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

通过纳入非传统风险因素改善血液透析患者不良结果的风险预测

• 批准号: 10327321
• 负责人: Lili Chan
• 金额: $ 19.01万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10327321
• 关键词: Advisory Committees; Affect; Age; Alcohol abuse; Algorithms; Appointment; Area; Award; Big Data; Bioinformatics; Biometry; Blood Vessels; Caring; Centers for Disease Control and Prevention (U.S.); Chronic; Chronic Disease; Clinical; Clinical Investigator; Clinical Research; Coca; Code; Communities; Complex; Computer software; Computing Methodologies; Consumption; Data; Data Analyses; Data Element; Data Reporting; Data Set; Diagnosis; Economics; Education; Electronic Health Record; End stage renal failure; Environment; Faculty; Family; Foundations; Funding; Future; Goals; Health; Health Personnel; Health system; Healthcare; Hemodialysis; Hospitalization; International Classification of Disease Codes; Intervention; Intervention Trial; K-Series Research Career Programs; Kidney; Kidney Diseases; Knowledge; Lead; Link; Literature; Machine Learning; Maintenance; Manuals; Maps; Medical; Medicine; Mentors; Methodology; Modeling; Morbidity - disease rate; Natural Language Processing; Neighborhoods; Nephrology; New York City; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Polypharmacy; Population; Process; Psychosocial Factor; Quality of life; Reference Standards; Research; Research Institute; Research Methodology; Research Personnel; Resources; Risk; Risk Factors; Self Care; Sensitivity and Specificity; Serum Albumin; Social Identification; Social outcome; Social support; Standardization; Statistical Methods; Substance abuse problem; Surveys; Symptoms; Techniques; Technology; Testing; Text; Time; Training; Treatment/Psychosocial Effects; Work; adverse outcome; base; built environment; career development; cohort; data standards; discrete data; electronic structure; experience; follow-up; health care availability; health data; health economics; high risk; high risk population; hospital readmission; hospitalization rates; improved; individual patient; machine learning method; machine learning model; medical schools; member; mid-career faculty; mortality; multidimensional data; multidisciplinary; novel; prediction algorithm; professor; prospective; risk prediction; risk prediction model; risk stratification; social; social health determinants; social relationships; standard measure; tool; transportation access

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.

项目概要： 候选人：此申请的主要目的是支持陈丽丽博士的职业发展， 独立资助的临床研究者利用电子健康记录（EHR）并改善风险 通过纳入以下社会因素预测血液透析（HD）患者的不良结局： 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 Steven Coca博士，医学副教授兼肾脏病学临床研究主任， 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。她的顾问团队包括翁博士， 机器学习和自然语言处理（NLP）专家Alex Federman博士， 对心理社会因素对病人护理的影响的文献有很大贡献。 Mazumdar说，他是生物统计学和风险预测建模方面的专家。陈博士提出的培训计划重点是 四个领域，（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：西奈山伊坎医学院是全国研究的领导者。特别是 肾脏科有30多名受资助的研究人员，并成功地指导了5名教员 从K奖到R 01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管理。 对高危群体进行分层和识别，以便对有针对性的干预措施进行测试。这个项目 利用前瞻性收集的调查和回顾性图表审查的一个队列的不同患者的慢性 接受四个肾脏研究所和六个西奈山卫生系统血液透析单位治疗的HD 遍布纽约市。本研究的具体目的是：（1）确定关联 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进行比较;（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（3） 使用标准测量和 使用标准统计方法和机器学习研究健康的社会决定因素。本研究 利用新的计算方法来研究健康的社会决定因素之间的联系， HD患者的住院治疗，并将SDOH纳入风险预测模型， 识别高危HD患者，以便纳入未来的干预试验。这项提案的结果是， 未来R 01研究的基础是在外部数据集中验证这些发现，并测试 EHR集成了减少住院、再入院和死亡率的临床决策工具。