Machine Learning Risk Prediction of Kidney Disease After Extremely Preterm Birth

项目总结 这个为期3年的以患者为导向的导师研究职业发展奖的总体目标是发展 提高极早产儿对肾脏疾病风险认识的工具，并支持 将凯亚·桑德森博士培养成一名独立的调查员。桑德森医生是一位积极的临床医生 北卡罗来纳大学教堂山分校(UNC)研究员，对早期识别特别感兴趣 儿童肾脏疾病的风险。在接下来的三年里，桑德森博士将与她的导师委员会合作 在她之前3年的KL2支持的培训基础上，朝着独立的目标前进。她的导师身份 委员会包括长期新生儿结局研究(O‘Shea，Laughon)、新生儿肾病方面的专家 (Askenazi)、机器学习(Kosorok)、定性研究(Flythe)和临床决策支持科学 (Kistler)。每个成员都有指导初级教员的既定记录，并得到一致的同行评审 支持，以及较高的研究效率。桑德森博士的职业发展目标是：1)获得多项 中心研究领导经验；2)培养针对大数据风险的机器学习方法技能 3)发展定性研究技能，以支持机器学习模型的实际应用； 4)获得研究指导技能；5)改进出版物和拨款撰写，为NIH做准备 R01应用程序。北卡罗来纳大学建立了良好的研究和培训环境，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 通过北卡罗来纳州的翻译和临床课程，进行指导研究和研讨会 科学研究所R-写作小组。本研究项目的具体目标是：1)利用机器学习 在产前、新生儿和早期生命暴露变量的大型数据库中预测肾脏的方法 早产青少年的疾病；2)开发和评估一种 用于极早产后儿童肾脏疾病的基于网络的风险分层工具原型 假设变量可以预测肾脏疾病。对现有的两个潜在队列进行扩展(非常 低胎龄新生儿环境对儿童健康结局的影响(ELGAN-ECHO) 早产促红细胞生成素神经保护试验(PENUT)队列)，桑德森博士将利用临床变量 确定儿科慢性肾脏疾病的“最高风险”和“风险”预测因素的组合 极早产后的孩子。这项研究将为R级NIH应用开发 最终确定的基于网络的风险预测工具由本提案通知公众传播，以扩大使用 机器学习将风险分层工具派生到其他医学复杂的儿科人群，并进行测试 风险预测工具的使用是否增加了关键干预措施(例如血管紧张素转换)的使用 酶抑制剂)，以防止高危儿童肾脏疾病的进展。

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.