A Machine Learning Approach to Predicting Iatrogenic Withdrawal in Critically-ill Children

项目摘要 医源性戒断影响到57%的儿童谁接受镇静和镇痛药物， 儿科重症监护室（ICU），导致延迟恢复，患者和父母痛苦， （估计）每年有70 000名儿童长期住院。由于样本量的限制， 变量集，儿科ICU医源性撤药的研究主要集中在 单一风险因素、筛查工具和治疗方案，而不关注风险的早期识别 孩子该提案将利用一个全国性的电子健康记录衍生数据库， 儿科ICU患者，以调查全方位的风险因素、患者特征和实践模式 与医源性停用镇静剂和止痛药有关， 在出现戒断症状之前或在治疗过程的早期，我将通过首先识别风险来实现这一目标 因素，患者概况和实践模式与医源性撤回使用传统 生物统计技术。第二，我将使用除了时间依赖变量之外的已识别的风险因素， 例如生命体征和实验室值，以开发动态模型来预测发生医源性 使用新的监督机器学习方法对儿科ICU患者进行戒断治疗。第三，我会 在来自我所在机构的电子健康记录的本地数据集中外部验证动态预测模型 以确定该模型是否可以准确预测临床证实的医源性 戒断成功完成这些目标将导致开发一种分析工具， ICU中使用电子资源的儿童医源性戒断， 临床实践建议的研究是可行的，因为1）我强大的和富有成效的多学科 临床医生和数据科学导师团队，他们在我的导师团队的指导下每两周会面一次 包括Murray Pollack博士，儿科重症监护预测模型领域的领导者， 迈克尔贝尔，在神经重症监护的国家领导人，和2）最近可用的可靠，大型，多- 直接来自电子健康记录（EHR）的机构儿科数据库。K23奖项 该提案还将促进综合教学和导师主导的体验式培训计划，旨在 发展和完善我在大数据库研究，预测建模和发病率方面的知识和技能 与镇静和止痛药物给药相关。职业发展与研究 我的建议将使我的长期职业目标，这是成为一个独立资助的临床 研究人员专注于通过大数据研究预防医疗保健获得性发病率， 预测分析

PROJECT ABSTRACT Iatrogenic withdrawal affects up to 57% of children who receive sedative and analgesic medications in the pediatric intensive care unit (ICU), contributing to delayed recovery, patient and parental distress and prolonged hospitalization in (an estimated) 70,000 children per year. Due to limitations in sample size and variable sets, studies on iatrogenic withdrawal in pediatric ICUs have primarily focused on the association of single risk factors, screening tools, and treatment regimens, without attention to early identification of at-risk children. This proposal will leverage a national, electronic health record derived database of over 200,000 pediatric ICU patients to investigate the full spectrum of risk factors, patient profiles, and practice patterns associated with iatrogenic withdrawal from sedatives and analgesic medications that could identify children at risk prior to withdrawal symptoms or early in their treatment course. I will achieve this by first identifying risk factors, patient profiles and practice patterns associated with iatrogenic withdrawal using traditional biostatistical techniques. Second, I will use the identified risk factors in addition to time dependent variables, such as vital signs and laboratory values, to develop a dynamic model to predict risk of developing iatrogenic withdrawal in pediatric ICU patients using novel supervised machine learning methodology. Third, I will externally validate the dynamic prediction model in a local dataset from my institution’s electronic health record to determine if the model can accurately predict those patients who develop clinically confirmed iatrogenic withdrawal. Successful completion of these aims will lead to the development of an analytical tool to identify iatrogenic withdrawal in children in ICUs using electronic-based resources which can be operationalized into clinical practice. The proposed studies are feasible because of 1) my strong and productive multi-disciplinary team of clinician and data science mentors who meet biweekly under the guidance of my mentorship team including Dr. Murray Pollack, a leader in the field of predictive modelling in pediatric critical care and Dr. Michael Bell, a national leader in neurocritical care, and 2) the recent availability of reliable, large, multi- institutional pediatric databases derived directly from the electronic health record (EHR). This K23 award proposal will also facilitate an integrated didactic and mentor-led experiential training program designed to develop and refine my knowledge and skills in big database research, predictive modelling, and morbidity associated with sedative and analgesic medication administration. The career development and research proposal will enable my long-term career goal, which is to become an independently funded clinical investigator focused on the prevention of healthcare-acquired morbidity through big data research and predictive analytics.