Managing Acute Pain in Critically Ill Non-communicative Palliative Care Patients

描述(由申请人提供)：此项目响应PA-10-006疼痛研究中的机制、模型、测量和管理(R01)。不能自我报告疼痛的存在或强度的急性疾病住院姑息护理患者，很有可能对疼痛认识不足和治疗不足，经历不必要的痛苦和其他不良影响。在提供姑息治疗的环境中，缺乏可靠和有效的方法来评估这些不与人交流的患者的疼痛，也很少有标准化的方案或算法被测试它们对患者结局的影响。我们最近完成的项目(5R01NR009684)测试了多维客观疼痛评估工具(MOPAT)在评估非沟通性姑息治疗患者急性疼痛方面的心理测量学特性和临床效用，证明了训练有素的护士随着时间的推移经常使用该工具治疗严重疾病非沟通性患者的有效性、可靠性和临床实用性。这项拟议研究的主要目的是测试与没有该算法的患者相比，在内科、外科和创伤重症监护病房住院的危重非通信性姑息治疗患者中，结合了MOPAT和止痛顺序集的疼痛算法是否改善了疼痛严重程度和药物疼痛管理策略的使用。我们假设：(H1)疼痛严重程度降低，(H2)用于疼痛的药理药剂的数量增加，以及(H3)由于实施疼痛算法，用于疼痛的药理药剂的总等量止痛剂增加。第二个描述性目标是(S1)比较有和没有并发疼痛相关情况的患者的疼痛相关结果，(S2)描述患者随着时间的推移的疼痛模式，以及(S3)评估护士对临床的看法 Pain算法的实用性。我们将在一所大型大学急诊医院的内科、外科和创伤重症监护室采用队列对照组准实验设计，分两个阶段进行。在第一阶段(常规护理控制队列n=150名患者)，MOPAT将被纳入常规临床实践和收集的关于疼痛严重程度和使用药理学疼痛管理策略的数据。在第二阶段(干预队列n=150名患者)，将在常规临床实践中实施将MOPAT评分纳入止痛顺序集的疼痛算法，并收集相同的患者结果数据以及护士对该算法(包括MOPAT)临床实用性的看法。主要目的及其相关假设的分析将使用来自2x4重复测量分析或混合线性模型的交互F检验和关于等级数据的2x2重复测量的Cochran‘s Q来完成。次级目标将使用描述性方法，包括重复测量分析和事后分析(如果存在显著的相互作用影响)、确定分组的图表技术(例如，意大利面曲线图)以及对调查问卷的答复的频率分布。这项研究具有重要意义和创新性，因为它使用在非交流患者中验证的工具来测试疼痛算法，以指导疼痛管理并改善这一脆弱和研究不足的人群中与疼痛相关的结果。

DESCRIPTION (provided by applicant): This project responds to PA-10-006 Mechanisms, Models, Measurement, & Management in Pain Research (R01). Acutely ill hospitalized palliative care patients who cannot self-report the presence or intensity of their pain are at high risk for under-recognition and under-treatment of pain, experiencing needless suffering and other adverse effects. Across the settings in which palliative care is rendered, there is a dearth of reliable and valid measures for assessing pain in these non-communicative patients, and few standardized protocols or algorithms have been tested for their effects on patients' outcomes. Our recently completed project (5 R01NR009684) tested the psychometric properties and clinical utility of the Multidimensional Objective Pain Assessment Tool (MOPAT) in assessing acute pain in non-communicative palliative care patients, demonstrating strong evidence of validity, reliability, and clinical utility when used regularly over time by trained nurses in critcally ill non-communicative patients. The primary aim of the proposed research is to test whether a pain algorithm that incorporates the MOPAT and an analgesic order set improves pain severity and use of pharmacologic pain management strategies in critically ill non-communicative palliative care patients who are hospitalized on medical, surgical, and trauma intensive care units when compared to patients without the algorithm. We hypothesize: (H1) a decrease in pain severity, (H2) an increase in the number of pharmacologic agents used for pain, and (H3) an increase in the total equi-analgesic dosage of pharmacologic agents used for pain as a result of implementation of the pain algorithm. The secondary descriptive aims are to (S1) Compare pain-related outcomes in patients with and without concurrent pain-related conditions, (S2) Describe the pattern of patients' pain over time, and (S3) Evaluate nurses' perceptions of clinical utility of the pain algorithm. We will use a cohort control group quasi-experimental design conducted in two phases on the medical, surgical, and trauma intensive care units of a major university acute care hospital. In Phase 1 (usual care control cohort n=150 patients), the MOPAT will be incorporated into routine clinical practice and data on pain severity and use of pharmacologic pain management strategies collected. In Phase 2 (intervention cohort n=150 patients), a pain algorithm incorporating MOPAT scores into an analgesic order set will be implemented in routine clinical practice and the same patient outcome data plus nurses' perceptions of clinical utility of the algorithm (including the MOPAT) will be collected. Analysis f the primary aim and its associated hypotheses will be accomplished using the interaction F-test from 2x4 repeated measures analysis or mixed linear modeling and Cochran's Q for 2x2 repeated measures on rank data. Descriptive approaches will be used for the secondary aims, including repeated measures analysis with post-hoc analyses if there are significant interaction effects, graphic techniques (e.g., spaghetti plots) to identify sub-groups, and frequency distributions for responses to questionnaires. This research is significant and innovative because it tests a pain algorithm using an instrument validated in non-communicative patients to guide pain management and improve pain- related outcomes in this vulnerable and understudied population.