Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania.
University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania.
Pain Med. 2019 Jun 1;20(6):1078-1092. doi: 10.1093/pm/pny180.
The goal of this study was to identify a limited set of pain indicators that were most predicive of physical pain. We began with 140 items culled from existing pain observation tools and used a modified Delphi approach followed by statistical analyses to reduce the item pool.
Through the Delphi Method, we created a candidate item set of behavioral indicators. Next, trained staff observed nursing home residents and rated the items on scales of behavior intensity and frequency. We evaluated associations among the items and expert clinicians' assessment of pain intensity.
Four government-owned nursing homes and 12 community nursing homes in Alabama and Southeastern Pennsylvania.
Ninety-five residents (mean age = 84.9 years) with moderate to severe cognitive impairment.
Using the least absolute shrinkage and selection operator model, we identified seven items that best predicted clinicians' evaluations of pain intensity. These items were rigid/stiff body or body parts, bracing, complaining, expressive eyes, grimacing, frowning, and sighing. We also found that a model based on ratings of frequency of behaviors did not have better predictive ability than a model based on ratings of intensity of behaviors.
We used two complementary approaches-expert opinion and statistical analysis-to reduce a large pool of behavioral indicators to a parsimonious set of items to predict pain intensity in persons with dementia. Future studies are needed to examine the psychometric properties of this scale, which is called the Pain Intensity Measure for Persons with Dementia.
本研究旨在确定一组最能预测躯体疼痛的有限疼痛指标。我们从现有的疼痛观察工具中筛选出 140 项指标,采用改良 Delphi 法和统计分析相结合的方法对指标集进行了简化。
通过 Delphi 法,我们创建了一个行为指标的候选项目集。然后,训练有素的工作人员观察养老院居民,并根据行为强度和频率量表对项目进行评分。我们评估了项目之间的关联以及专家临床医生对疼痛强度的评估。
阿拉巴马州和宾夕法尼亚州东南部的四家政府所有的养老院和十二家社区养老院。
95 名(平均年龄=84.9 岁)患有中度至重度认知障碍的居民。
使用最小绝对收缩和选择算子模型,我们确定了七个最能预测临床医生评估疼痛强度的项目。这些项目包括身体或身体部位僵硬/强直、支撑、抱怨、表情丰富的眼睛、咧嘴、皱眉和叹气。我们还发现,基于行为频率评分的模型并不比基于行为强度评分的模型具有更好的预测能力。
我们采用两种互补的方法——专家意见和统计分析,从大量行为指标中简化为一组简洁的项目,以预测痴呆患者的疼痛强度。未来需要研究该量表的心理测量特性,该量表称为痴呆患者疼痛强度测量量表。