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）是全球死亡率的主要原因之一， 与呼吸困难，咳嗽和功能能力降低的症状有关。虽然有许多科学 已经取得了进步来改善COPD患者的护理，仍然存在很大的不确定性 最佳管理。对于某些问题，临床试验数据一直存在冲突。对于其他人，临床试验 不可行。 COPD管理的不确定性已被问题进一步加剧 随机对照试验中的普遍性。研究表明，大多数患者 COPD由于其严格的纳入标准，基于特征，例如 年龄，合并症，吸烟状况和肺活量测定法。冲突或没有临床试验数据以及 可推广性促使对在常规临床实践中接受COPD治疗的患者进行“现实世界”研究。 鉴于在临床试验之外对吸入器疗法的依从性不佳，还需要研究以了解为什么 患者停止治疗。改善COPD患者的护理需要确定哪些疗法是 最有可能在常规临床实践和制定干预措施方面安全有效 可能是依附的。拟议研究的最终目标是补充来自 随机对照试验通过药物ePIDEMIologic研究，以完善COPD中的治疗策略。 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加重定义； （2）比较 疗法在COPD管理中的有效性和安全性，重点是持续的四个领域 临床不确定性； （3）制定吸入器依从性的临床预测规则，该预测包含关键 从自付费用和保险福利设计到与治疗有关的几个领域的变量 特征（例如，给药的频率）和COPD疾病严重程度。通过解决治疗效率，安全性， 在常规临床实践中接受治疗的患者之间的依从性，拟议的研究将收集新颖 对COPD管理的洞察力，特别是对于临床试验中人数不足的患者， 包括老年人，种族和少数民族，妇女以及具有复杂合并症的人。 Feldman博士作为具有公共卫生经验的实践脉冲医生具有独特的背景。这 K08提出了一项教育计划，该计划将帮助他建立药物ePidemiology的新技能。他会收到的 塞巴斯蒂安·施尼维斯（Sebastian Schneeweiss）博士的遗产，药物电子学的先驱和亚伦·凯塞尔海姆（Aaron Kesselheim）博士， 药品政策和使用的领先权威，并将依靠一个科学顾问团队 机器学习专业知识（Joshua Lin博士），数据管理（Shirley Wang博士），生物统计学（Robert博士 Glynn），老年处方（Jerry Avorn博士）和COPD流行病学（Edwin Silverman博士）。这个奖项将 向费尔德曼博士提供成为独立调查员所需的工具。