementation of these models should be accompanied by further evaluation of clinical and implementation effectiveness.
初级保健提供者（PCP）在为患有复杂慢性疼痛的患者提供多模式护理方面面临许多系统和患者层面的挑战，这些挑战是一些疼痛管理指南所推荐的。已经开发了几种模型来改善多模式慢性疼痛护理的提供。这些模型在其关键组成部分上有所不同，需要努力确定哪些模型具有改善疼痛和功能的临床重要证据。我们的目的是确定基于初级保健的多模式慢性疼痛护理模型中哪些提供了临床相关的益处，定义这些模型的关键要素，并确定最有可能受益的患者。
为了确定研究，我们搜索了 MEDLINE®（1996 年至 2016 年 10 月）、CINAHL、参考文献列表和许多其他来源，并咨询了专家。我们使用了预先确定的研究选择、数据提取、内部有效性评估和证据分级标准。
我们确定了 9 种模型，这些模型在大多数随机对照试验（RCT）中进行了评估。RCT 纳入了 3816 名主要来自美国的个体。最常见的疼痛部位是背部。五种主要结合决策支持组件的模型（最常见的是算法指导治疗和/或阶梯式护理）和积极的持续治疗监测，具有在 9 至 12 个月内提供疼痛强度和与疼痛相关的功能方面的临床相关改善（NNT 范围为 4 至 13）以及生活质量、抑郁、焦虑和睡眠方面的可变改善的最佳证据。证据的强度通常较低，因为每个模型仅由一个 RCT 支持，且结果不够精确。
将决策支持与积极的治疗监测相结合的多模式慢性疼痛护理提供模型始终提供疼痛和功能方面的临床相关改善。更广泛地实施这些模型应伴随对临床和实施效果的进一步评估。
