A protocol for chronic pain outcome measurement enhancement by linking PROMIS-29 scale to legacy measures and improving chronic pain stratification.
A protocol for chronic pain outcome measurement enhancement by linking PROMIS-29 scale to legacy measures and improving chronic pain stratification.
复制标题
通过将PROMIS-29量表与传统测量相联系并改善慢性疼痛分层来增强慢性疼痛结果测量的协议。
DOI：
10.1186/s12891-020-03696-2
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发表时间：
2020-10-10
影响因子：
2.3
通讯作者：
Hays RD
中科院分区：
文献类型：
--
作者：
Herman PM;Edelen MO;Rodriguez A;Hilton LG;Hays RD
Substantial investment has gone into research on the efficacy and effectiveness of pharmaceutical and nonpharmacologic interventions for chronic pain. However, synthesizing this extensive literature is challenging because of differences in the outcome measures used in studies of similar or competing interventions. The absence of a common metric makes it difficult to replicate findings, pool data from multiple studies, resolve conflicting conclusions, or reach consensus when interpreting findings. Aim 1 will develop and evaluate links between the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS®-29) and legacy measures used for chronic pain such as the Roland-Morris Disability Questionnaire (RMDQ) and the Oswestry Disability Index (ODI). We will assess the best method of score linking and create crosswalk tables. Aim 2 will evaluate and refine the Impact Stratification Score (ISS) based on 9 PROMIS-29 items and proposed by the NIH Research Task Force on chronic low back pain. We will evaluate the ISS in terms of other indicators of condition severity and patient prognosis and outcomes and identify cut-points to stratify chronic pain patients into subgroups. Aim 3 will evaluate the strengths and limitations of MTurk as a data collection platform for estimating chronic pain by comparing its data to other data sources. This study has a seven-member Advisory Council of chronic pain experts. Preliminary analyses will be performed on data from several large existing datasets; intermediate analyses will be performed using primary data collected from Amazon’s Mechanical Turk (MTurk); and cross-validation will use primary data collected from a nationally-representative, probability-based panel. Target sample size for both primary datasets is 1500. The three study aims are as follows: The accomplishment of Aims 1 and 2 will allow direct comparison of results across past and future studies of chronic pain. These comparisons will help us to understand different results from seemingly similar studies, and to determine the relative effectiveness of all pharmaceutical and nonpharmacologic interventions for chronic pain across different trials. Aim 3 findings will provide valuable information to researchers about the pros and cons of using the MTurk platform for research-based data collection. ClinicalTrials.gov: NCT04426812; June 10, 2020.
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影响因子：
4.1
作者：
Burns, JW;Glenn, B;Lofland, K
通讯作者：
Lofland, K
影响因子：
3.1
作者：
Deyo, Richard A.;Dworkin, Samuel F.;Weiner, Debra K.
通讯作者：
Weiner, Debra K.
影响因子：
3.5
作者：
Cook, Karon F.;Kallen, Michael A.;Amtmann, Dagmar
通讯作者：
Amtmann, Dagmar
影响因子：
4
作者：
Abshire M;Dinglas VD;Cajita MI;Eakin MN;Needham DM;Himmelfarb CD
通讯作者：
Himmelfarb CD
影响因子：
4.5
作者：
Fayers, Peter M.;Hays, Ron D.
通讯作者：
Hays, Ron D.