Machine Learning Risk Prediction of Kidney Disease After Extremely Preterm Birth

项目摘要 这个为期3年的指导以病人为导向的研究职业发展奖的总体目标是发展 提高对极早产儿肾脏疾病风险认识的工具， 将凯亚·桑德森博士培养成独立调查员桑德森医生是一个积极的临床医生 我是查佩尔山的北卡罗来纳州大学的研究员，对早期识别特别感兴趣 儿童肾脏疾病在接下来的3年里，桑德森博士将与她的导师委员会 在她之前3年KL 2支持的培训的基础上，朝着她独立的目标迈进。她的指导 委员会包括长期新生儿结局研究（O 'Shea，Laughon），新生儿肾脏学 （Askenazi）、机器学习（Kosorok）、定性研究（Flythe）和临床决策支持科学 （奇石）。每个成员都有指导初级教师的既定记录，一致的同行评审 支持和高研究生产力。桑德森博士的职业发展目标是：1）获得多- 中心研究领导经验; 2）开发机器学习方法的技能，以应对大数据风险 分层; 3）发展定性研究技能，以支持机器学习模型的实际应用; 4)获得研究指导的技能; 5）提高出版和资助写作，为NIH做准备 R 01应用程序。研究和培训环境，以支持这些 目标.为了实现她的职业发展目标，桑德森博士将参加结构化的 课程，指导研究，并通过北卡罗来纳州翻译和临床研讨会 科学研究所R-写作小组。该研究项目的具体目标是：1）利用机器学习 在产前、新生儿和生命早期暴露变量的大型数据库中预测肾脏疾病的方法 疾病的青少年出生极早产; 2）开发和评估的可用性和可接受性， 基于网络的极早产儿肾脏疾病风险分层工具原型 预测肾脏疾病的假设变量。扩展现有的两个前瞻性队列（极端 低出生年龄新生儿-环境对儿童健康结果的影响（ELGAN-ECHO）和 早产儿促红细胞生成素神经保护试验（PENUT）队列），Sanderson博士将利用临床变量， 确定儿童慢性肾脏疾病的“最高风险”和“风险”预测因子的组合， 极早产后的孩子。这项研究将是R级NIH应用程序开发的基础， 根据这一建议最后确定网上风险预测工具，供公众传播，以扩大 机器学习衍生的风险分层工具，以其他医学复杂的儿科人群，并测试 风险预测工具的使用是否增加了关键干预措施的使用（例如血管紧张素转换 酶抑制剂），以预防高危儿童的肾脏疾病进展。

PROJECT SUMMARY The overall goals of this 3 year mentored patient-oriented research career development award are to develop tools to improve recognition of kidney disease risks among extremely preterm born children and to support the development of Dr. Keia Sanderson into an independent investigator. Dr. Sanderson is a motivated clinical researcher at the University of North Carolina at Chapel Hill (UNC), with a specific interest in early recognition of kidney disease in children. Over the next 3 years, Dr. Sanderson will work with her mentorship committee toward her goal of independence building upon her prior 3 years of KL2-supported training. Her mentorship committee includes experts in long-term neonatal outcomes research (O’Shea, Laughon), neonatal nephrology (Askenazi), machine learning (Kosorok), qualitative research (Flythe), and clinical decision support science (Kistler). Each member has an established track record of mentoring junior faculty, consistent peer-reviewed support, and high research productivity. Dr. Sanderson’s career development objectives are to: 1) gain multi- center research leadership experience; 2) develop skills in machine learning methods for large data risk stratification; 3) develop qualitative research skills to support practical application of machine learning models; 4) acquire skills in research mentorship; and 5) improve publication and grant writing in preparation for NIH R01 applications. The research and training environment at UNC is well established to support these objectives. To achieve her career development objectives, Dr. Sanderson will participate in structured coursework, conduct mentored research, and workshops through North Carolina Translational and Clinical Sciences Institute R-Writing Group. The specific aims of this research project are to 1) utilize machine learning approaches within large databases of prenatal, neonatal, and early life exposure variables to predict kidney disease in adolescents born extremely premature; 2) develop and evaluate the usability and acceptability of a prototype web-based risk stratification tool for pediatric kidney disease after extremely preterm birth using variables hypothesized to predict kidney disease. Expanding on two existing prospective cohorts (Extremely Low Gestational Age Newborn-Environmental influences on Child Health Outcomes (ELGAN-ECHO) and the Preterm Erythropoietin Neuroprotection Trial (PENUT) cohorts), Dr. Sanderson will utilize clinical variables to identify the combination of “at-highest risk,” and “at-risk” predictors for pediatric chronic kidney disease in children after extremely preterm birth. This research will be the basis for R-level NIH applications to develop a finalized web-based risk prediction tool informed by this proposal for public dissemination, to expand the use of machine learning derived risk stratification tools to other medically complex pediatric populations, and to test whether use of risk prediction tools increases the use of critical interventions (e.g. angiotensin converting enzyme inhibitors) to prevent kidney disease progression in high risk children.