Using data science to measure the impact of opioid agonist therapy in patients admitted with Staphylococcus aureus bloodstream infections

使用数据科学测量阿片类激动剂治疗对金黄色葡萄球菌血流感染患者的影响

• 批准号: 10164748
• 负责人: David Goodman
• 金额: $ 20.3万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10164748
• 关键词: Acute suppurative arthritis due to bacteria; Address; Admission activity; Algorithms; Area; Award; Bacteremia; Bacteria; Bacterial Infections; Behavior; Blood; Brain; Buprenorphine; Cessation of life; Clinical Research; Coagulation Process; Communicable Diseases; Data; Data Science; Development; Diagnosis; Disease Outcome; Distant; Drug usage; Economic Burden; Electronic Health Record; Embolism; Endocarditis; Epidemic; Equipment; Evidence based practice; Future; Geography; Goals; Gold; HIV risk; Head; Health; Health Expenditures; Heart; Hepatitis C; Hepatitis C Transmission; Hospitalization; Hospitals; Immunosuppression; Incidence; Individual; Infection; Injecting drug user; International Classification of Disease Codes; Investigation; Joints; K-Series Research Career Programs; Kidney; Life; Location; Lung; Machine Learning; Measures; Medical; Medicine; Mentors; Methadone; Methods; Natural Language Processing; Outcome; Outpatients; Patient-Focused Outcomes; Patients; Performance; Persons; Pharmaceutical Preparations; Phenotype; Physicians; Population; Practice Guidelines; Predisposition; Recording of previous events; Records; Research; Research Personnel; Research Training; Science; Scientist; Sepsis; Services; Staphylococcus aureus; Statistical Methods; Stream; Testing; Thrombus; Time; Training Programs; Travel; United States; United States National Institutes of Health; Veterans Health Administration; Water; Work; addiction; base; biomedical data science; blood treatment; career; classification algorithm; cohort; data format; data repository; efficacy testing; electronic data; experience; heroin use; hospital readmission; improved; injection drug use; innovation; mortality; multidisciplinary; novel; opioid agonist therapy; opioid epidemic; opioid injection; opioid misuse; opioid use; opioid use disorder; opioid user; overdose death; pathogenic bacteria; pathogenic virus; prescription opioid; responsible research conduct; screening; skin microbiota; substance use treatment; time interval; treatment services; trend; viral transmission

PROJECT SUMMARY/ABSTRACT This career development award will provide early career support for investigation of the management of infec- tious diseases in the setting of addiction in hospitals. The award will provide support for the candidate to develop expertise in the following areas: 1) addiction science research; 2) natural language processing; 3) machine learn- ing; 4) professional development; and 5) responsible conduct of research. For this, Dr. Goodman-Meza will be mentored by a multidisciplinary, cross-institutional team with expertise in addiction, infectious diseases, and data science. His primary mentor, Dr. Steve Shoptaw, has an extensive track record in addiction-related research and training of future independent investigators. His co-mentors include Dr. Alex Bui and Dr. Matthew B. Goetz. Dr. Bui is an expert in biomedical data science and heads NIH training programs in this field. Dr. Goetz has broad experience of productive infectious diseases clinical research within the Veterans Health Administration (VHA). The current opioid epidemic in the United States has been associated with an increase in infections, in particular hepatitis C and bacterial infections. Bacterial infections are the leading infectious diagnosis leading to hospitali- zation in individuals with an opioid use disorder (OUD), and incur significant healthcare expenditures. Despite the availability of opioid agonist therapy (OAT) in the form of methadone or buprenorphine, less than 20% of people with OUD actually receive OAT. Hospitalization for a bacterial infection may be an ideal time to initiate OAT, but the benefits of this practice are unknown. In this proposal, the candidate will assess the impact of initiating OAT in people who inject opioids admitted to the VHA due to a Staphylococcus aureus blood stream infection (bacteremia) – the most common bacterial pathogen among people who inject opioids. Using data already collected for 36,868 cases of S. aureus bacteremia (SAB) from the VHA electronic data repository, the candidate will address three research questions: 1) is a natural language processing algorithm (NLP) more ac- curate than a standard International Classification of Diseases (ICD) code-based approach at screening records to correctly identify individuals who inject opioids in a cohort of patients admitted with SAB; 2) what are the temporal and geographic trends of SAB in people who inject opioids and those who receive OAT at the facility- level; and 3) using a machine learning framework, what are the estimated impacts of OAT on patient centered outcomes – death, readmissions, leaving against medical advice, and subsequent outpatient engagement in OAT. These formative data will help the candidate to establish a productive early career as a physician-scientist and advise development of an OAT-delivery strategy to mitigate infectious complications of injection opioid use. Through this award, Dr. Goodman-Meza will establish himself as an expert physician-scientist at the intersection of infectious disease and addiction, poised to make significant contributions to this important area of medicine.

项目摘要/摘要 该职业发展奖将为感染管理的投资提供早期的职业支持 - 在医院成瘾的环境中，疾病的疾病。该奖项将为候选人提供支持 以下领域的专业知识：1）成瘾科学研究； 2）自然语言处理； 3）机器学习 - ; 4）专业发展； 5）负责任的研究。为此，Goodman-Meza博士将 由一个多学科的跨机构团队指导，具有成瘾，传染病和数据方面的专业知识 科学。他的主要导师史蒂夫·shoptaw博士在与成瘾有关的研究和 培训未来的独立研究人员。他的院长包括Alex Bui博士和Matthew B. Goetz博士。博士 Bui是生物医学数据科学的专家，并在该领域的NIH培训计划负责人。 Goetz博士有广泛的 在退伍军人卫生管理局（VHA）中，生产性传染病的经验。 当前美国的阿片类药物流行与感染的增加有关 丙型肝炎和细菌感染。细菌感染是导致住院的主要感染性诊断。 患有阿片类药物使用障碍（OUD）的个体的Zaation，并产生了大量的医疗保健支出。尽管 阿片类药物激动剂治疗（OAT）的可用性，形式为Meadadone或丁丙诺啡，不到20％ 有Oud的人实际上会收到燕麦。细菌感染的住院可能是启动的理想时机 燕麦，但是这种做法的好处是未知的。在此提案中，候选人将评估 在因金黄色葡萄球菌血液中注射阿片类药物而引发阿片类药物的人发起燕麦 感染（菌血症） - 注射阿片类药物的人中最常见的细菌病原体。使用数据 已经从VHA电子数据存储库中收集了36,868例金黄色葡萄球菌（SAB）， 候选人将解决三个研究问题：1）是一种自然语言处理算法（NLP）更多 策划比标准的国际疾病分类（ICD）基于筛查记录的方法 正确识别在允许SAB的一系列患者中注射阿片类药物的人； 2）什么是 SAB的临时和地理趋势是注射阿片类药物和在设施中燕麦的人的临时和地理趋势 - 等级; 3）使用机器学习框架，燕麦对以患者为中心的估计影响是什么 结果 - 死亡，再入院，违反医疗建议以及随后的门诊参与 燕麦。这些形成性数据将有助于候选人成为物理科学家的早期职业 并建议制定燕麦递送策略，以减轻注射OOID使用的感染并发症。 通过这个奖项，古德曼·梅扎（Goodman-Meza）博士将在交叉路口确立自己的身体科学家 传染病和成瘾的中毒，为这一重要的医学领域做出了重大贡献。