Measuring Chronic Pain Impact

点击翻译按钮获取中文摘要

Summary/Abstract Collection of individual patient-reported outcome (PRO) data in clinical contexts is increasing and has been shown to enhance clinician-patient communication. It can be used to identify patient priorities and preferences, set treatment expectations, and support shared decision-making. At an aggregate level, PROs can be used to examine the clinical- and cost-effectiveness of treatments, measure and monitor quality, and optimize planning and delivery of healthcare services. The Patient-Reported Outcome Measurement Information System (PROMIS®) measures are attractive for use in clinical settings because of their psychometric soundness, straightforward interpretation, and standardized metrics normed to the general population. However, the existing PROMIS Profile measures are burdensome for routine clinical use, leading many to opt for shorter more limited measures within or outside the PROMIS library. Our overarching goal is to plan and conduct complex statistical analyses, including item response theory analyses to fill this gap by identifying a brief and psychometrically sound measure that future researcher can use to examine impacts of their interventions on whole person health outcomes that matter most to patients. Specifically, we propose to develop a Short PROMIS Profile measure (SPP) that can: 1) efficiently produce scores for eight health domains (i.e., physical function, pain interference, fatigue, sleep disturbance, depression, anxiety, social role participation, cognitive function); 2) generate physical and mental health summary scores; and 3) calculate the PROMIS preference (PROPr) score. Our approach includes ongoing input from invested stakeholders who will work closely with the study to ensure broad buy-in for the resulting SPP. Through our work on the parent grant, our study team has identified 50 unique PROMIS items for possible inclusion in the SPP and has access to a data source that includes responses to all candidate items. We will conduct analyses using the existing data and arrive at an optimal SPP through completion of the following specific aims: 1. Construct several candidate SPPs by identifying and combining optimally performing two-item sets for each of the 8 PROMIS domains being considered for inclusion in the SPP; 2. Reduce and refine the number of candidate measures based on: a) performance of general physical and mental health summary scores derived from factor analysis; and b) evaluation of candidate profile measures’ estimated PROPr scores; and 3. Select the final SPP specification in collaboration with the stakeholder group based on results from Aim 2. At the completion of these three aims we will have a robust SPP that can be included in the PROMIS library and made available for general use. The availability of this measure has the potential to significantly impact the frequency and usefulness of routine PRO data collection in clinical care and will also enable behavioral health researchers and clinical trialists to include a robust PROMIS scale in their surveys without excessive burden or loss in precision.