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

描述（由申请人提供）：本项目响应PA-10-006疼痛研究的机制，模型，测量和管理（R 01）。不能自我报告疼痛存在或强度的急性病住院姑息治疗患者存在对疼痛认识不足和治疗不足的高风险，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，缺乏可靠和有效的措施来评估这些非交流患者的疼痛，并且很少有标准化的协议或算法被测试其对患者结局的影响。我们最近完成的项目（5 R 01 NR 009684）测试了多维客观疼痛评估工具（MOPAT）在评估非交流性姑息治疗患者急性疼痛中的心理测量学特性和临床实用性，证明了经过培训的护士在重症非交流性患者中定期使用时有效性、可靠性和临床实用性的强有力证据。拟议研究的主要目的是测试与未使用该算法的患者相比，合并MOPAT和镇痛顺序集的疼痛算法是否改善了在内科、外科和创伤重症监护室住院的重症非交流性姑息治疗患者的疼痛严重程度和药理学疼痛管理策略的使用。我们假设：（H1）疼痛严重程度的降低，（H2）用于疼痛的药理学试剂的数量的增加，和（H3）由于疼痛算法的实施，用于疼痛的药理学试剂的总等镇痛剂量的增加。次要描述性目的是（S1）比较伴有和不伴有疼痛相关疾病的患者的疼痛相关结局，（S2）描述患者疼痛随时间推移的模式，（S3）评价护士对临床治疗的看法。 疼痛算法的实用性。我们将采用队列对照组准实验设计，分两个阶段在一所主要大学急症医院的内科、外科和创伤重症监护室进行。在I期（常规治疗对照队列n=150例患者）中，将MOPAT纳入常规临床实践，并收集疼痛严重程度和药物疼痛管理策略使用的数据。在II期（干预队列n=150例患者）中，将在常规临床实践中实施将MOPAT评分纳入镇痛顺序集的疼痛算法，并将收集相同的患者结局数据以及护士对算法（包括MOPAT）临床效用的看法。主要目的及其相关假设的分析将使用来自2x 4重复测量分析或混合线性建模的交互作用F检验和秩数据2x2重复测量的Cochran Q完成。描述性方法将用于次要目的，包括重复测量分析和事后分析（如果存在显著的相互作用效应）、图形技术（例如，spaghetti图）以识别子组，以及对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人群的疼痛相关结局。

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.