Predicting In-hospital Cardiac Arrest Using Electronic Health Record Data

使用电子健康记录数据预测院内心脏骤停

• 批准号: 8617518
• 负责人: Matthew Michael Churpek
• 金额: $ 12.95万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8617518
• 关键词: Adult; Applications Grants; Area; Award; Biological Neural Networks; Biometry; Brain; Case-Control Studies; Categories; Cessation of life; Characteristics; Clinic; Clinical; Clinical Research; Clinical Trials; Complex; Critical Care; Data; Data Set; Databases; Decision Trees; Deterioration; Development; Diastolic blood pressure; Doctor of Philosophy; Early Diagnosis; Electronic Health Record; Emergency Situation; Ensure; Epidemiology; Event; Foundations; Frequencies; Future; Goals; Grant; Health; Health Sciences; Heart Arrest; Hospitalization; Hospitals; Hour; Hylobates Genus; Individual; Intensive Care; Intervention; Investigation; K-Series Research Career Programs; Laboratories; Life; Literature; Lung; Machine Learning; Master' s Degree; Medicine; Mentors; Mentorship; Methods; Modeling; Outcome; Outcomes Research; Patient Discharge; Patients; Physicians; Procedures; Public Health; Publishing; Receiver Operating Characteristics; Reporting; Research; Research Activity; Research Personnel; Resources; Rest; Resuscitation; Risk; Scientist; Sensitivity and Specificity; Social Sciences; Statistical Methods; Statistical Models; Supervision; Survival Analysis; Techniques; Testing; Time; Training; Uncertainty; United States; Update; Validation; Work; adverse outcome; base; career; cost effectiveness; design; evidence base; high risk; improved; model development; models and simulation; mortality; multidisciplinary; programs; public health relevance; simulation; skills; statistics; success; symposium; tool; ward

DESCRIPTION (provided by applicant): In-hospital cardiac arrest (IHCA) is a significant public health problem, afflicting over 200,000 patients in the United States annually with a mortality rate of approximately 80%. The majority of these patients show signs of clinical deterioration in the hours before the event. This has led to the development of vital sign-based early warning scores designed to detect high-risk patients before IHCA to trigger life-saving interventions. However, the vast majority of these risk scores were created subjectively in individual hospitals and have shown limited accuracy for detecting adverse outcomes. Developing an accurate risk score to detect patients at highest risk of IHCA is essential to decreasing preventable in-hospital death. In my prior work, I completed several studies investigating the accuracy of vital signs for predicting IHCA. These studies, previous literature, and my preliminary data have resulted in the following conclusions: 1) statistically developed risk scores are more accurate than previously published risk scores, 2) multicenter data is needed to create the most accurate and generalizable risk score, 3) additional data, such as laboratory results, will likely improve the accuracy of risk scores, and 4) a cutting-edge method for developing prediction models, called machine learning, may result in more accurate risk scores. Importantly, significant improvement in accuracy leads to better identification of patients at highest risk of IHCA and decreased resource utilization. Therefore, in this grant proposal I aim to develop and validate IHCA prediction models using different statistical techniques in a multicenter database and then estimate the impact of the most accurate risk score using simulation studies. I will do this by firt developing prediction models using classic survival analysis methods (Aim 1a) and machine learning methods, such as neural networks and decision trees (Aim 1b). Then, I will compare the models I develop to the most accurate previously published risk scores in Aim 2. Finally, I will investigate the impact of the most accurate model from Aim 2 on patient outcomes using simulation modeling (Aim 3). Completion of this proposal will result in a validated IHCA risk score that can be implemented in the electronic health record to trigger life- saving interventions to decrease preventable in-hospital death. In addition, this career development award will provide critical data to inform future R01-level awards, including a clinical trial to investigate he impact of the developed prediction model on patient outcomes. I will complete this project under the direct supervision of my mentor (Dr. David Meltzer), co-mentor (Dr. Dana Edelson), and the rest of my advisory team (Drs. Jesse Hall, Robert Gibbons, and Michael Kattan). Together, this multidisciplinary team brings nationally renowned expertise in in-hospital cardiac arrest, outcomes research, critical care, and clinical prediction modeling. In addition, they serve as Chairs of the Section of Hospital Medicine (Dr. Meltzer), Section of Pulmonary and Critical Care (Dr. Hall), and Quantitative Health Sciences at the Cleveland Clinic (Dr. Kattan), and Directors of the Center for Health and the Social Sciences (Dr. Meltzer), Center for Health Statistics (Dr. Gibbons), and Clinical Research for the Emergency Resuscitation Center (Dr. Edelson). The mentorship, expertise, and resources that they provide will ensure my success as I grow into an independent physician-scientist. My career goal is to become an independent critical care outcomes researcher with a focus on developing prediction models for clinical deterioration that will improve patient outcomes. To accomplish this long-term goal, I have three short-term goals: (1) to gain expertise in the development and implementation of clinical prediction models, (2) to create an IHCA prediction model that will identify high-risk patients on the wards to trigger life-saving interventions, and (3) to gain expertise in simulation modeling in order to study the impact of the developed prediction model. To accomplish these goals, I will build upon the foundation I developed when earning my Master's Degree in Public Health and during my initial training in the PhD program in the Department of Health Studies. Although my training to date has provided me with a strong background in epidemiology and biostatistics, further advanced training in biostatistics is crucial for my development into a successful independent researcher. An integrated program of didactic coursework, seminars, research activities, and conference participation will span the duration of the award. By accomplishing my three short- term goals, I will develop unique skills that will allow me to become a successful independent researcher. Specifically, the expertise I will gain in prediction model development, implementation, and simulation modeling can be applied not only to IHCA research but also to other areas of critical care medicine. In addition, completion of these goals will result in a validated IHCA prediction model that I will study in future implementation and cost-effectiveness studies and will serve as a basis for future R01-level grant submissions.

