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

预测危重儿童医源性戒断的机器学习方法

• 批准号: 10456173
• 负责人: Anita K. Patel
• 金额: $ 15.25万
• 依托单位: CHILDREN'S RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10456173
• 关键词: Absence of pain sensation; Address; Admission activity; Adverse event; Affect; Age; Analgesics; Anxiety; Applications Grants; Artificial Intelligence; Attention; Award; Big Data; Biometry; Caring; Child; Child Care; Childhood; Circadian Dysregulation; Circadian Rhythms; Clinical; Clinical Investigator; Critical Care; Critically ill children; Data; Data Element; Data Science; Data Set; Databases; Development; Diagnostic; Distress; Dose; Early identification; Electronic Health Record; Feasibility Studies; Fentanyl; Functional disorder; Funding; Future; Goals; Grant; Hallucinations; Health; Healthcare; Hospitalization; Hospitals; Iatrogenesis; Institution; Intensive Care Units; Intervention; Investigation; Knowledge; Laboratories; Machine Learning; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Midazolam; Mission; Modeling; Morbidity - disease rate; National Institute of Child Health and Human Development; Neurologic; Patient risk; Patients; Pattern; Pediatric Hospitals; Pediatric Intensive Care Units; Performance; Pharmaceutical Preparations; Phenotype; Physiological; Predictive Analytics; Predisposition; Prevention; Procedures; Programming Languages; Recovery; Regression Analysis; Research; Research Personnel; Research Proposals; Resources; Risk; Risk Factors; Sample Size; Scientist; Screening procedure; Sedation procedure; Seizures; Symptoms; Techniques; Testing; Time; Training; Training Programs; Treatment Protocols; Withdrawal; Withdrawal Symptom; analytical tool; base; career; career development; clinical practice; cohort; data management; deep neural network; design; gastrointestinal; large datasets; machine learning method; machine learning prediction; medication administration; multidisciplinary; next generation; novel; pediatric patients; predictive modeling; profiles in patients; sedative; side effect; skills; supervised learning; time interval

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.

项目摘要 在接受镇静和镇痛药物治疗的儿童中，医源性戒断影响高达 57% 儿科重症监护病房 (ICU)，导致康复延迟、患者和家长的痛苦以及 由于样本量和时间的限制，每年（估计）有 70,000 名儿童长期住院。 变量集，关于儿科 ICU 医源性戒断的研究主要集中在 单一危险因素、筛查工具和治疗方案，而不注重早期识别高危人群 该提案将利用一个包含超过 200,000 个国家电子健康记录的数据库。 儿科 ICU 患者调查全方位的危险因素、患者概况和实践模式 与镇静剂和镇痛药物的医源性戒断有关，可以识别儿童 我将通过首先识别风险来实现这一点。 与传统医源性戒断相关的因素、患者概况和实践模式 其次，除了时间相关变量之外，我还将使用已确定的风险因素， 例如生命体征和实验室值，开发动态模型来预测发生医源性的风险 使用新型监督机器学习方法对儿科 ICU 患者进行戒断治疗 第三，我会。 从我机构的电子健康记录中外部验证本地数据集中的动态预测模型 确定该模型是否可以准确预测那些发生临床证实的医源性疾病的患者 成功完成这些目标将导致开发一种分析工具来识别退出。 使用可操作化的电子资源对 ICU 儿童进行医源性戒断治疗 所提出的研究是可行的，因为 1）我强大而富有成效的多学科知识。 由临床医生和数据科学导师组成的团队，在我的导师团队的指导下每两周举行一次会议 其中包括儿科重症监护预测模型领域的领导者 Murray Pollack 博士和 Michael Bell，神经重症监护领域的全国领导者，以及 2) 最近出现了可靠、大型、多功能的医疗设备 该 K23 奖项直接源自电子健康记录 (EHR)。 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 与镇静和镇痛药物管理相关的职业发展和研究。 该提案将使我的长期职业目标成为可能，即成为一名独立资助的临床医生 研究人员专注于通过大数据研究和预防医疗保健获得性发病率 预测分析。