Improving risk prediction of adverse outcomes in hemodialysis patients by incorporating non-traditional risk factors

项目总结： 应聘者：这份申请的主要目的是支持陈丽丽博士的职业发展 独立出资的临床研究人员利用电子健康记录(EHR)并提高风险 纳入社会决定因素对血液透析(HD)患者不良结局的预测 健康。为了实现这一目标，陈博士组建了一个多学科的指导和咨询团队 医学副教授兼肾脏病临床研究主任史蒂文·可卡博士 西奈山伊坎医学院和共同导师彼得·科坦科博士，医学兼职教授 西奈山和肾脏研究所的研究主任。她的顾问团队由翁博士组成， 亚历克斯·费德曼博士是机器学习和自然语言处理(NLP)方面的专家，他拥有 对有关心理社会因素对病人护理的影响的文献做出了重大贡献。 生物统计学和风险预测建模方面的专家Mazumdar说。陈冯富珍博士提出的培训计划重点是 在四个方面，(1)先进的统计方法；(2)生物信息学；(3)以患者为中心的结果；(4) 职业发展。 环境：位于西奈山的伊坎医学院在研究方面处于全国领先地位。具体地说 肾病学部资助了30多名研究人员，并成功地指导了5名教职员工 从K奖到R01奖。 研究：鉴于HD患者的高发病率和死亡率，迫切需要更好的风险 对高危人群进行分层和识别，以便测试有针对性的干预措施。这个项目 利用前瞻性收集的调查和对不同慢性病患者队列的回顾图表 接受四个肾脏研究所和六个西奈山健康系统血液透析单元护理的HD 位于整个纽约市。研究的具体目的是：(1)确定关联性 使用调查研究方法在健康和住院的社会决定因素领域之间；(2) 使用自然处理语言以准确的方式识别健康的社会决定因素；以及 创建HD患者住院风险预测模型，使用标准措施和 使用标准统计方法和机器学习的健康的社会决定因素。这项研究 利用新的计算方法来检查健康和社会决定因素之间的关联 HD患者的住院情况并将SDOH纳入风险预测模型，这将允许 确定高危HD患者以纳入未来的干预试验。这项提案的结果是 未来R01研究的基础是在外部数据集中验证这些发现，并测试 EHR集成了临床决策工具，可减少住院、再入院和死亡率。

PROJECT SUMMARY: Candidate: The primary objective of this application is to support Dr. Lili Chan's career development into an independently funded clinical investigator leveraging electronic health records (EHR) and improve risk prediction of adverse outcomes in patients on hemodialysis (HD) by incorporating social determinants of health. To accomplish this goal, Dr. Chan has assembled a multidisciplinary mentoring and advisory team lead by Dr. Steven Coca, Associate Professor of Medicine and Director of Clinical Research in Nephrology at the Icahn School of Medicine at Mount Sinai, and co-mentor Dr. Peter Kotanko, Adjunct Professor of Medicine at Mount Sinai and Research Director of the Renal Research Institute. Her advisory team consists of Dr. Weng, an expert and in machine learning and natural language processing (NLP), Dr. Alex Federman, who has contributed significantly to the literature on the effects of psychosocial factors on patient care, and Dr. Mazumdar, an expert in biostatistics and risk prediction modeling. Dr. Chan's proposed training plan focuses on four areas, (1) advanced statistical methodology; (2) bioinformatics; (3) patient centered outcomes; and (4) career development. Environment: The Icahn school of Medicine at Mount Sinai is a national leader in research. Specifically the Division of Nephrology has over 30 funded investigators and has successfully mentored five faculty members from K awards to R01 awards. Research: Given the high morbidity and mortality of HD patients, there is a critical need for better risk stratification and identification of high risk groups in order for targeted interventions to be tested. This project utilizes prospectively collected surveys and retrospective chart review of a cohort of diverse patients on chronic HD who receive care from four Renal Research Institute and six Mount Sinai Health System hemodialysis units located throughout New York City. The Specific Aims of the research are: (1) to determine the association between domains of social determinants of health and hospitalizations using survey research methods; (2) to identify social determinants of health in an accurate manner using natural processing language; and (3) to create risk prediction models for hospitalization among patients on HD utilizing both standard measures and social determinants of health using standard statistical methods and machine learning. This research leverages novel computational methods to examine the association of social determinants of health and hospitalizations in HD patients and incorporates SDOH into risk prediction models which will allow for identification of high risk HD patients for inclusion in future intervention trials. The results of this proposal sets the foundation for future R01 studies validating these findings in external data sets and testing the utility of EHR integrated clinical decision tools on reducing hospitalizations, readmissions, and mortality.