描述（由申请人提供）：院内心脏骤停（IHCA）是一个重要的公共卫生问题，每年在美国折磨超过20万名患者，死亡率约为80%。这些患者中的大多数在事件发生前数小时内表现出临床恶化的迹象。这导致了基于生命体征的早期预警评分的发展，旨在在IHCA之前发现高风险患者，以触发挽救生命的干预措施。然而，这些风险评分中的绝大多数是在个别医院主观创建的，并且在检测不良结局方面显示出有限的准确性。制定一个准确的风险评分，以检测IHCA风险最高的患者，对于减少可预防的住院风险至关重要。 死亡在我之前的工作中，我完成了几项研究，调查生命体征预测IHCA的准确性。这些研究、以前的文献和我的初步数据得出了以下结论：1）统计学上开发的风险评分比以前公布的风险评分更准确，2）需要多中心数据来创建最准确和可推广的风险评分，3）额外的数据，如实验室结果，将可能提高风险评分的准确性，以及4）开发预测模型的尖端方法，称为机器学习，可能会导致更准确的风险评分。重要的是，准确性的显著提高可以更好地识别出IHCA风险最高的患者，并降低资源利用率。因此，在这项资助提案中，我的目标是在多中心数据库中使用不同的统计技术开发和验证IHCA预测模型，然后使用模拟研究估计最准确的风险评分的影响。我将首先使用经典的生存分析方法（Aim 1a）和机器学习方法（如神经网络和决策树（Aim 1b））开发预测模型。然后，我将把我开发的模型与Aim 2中之前公布的最准确的风险评分进行比较。最后，我将使用模拟建模（目标3）研究目标2中最准确的模型对患者结局的影响。完成该提案将产生经过验证的IHCA风险评分，可在电子健康记录中实施，以触发挽救生命的干预措施 减少可预防的住院死亡。此外，该职业发展奖将为未来的R01级奖项提供关键数据，包括一项临床试验，以调查开发的预测模型对患者结局的影响。我将在我的导师（大卫梅尔策博士）、共同导师（达纳·埃德尔森博士）和我的顾问团队的其他成员（杰西·霍尔博士、罗伯特·吉本斯博士和迈克尔·凯布尔博士）的直接监督下完成这个项目。这个多学科团队共同带来了在医院心脏骤停，结果研究，重症监护和临床预测建模方面的全国知名的专业知识。此外，他们还担任克利夫兰诊所医院医学科（Meltzer博士）、肺部和重症监护科（Hall博士）和定量健康科学科（Kattan博士）的主席，以及健康中心主任和社会科学（Meltzer博士）、健康统计中心（Gibbons博士）和紧急复苏中心临床研究（Edelson博士）。他们提供的指导，专业知识和资源将确保我成长为一名独立的医生科学家。我的职业目标是成为一名独立的重症监护结果研究人员，专注于开发临床恶化的预测模型，以改善患者的预后。为了实现这个长期目标，我有三个短期目标：（1）获得开发和实施临床预测模型的专业知识，（2）创建一个IHCA预测模型，该模型将识别病房中的高风险患者，以触发挽救生命的干预措施，以及（3）获得模拟建模的专业知识，以研究开发的预测模型的影响。为了实现这些目标，我将建立在我获得公共卫生硕士学位时以及在卫生研究系博士课程的初始培训期间开发的基础上。虽然我的培训迄今为止为我提供了一个强大的背景，流行病学和生物统计学，在生物统计学进一步的高级培训是至关重要的，我的发展成为一个成功的独立研究人员。教学课程，研讨会，研究活动和会议参与的综合计划将跨越奖项的持续时间。通过完成我的三个短期目标，我将培养独特的技能，使我成为一名成功的独立研究人员。具体来说，我将获得的预测模型开发，实施和模拟建模的专业知识不仅可以应用于IHCA研究，也可以应用于重症监护医学的其他领域。此外，这些目标的完成将产生一个经过验证的IHCA预测模型，我将在未来的实施和成本效益研究中进行研究，并将作为未来R01级赠款提交的基础。