PHARMACOLOGIC ALGORITHM FOR CANCER PAIN MANAGEMENT

本申请是对RFA CA-93-035的回应，建议实施并 传播乳腺癌疼痛管理的药理学算法。 许多国家组织，如卫生保健政策机构和 研究（AHCPR）和美国疼痛协会（APS），以及 世界卫生组织（WHO）等国际组织 国际疼痛研究协会（International Association for the Study of Pain，IASP） 主张使用基于一个 阶梯式药理决策模型。该提案将测试 药理学算法利用完善的和推荐的药物 基于疼痛评估的乳腺癌治疗 人群，包括药物剂量、剂量调整和副作用 管理的医生和护士在门诊中心， 家庭护理护士的协助。研究的第一阶段将测试 算法的可行性。八个肿瘤门诊诊所， 28名医学肿瘤学家及其护士（MD/RN）将参加。乳腺 有转移性或侵袭性疾病证据的癌症患者将被 随机化并分配至算法或无算法治疗。患者 两组患者均接受初步的综合疼痛评估， 效果评估和基线结果研究。将进行随机化 评估完成后。算法组的患者将 根据疼痛评估和既往阿片类药物 治疗，并开始药物治疗 研究团队的算法。由诊所或家庭进行疼痛重新评估 护理人员也将由算法支配。患者在无- 算法组将接受“常规和习惯”疼痛管理 根据其主要肿瘤学MD/RN的规定进行护理。观察指标 检查疼痛、症状困扰和生活质量， 在第1、2、3和6个月结束时，两组均进行了检测。中期分析将 在第一阶段结束时进行。第一阶段的资料 来自癌症疼痛领域领导者的经验和专家咨询 管理领域将导致规划和实施一个 针对参与的肿瘤学家和肿瘤学的算法培训课程 护士MD/RN对将随机分配至算法组或无算法组 组算法MD/RN将参加四小时的培训课程， 通过一次教育强化会议。2研究阶段 这项研究将考察研究团队转移知识的能力 成功地将算法过程的信息传递给社区 医生和护士。我们假设l）接受癌症治疗的患者 利用算法治疗决策模型的疼痛治疗将 报告疼痛更少，副作用更少，生活质量更高， 接受癌症疼痛治疗而没有决策模型的患者，以及2） 接受算法使用专门培训的MD/RN团队 决策模型将能够成功地转移用户的知识 将算法决策模型应用于实践。

This application, in response to RFA CA-93-035, proposes to implement and disseminate a pharmacologic algorithm for breast cancer pain management. Many national organizations such as the Agency for Health Care Policy and Research (AHCPR) and the American Pain Society (APS), as well as international organizations such as The World Health Organization (WHO) and the International Association for the Study of Pain (IASP) have advocated for the use of "guidelines" or "standards" that are based on a step ladder pharmacologic decision making model. This proposal will test a pharmacologic algorithm utilizing well established and recommended drug therapies, based on pain assessment, applied in the breast cancer population, with medication dosing and titration and side effect management by physicians and nurses in outpatient centers with facilitation by home care nurses. The first phase of the study will test the feasibility of the algorithm. Eight outpatient oncology clinics with 28 medical oncologists and their nurses (MD/RN) will participate. Breast cancer patients with evidence of metastatic or invasive disease will be randomized and assigned to algorithm or no-algorithm treatment. Patients in both groups will receive an initial comprehensive pain evaluation, side effect evaluation and baseline outcome studies. Randomzation will occur after evaluations are completed. Patients in the algorithm group will be placed into the algorithm based on pain assessment and previous opioid therapy and initiated on pharmacologic therapy as dictated by the algorithm by the research team. Pain reassessment by the clinic or home care staff will also be dictated by the algorithm. Patients in the no- algorithm group will receive the "usual and customary" pain management care as prescribed by their primary oncology MD/RN. Outcome measures examining pain, symptom distress, and quality of life will be collected at the end of months 1,2,3, and 6, from both groups. As interim analysis will be performed at the end of phase I. Information from the phase I experience and expert consultation from leaders in the cancer pain management field will result in the planning and implementation of a algorithm training session for participating oncologists and oncology nurses. MD/RN pairs will be randomized to algorithm or no-algorithm groups. Algorithm MD/RNs will attend a four hour training session followed by a one time educational reinforcement session. The 2nd phase of the study will examine the ability of the research team to transfer knowledge of the algorithmic process successfully into the hands of community physicians and nurses. We hypothesize that l) patients receiving cancer pain treatment utilizing an algorithmic treatment decision model will report less pain, fewer side effects, and enhanced quality of life than patients receiving cancer pain treatment without the decision model and 2) MD/RN teams that receive specialized training in the use of an algorithmic decision model will be able to successfully transfer that knowledge of the algorithmic decision making model into their practice.