Improving COPD Outcomes: Using Real-World Data to Analyze Treatment Effectiveness, Safety, and Adherence

改善慢性阻塞性肺病的治疗效果：使用真实世界数据分析治疗效果、安全性和依从性

• 批准号: 10590302
• 负责人: William Brand Feldman
• 金额: $ 16.25万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-19 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590302
• 关键词: Activities of Daily Living; Address; Adherence; Adrenal Cortex Hormones; Age; Agonist; Algorithms; Area; Authorization documentation; Award; Azithromycin; Biometry; Budesonide; Caring; Cause of Death; Characteristics; Chronic; Chronic Obstructive Pulmonary Disease; Clinical; Clinical Trials; Code; Complex; Computerized Medical Record; Costs and Benefits; Coughing; Data; Databases; Disease; Disease Outcome; Dose; Dryness; Dyspnea; Elderly; Epidemiology; Face; Frequencies; Future; Glean; Glycopyrrolate; Goals; Grant; Guidelines; Health system; Healthcare; Healthcare Systems; Hospitalization; Inhalation; Inhalators; Insurance; Insurance Benefits; Intervention; Link; Literature; Lung; Machine Learning; Mentors; Mentorship; Morbidity - disease rate; Muscarinic Antagonists; Ontology; Outcome; Outcome Measure; Patient Care; Patients; Performance; Pharmaceutic Policy; Pharmaceutical Preparations; Pharmacoepidemiology; Pneumonia; Powder dose form; Predictive Value; Public Health; Randomized, Controlled Trials; Research; Research Personnel; Risk; Safety; Sample Size; Schedule; Scientific Advances and Accomplishments; Severity of illness; Shapes; Smoking Status; Spirometry; Symptoms; Techniques; Testing; Training; Treatment Effectiveness; Uncertainty; Woman; Work; authority; clinical practice; clinical predictors; comorbidity; comparative effectiveness; comparative effectiveness study; comparative safety; compare effectiveness; cost; data management; design; education planning; ethnic minority; experience; fluticasone; formoterol; improved; inclusion criteria; insight; insurance claims; longitudinal dataset; machine learning method; meter; mortality; novel; optimal treatments; racial minority; skills; tool; treatment guidelines; treatment strategy; trial comparing; trial design; validation studies

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality worldwide and is associated with symptoms of dyspnea, cough, and reduced functional capacity. While numerous scientific advances have been made to improve the care of patients with COPD, considerable uncertainty remains about optimal management. For some questions, clinical trial data have been conflicting; for others, clinical trials have not been feasible. Uncertainty in the management of COPD has been further compounded by questions of generalizability in randomized controlled trials. Studies have demonstrated that the majority of patients with COPD would not qualify for such trials because of their strict inclusion criteria based on characteristics such as age, comorbidities, smoking status, and spirometry. Conflicting or absent clinical trial data and questions of generalizability prompt the need for “real-world” studies of patients treated for COPD in routine clinical practice. Given poor adherence to inhaler therapy outside of clinical trials, studies are also needed to understand why patients discontinue therapy. Improving the care of patients with COPD requires identifying which therapies are most likely to be safe and effective in routine clinical practice and developing interventions to target those least likely to be adherent. The ultimate goal of the proposed research is to supplement existing data from randomized controlled trials with pharmacoepidemiologic studies to refine treatment strategies in COPD. The proposed research will accomplish this goal by using large, longitudinal healthcare databases to pursue three specific aims: (1) To validate claims-based definitions of COPD exacerbations; (2) To compare the effectiveness and safety of therapies in the management of COPD, focusing on four areas of ongoing clinical uncertainty; and (3) To develop a clinical prediction rule of inhaler adherence that incorporates key variables across several domains, from out-of-pocket costs and insurance benefit design to therapy-related features (e.g., frequency of dosing) and COPD disease severity. By addressing treatment effectiveness, safety, and adherence among patients treated in routine clinical practice, the proposed research will glean novel insights into the management of COPD, particularly for patients who are underrepresented in clinical trials, including older adults, racial and ethnic minorities, women, and those with complex co-morbidities. Dr. Feldman has a unique background as a practicing pulmonologist with public health experience. This K08 proposes an education plan that will help him build new skills in pharmacoepidemiology. He will receive mentorship from Dr. Sebastian Schneeweiss, a pioneer in pharmacoepidemiology, and Dr. Aaron Kesselheim, a leading authority on pharmaceutical policy and use, and will rely on a team of scientific advisors with expertise in machine learning (Dr. Joshua Lin), data management (Dr. Shirley Wang), biostatistics (Dr. Robert Glynn), geriatric prescribing (Dr. Jerry Avorn), and COPD epidemiology (Dr. Edwin Silverman). This award will provide Dr. Feldman with the tools needed to become an independent investigator.

慢性阻塞性肺疾病（COPD）是全球死亡的主要原因之一， 与呼吸困难、咳嗽和功能减退的症状有关。虽然许多科学 尽管在改善COPD患者的护理方面取得了进展，但仍存在相当大的不确定性， 优化管理对于某些问题，临床试验数据一直相互矛盾;对于其他问题，临床试验 是不可行的。COPD管理的不确定性因以下问题而进一步加剧 随机对照试验的普遍性。研究表明，大多数患者 COPD不符合此类试验的条件，因为其严格的入选标准基于以下特征， 年龄、合并症、吸烟状况和肺量测定。确认或缺失临床试验数据以及 普遍性提示需要在常规临床实践中对接受COPD治疗的患者进行“真实世界”研究。 考虑到临床试验以外的吸入器治疗依从性差，还需要进行研究以了解原因 患者停止治疗。改善COPD患者的护理需要确定哪些治疗方法 在常规临床实践中最有可能安全有效，并针对那些最不安全有效的患者制定干预措施。 很可能是粘附性的。拟议研究的最终目标是补充现有数据， 药物流行病学研究的随机对照试验，以完善COPD的治疗策略。 拟议的研究将通过使用大型纵向医疗保健数据库来实现这一目标， 追求三个具体目标：（1）验证基于索赔的COPD急性加重定义;（2）比较 治疗COPD的有效性和安全性，重点关注正在进行的四个领域 临床不确定性;（3）制定吸入器依从性的临床预测规则， 从自付费用和保险福利设计到治疗相关的多个领域的变量 特征（例如，给药频率）和COPD疾病严重程度。通过解决治疗有效性、安全性、 在常规临床实践中接受治疗的患者中，拟议的研究将收集新的 对COPD管理的见解，特别是对于在临床试验中代表性不足的患者， 包括老年人、少数种族和民族、妇女和患有复杂合并症的人。 费尔德曼博士有一个独特的背景，作为一个执业肺科医生与公共卫生经验。这 K 08提出了一个教育计划，将帮助他建立药物流行病学的新技能。他将获得 来自药物流行病学先驱塞巴斯蒂安施内维斯博士和亚伦凯塞尔海姆博士的指导， 制药政策和使用方面的领先权威，并将依靠一个科学顾问团队， 机器学习（约书亚林博士）、数据管理（王雪莉博士）、生物统计学（罗伯特 Glynn）、老年处方（Jerry Avorn博士）和COPD流行病学（Edwin Silverman博士）。这个奖项将 为费尔德曼博士提供成为独立调查员所需的工具。