Husebo Bettina S, Vislapuu Maarja, Cyndecka Malgorzata A, Mustafa Manal, Patrascu Monica
Department of Global Public Health and Primary Care, Centre for Elderly and Nursing Home Medicine, University of Bergen, Bergen, Norway.
Department of Nursing Home Medicine, Bergen, Norway.
Front Pain Res (Lausanne). 2022 Mar 17;3:847578. doi: 10.3389/fpain.2022.847578. eCollection 2022.
Many people with dementia (PwD) live and die with undiagnosed and untreated pain and are no longer able to report their suffering. Several pain assessment tools have been developed, tested, and implemented in clinical practice, but nursing home patients are reported to be still in pain. Clinicians and research groups worldwide are seeking novel approaches to encode the prediction, prevalence, and associations to pain in PwD.
The data in this analysis are acquired from the COSMOS study, a cluster-randomized controlled trial (2014 to 2015), aimed to improve the quality of life in nursing home patients ( = 723) through the implementation of a multicomponent intervention. We utilize baseline data of PwD ( = 219) with complete datasets of pain and agitation.
Systems analysis explores the relationship between pain and agitation using the Mobilization-Observation-Behavior-Intensity-Dementia (MOBID-2) Pain Scale, Cohen-Mansfield Agitation Inventory (CMAI), and Neuropsychiatric Inventory-Nursing Home version (NPI-NH). For each patient, the individualized continuous time trajectory, and rates of change of pain and agitation are estimated. We determine the relationship between these rates by analyzing them across the entire group.
We found that the new analysis method can generate individualized estimations for pain and agitation evolution for PwD, as well as their relationship. For 189 of 219 PwD, results show that whenever pain increases or decreases, agitation does too, with the same rate. The method also identifies PwD for whom pain or agitation remains constant while the other varies over time, and patients for whom agitation and pain do not change together. The algorithm is scalable to other variables and compatible with wearable devices and digital sensors.
We presented a new approach to clinical data analysis using systems concepts and algorithms. We found that it is possible to quantify and visualize relationships between variables with a precision only dependent on the precision of measurements. This method should be further validated, but incipient results show great potential, especially for wearable-generated continuous data.
许多痴呆症患者在未被诊断和治疗疼痛的情况下生活直至死亡，且已无法报告自身的痛苦。已经开发、测试并在临床实践中应用了多种疼痛评估工具，但据报道疗养院患者仍处于疼痛之中。全球的临床医生和研究团队都在寻找新方法来对痴呆症患者疼痛的预测、患病率及相关性进行编码。
本分析中的数据来自COSMOS研究，这是一项整群随机对照试验（2014年至2015年），旨在通过实施多组分干预来改善疗养院患者（n = 723）的生活质量。我们使用了痴呆症患者（n = 219）的基线数据，这些数据包含完整的疼痛和激越数据集。
系统分析使用动员-观察-行为-强度-痴呆（MOBID-2）疼痛量表、科恩-曼斯菲尔德激越量表（CMAI）和神经精神科问卷-疗养院版（NPI-NH）来探究疼痛与激越之间的关系。对于每位患者，估计其个体化的连续时间轨迹以及疼痛和激越的变化率。我们通过对整个群体进行分析来确定这些变化率之间的关系。
我们发现新的分析方法可以为痴呆症患者的疼痛和激越演变及其关系生成个体化估计。对于219名痴呆症患者中的189名，结果表明，每当疼痛增加或减少时，激越也会以相同的速率增加或减少。该方法还能识别出疼痛或激越保持不变而另一个随时间变化的痴呆症患者，以及激越和疼痛不同时变化的患者。该算法可扩展到其他变量，并与可穿戴设备和数字传感器兼容。
我们提出了一种使用系统概念和算法进行临床数据分析的新方法。我们发现可以仅依赖测量精度来精确量化和可视化变量之间的关系。这种方法应进一步验证，但初步结果显示出巨大潜力，特别是对于可穿戴设备生成的连续数据而言。
