Generating Evidence on Deprescribing Safety

项目总结：Geodes R01 AG070047-01 患有多种慢性病(MCC)的老年人，特别是患有阿尔茨海默病和相关痴呆症(ADRD)的老年人，多药联用、药物不良事件、治疗负担和药物副作用导致的认知变化的风险增加。停药是在医疗保健专业人员的监督下，减少或停止不适当的药物或不太可能有益的药物的过程。患者、ADRD患者的照顾者和临床医生将停药后潜在的不良药物停用事件(ADWE)列为取消处方的关键障碍。ADWE通过停药试验没有得到很好的报道，也没有大规模干预措施报告ADWE的患病率或预测因素。此外，在门诊护理中出现的ADWE很少被捕捉到。缺乏证据在很大程度上是由于衡量ADWE的方法不充分：识别和衡量ADWE需要知道停药的时间，并了解随后的症状或诊断是否构成ADWE。我们对OPTMIZE干预ADRD中的停药教育的安全性监测的经验(R33AG057289，MPIS Bayliss/Boyd)是一项务实的整群随机试验，旨在提高ADRD患者、他们的家人和初级保健临床医生的停药意识，证明了这些方法学上的差距，并为拟议的工作提供了动力。该项目将利用ADRD人口安全监测的知识和方法进行优化，结合科罗拉多州凯撒永久医院(KPCO)电子病历和虚拟数据仓库(VDW)丰富的纵向临床和药学数据，开发和测试方法，以生成关于非处方安全性的可靠证据。我们的研究人群将是患有MCC的老年人和痴呆症合并MCC的优化人群。目标1将使用记录审查和裁决来确定经历药物停用和随后的ADWE的个人。AIM 2将开发和验证一种文本挖掘工具，以识别适用于多种药物类别的药物停用。AIM 3将在两个不同的队列中将文本挖掘工具与常见的ADWE诊断相结合，以演示一种基于数据的方法来识别药物停用和可能的ADWE：患有MCC的老年人样本和痴呆症+MCC的最佳人群。目的4将对ADRD和MCC人群中预测ADWE的患者和临床因素进行建模。要产生足够的证据来支持对ADRD和MCC人群进行安全有效的停药，需要新的基于数据的方法和途径。

PROJECT SUMMARY: GEODES R01 AG070047-01 Older adults with multiple chronic conditions (MCC), especially those with Alzheimer’s Disease and Related Dementias (ADRD), have an increased risk of polypharmacy, adverse drug events, treatment burden, and cognitive changes from medication side effects. Deprescribing is the process of reducing or stopping inappropriate medications or medications unlikely to be beneficial supervised by a health-care professional. Patients, caregivers for people with ADRD, and clinicians list potential adverse drug withdrawal events (ADWE) following medication discontinuation as a key barrier to deprescribing. ADWE are not well reported by deprescribing trials, and no large-scale interventions have reported prevalence or predictors of ADWE. Further, ADWE presenting in ambulatory care are rarely captured. This lack of evidence is largely due to inadequate methods for measuring ADWE: Identifying and measuring ADWE requires knowing the timing of medication discontinuation and understanding whether subsequent symptoms or diagnoses constitute ADWE. Our experience with safety monitoring for the OPTMIZE intervention of deprescribing education in ADRD (R33AG057289, MPIs Bayliss / Boyd), a pragmatic, cluster randomized trial to increase deprescribing awareness among patients with ADRD, their family, and primary care clinicians, has exemplified these methodologic gaps and provided the impetus for the proposed work. The project will leverage knowledge and methods from safety monitoring for the ADRD population in OPTIMIZE, combined with rich longitudinal clinical and pharmacy data from the Kaiser Permanente Colorado (KPCO) EHR and Virtual Data Warehouse (VDW) to develop and test methods to generate robust evidence on deprescribing safety. Our study populations will be older adults with MCC and the OPTIMIZE population with dementia plus MCC. Aim 1 will identify individuals who experience medication discontinuation and subsequent ADWE using record review and adjudication. Aim 2 will develop and validate a text mining tool to identify medication discontinuation applicable to multiple classes of medications. Aim 3 will apply the text mining tool in combination with common ADWE diagnoses in two separate cohorts to demonstrate a data-based approach to identifying medication discontinuation and possible ADWE: a sample of older adults with MCC and the OPTIMIZE population of individuals with dementia plus MCC. Aim 4 will model patient and clinical factors predictive of ADWE in the ADRD and MCC populations. Generating adequate evidence to support safe and effective deprescribing for ADRD and MCC populations requires new data-based methods and approaches.