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)是世界范围内主要的死亡原因之一， 与呼吸困难、咳嗽和功能减退的症状有关。虽然无数的科学研究 在改善COPD患者护理方面取得了进展，但仍有相当大的不确定性 优化管理。对于一些问题，临床试验数据是相互矛盾的；对于另一些问题，临床试验 都是不可行的。慢性阻塞性肺疾病管理中的不确定性因问题而进一步加剧 在随机对照试验中的概括性。研究表明，大多数患者患有 COPD没有资格参加这样的试验，因为它们基于以下特征的严格的纳入标准 年龄、合并症、吸烟状况和肺活量测定。冲突或缺失的临床试验数据和问题 概括性促使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。 鉴于临床试验以外的吸入器治疗依从性很差，也需要研究来理解为什么 患者停止治疗。改善COPD患者的护理需要确定哪些治疗方法 最有可能在常规临床实践中安全有效，并开发针对那些最不安全的人的干预措施 很可能是追随者。拟议研究的最终目标是补充现有的数据 通过药物流行病学研究改进COPD治疗策略的随机对照试验。 拟议的研究将通过使用大型的纵向医疗数据库来实现这一目标 追求三个具体目标：(1)验证基于索赔的慢性阻塞性肺病加重定义；(2)比较 治疗在COPD管理中的有效性和安全性，重点是正在进行的四个领域 临床不确定性；以及(3)开发吸入器依从性的临床预测规则，其中包括关键 几个领域的变量，从自付成本和保险福利设计到与治疗相关的 特征(例如，给药频率)和COPD疾病严重程度。通过解决治疗的有效性、安全性、 和在常规临床实践中接受治疗的患者的依从性，拟议的研究将收集新的 对慢性阻塞性肺疾病的管理，特别是对在临床试验中代表性不足的患者的见解， 包括老年人、种族和少数民族、妇女和患有复杂并存疾病的人。 作为一名具有公共卫生经验的执业肺科医生，费尔德曼博士有着独特的背景。这 K08提出了一项教育计划，将帮助他在药物流行病学方面建立新的技能。他将收到 来自药物流行病学先驱Sebastian Schneweiss博士和Aaron Kesselheim博士的指导， 是药品政策和使用方面的领先权威，并将依靠一支科学顾问团队， 擅长机器学习(Joshua Lin博士)、数据管理(Shirley Wang博士)、生物统计学(Robert博士 老年处方(曾傑瑞·埃沃恩博士)和慢性阻塞性肺病流行病学(埃德温·西尔弗曼博士)。这一奖项将 为费尔德曼博士提供成为独立调查员所需的工具。