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: 10618404
• 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/10618404
• 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; Classification; 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; Equipment Contamination; Evidence based practice; Future; Geography; Goals; HIV risk; Health; Health Expenditures; Heart; Hepatitis C; Hepatitis C Transmission; Hospitalization; Hospitals; Immunosuppression; Incidence; Individual; Infection; Injecting drug user; Inpatients; Institution; 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; Opioid; Outcome; Outpatients; Overdose; Patient Admission; Patient-Focused Outcomes; Patients; Performance; Persons; Pharmaceutical Preparations; Phenotype; Physicians; Population; Practice Guidelines; Predisposition; Productivity; Recording of previous events; Records; Research; Research Personnel; Science; Scientist; Sepsis; Services; Staphylococcus aureus; Statistical Methods; Stream; Testing; Thrombus; Time; Training; Training Programs; Travel; United States; United States National Institutes of Health; Veterans Health Administration; Water; Work; addiction; biomedical data science; blood treatment; career; classification algorithm; cohort; data repository; efficacy testing; electronic data; experience; heroin use; hospital readmission; improved; injection drug use; innovation; machine learning framework; mortality; multidisciplinary; novel; opioid agonist therapy; opioid epidemic; opioid injection; opioid misuse; opioid use; opioid use disorder; opioid user; 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 该职业发展奖将为早期的职业支持，以调查Infec的管理 在增加医院的情况下，有疾病。 以下领域的专业知识：1）成瘾科学研究； 2）自然语言处理； ; 4）专业发展； 由一支多学科的跨国团队指导，具有经验，除了传染病和数据 科学。 对未来的独立调查员的培训。 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。 在退伍军人卫生管理局（VHA）中，生产性传染性传染病的经验。 美国HASS当前的阿片类药物流行与发作性感染有关，特别是 肝炎和细菌感染。 尽管有阿片类药物使用障碍（OUD）的个体，但尽管有大量的医疗保健支出。 美沙酮或丁丙诺啡的形式的阿片类药物激动剂治疗（OAT）的可用性，不到20％ Oud Oud Oud的人会接受燕麦的燕麦。 燕麦，但是这种做法的好处是未知的。 向注射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双臂病原体。 已经收集了来自VHA电子数据存储库的36,868例金黄色葡萄球菌（SAB）病例 候选人将解决三个研究问题：1）是一种母语处理算法（NLP） 策划比标准的国际疾病分类（ICD）基于筛查记录的方法 正确地将阿片类药物注入SAB的患者同类； 2） 在注射阿片类药物和收到燕麦脚趾设施的人中，SAB的时间和地理趋势 - 级别； 3）使用机器学习框架，燕麦燕麦燕麦燕麦的估计影响是什么 成果 - 死亡，再入院，违反医疗建议以及随后的门诊参与 燕麦。 以及燕麦递送策略来减轻感染并发症的并发症。 通过这个奖项，古德曼·梅扎（Goodman-Meza 感染和补充，为这一重要的医学领域做出了重大贡献。