Vu Peter D, D'Souza Ryan S, Javed Saba
Department of Physical Medicine and Rehabilitation, McGovern Medical School, The University of Texas Health Science Center at Houston, 1333 B Moursund St., Ste. 114, Houston, TX, 77030, USA.
Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
Curr Pain Headache Rep. 2025 Jan 27;29(1):36. doi: 10.1007/s11916-024-01343-2.
Quickly referenceable, streamlined, algorithmic approaches for advanced pain management are lacking for patients, trainees, non-pain specialists, and interventional specialists. This manuscript aims to address this gap by proposing a comprehensive, evidence-based algorithm for managing neuropathic, nociceptive, and cancer-associated pain. Such an algorithm is crucial for pain medicine education, offering a structured approach for patient care refractory to conservative management.
A comprehensive literary review with PubMed and regulatory documents from the United States Food and Drug Administration were searched for a variety of interventions. Pain syndromes were categorized into nociceptive and neuropathic pain, and an algorithm was constructed. Serving as an educational tool for patients, trainees, and non-pain specialists, and as an accessible reference for pain specialists, this algorithm bridges knowledge gaps, promotes interdisciplinary collaboration, and streamlines the learning curve for new practitioners. The strength of this algorithm lies in integrating extensive clinical data, emphasizing the latest clinical evidence, and providing a structured decision-making pathway.
对于患者、实习生、非疼痛专科医生和介入专科医生而言，缺乏可快速参考的、简化的、用于高级疼痛管理的算法方法。本手稿旨在通过提出一种全面的、基于证据的算法来管理神经性疼痛、伤害性疼痛和癌症相关疼痛，以填补这一空白。这样一种算法对于疼痛医学教育至关重要，为保守治疗难治的患者护理提供了一种结构化方法。
通过PubMed进行了全面的文献综述，并检索了美国食品药品监督管理局的监管文件，以获取各种干预措施。疼痛综合征被分为伤害性疼痛和神经性疼痛，并构建了一种算法。作为患者、实习生和非疼痛专科医生的教育工具，以及疼痛专科医生易于获取的参考资料，该算法弥合了知识差距，促进了跨学科合作，并简化了新从业者的学习曲线。该算法的优势在于整合了广泛的临床数据，强调了最新的临床证据，并提供了结构化的决策途径。
