Individualized Prediction of Treatment Effects Using Data from Both Embedded Clinical Trials and Electronic Health Records

使用嵌入式临床试验和电子健康记录的数据个性化预测治疗效果

• 批准号: 10705264
• 负责人: GREGORY F. COOPER
• 金额: $ 60.31万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705264
• 关键词: Acceleration; Acute Respiratory Distress Syndrome; Acute respiratory failure; Award; Bayesian Method; Big Data; COVID-19 patient; Caring; Clinical Trials; Companions; Conduct Clinical Trials; Critical Illness; Data; Data Science; Data Set; Deterioration; Electronic Health Record; Enrollment; Event; Funding; Generations; Guidelines; Health system; Hospital Mortality; Individual; Intervention; Learning; Lung; Measures; Mechanical ventilation; Methods; Modeling; Monoclonal Antibody Therapy; National Heart, Lung, and Blood Institute; Operative Surgical Procedures; Outcome; Patients; Perioperative; Population; Postoperative Period; Prediction of Response to Therapy; Prone Position; Public Health; Randomized; Research Priority; Respiratory Failure; SARS-CoV-2 infection; Selection for Treatments; Statistical Methods; Strategic vision; Training; Translational Research; Treatment outcome; United States; United States National Institutes of Health; Vision; clinical care; clinical trial enrollment; cost; design; electronic health record system; high risk; high risk population; improved; improved outcome; in silico; individualized medicine; innovation; mortality; novel; personalized medicine; personalized predictions; prevent; randomized, clinical trials; response; treatment as usual; treatment effect; treatment response; ventilation; working group

Abstract More than 790,000 patients undergo mechanical ventilation for acute respiratory failure (ARF) in the United States each year at a cost of $27 billion. The in-hospital mortality for these patients is nearly 35%, and for patients with critical illness, such as acute respiratory distress syndrome (ARDS), mortality can approach 50%. In some patients, guideline-appropriate care with lung-protective ventilation or prone positioning will save lives, yet in many others, an individualized treatment is elusive. There is a need for advances in leveraging opportunities in data science to improve outcomes from respiratory failure. The primary method for generating new evidence is the randomized clinical trial (RCT). Yet they are often costly, take many years, and can be slow to accelerate learning and implementation at the bedside. In addition, RCTs usually enroll a moderate number of patients at high cost (100 to 1000s) and measure a limited range of covariates (10 to 100s). Thus, they do not lead to prediction of highly individualized treatment effects, as called for by the NHLBI Working Group on Research Priorities. In contrast, real-world evidence from electronic health records (EHRs) includes many patients (often millions) and covariates (often 1000s). They are inherently generalizable, less costly, and less timely to acquire than conducting RCTs. However, the estimation of treatment effects from EHR data is often biased due to confounding, which occurs when a treatment and its effect(s) are both causally influenced by one or more events. This project uses two Specific Aims to solve these challenges. Aim 1 proposes to develop and evaluate a new method for making individualized predictions of treatment effects using data from RCTs and EHRs. It uses “embedded” RCTs in which the clinical trial occurs within the context of usual care of a health system. The embedded RCT data are applied to control for confounding when using EHR data to predict treatment effects. Aim 2 will apply these methods to two embedded RCTs at UPMC that are studying treatments that may help prevent ARF. The OPTIMISE C-19 trial is studying monoclonal antibody therapy for non-hospitalized patients with SARS-CoV-2 infection. The PeriOp trial will be studying perioperative interventions to improve post-operative outcomes after major surgery. The hypothesis to be investigated is that the proposed new methods will predict the effects of treatment on acute respiratory failure and other outcomes more accurately than will using the clinical trial or the EHR data alone. Such results would provide support that these methods yield individualized predictions of treatment effects that can inform clinical care to help prevent ARF.

抽象的 联合的急性呼吸衰竭（ARF）的机械通气超过790,000例 各州每年的成本为270亿美元。这些患者的住院死亡率几乎为35％，对 患有急性呼吸窘迫综合征（ARDS）等重症患者，死亡率可能接近50％。 在某些患者中，肺部保护通风或容易发生定位的适当护理将挽救生命， 然而，在许多其他方面，个性化的治疗是难以捉摸的。需要利用的进步 数据科学的机会可以改善呼吸衰竭的结果。生成的主要方法 新证据是随机临床试验（RCT）。但是它们通常是昂贵的，需要很多年，可以 在床边加速学习和实施速度慢。此外，RCT通常会招募中等 以高成本（100至1000秒）的患者数量，并测量有限的协变量（10至100s）。那， 正如NHLBI工作所要求的那样，它们不会导致高度个性化的治疗效果 研究重点小组。 相反，来自电子健康记录（EHR）的现实世界证据包括许多患者（通常数百万） 和协变量（通常为1000秒）。它们固有地是可推广的，成本较低，及时获得的及时而不是 进行RCT。但是，由于EHR数据的治疗效果的估计通常是由于 混淆，当治疗及其效果都会意外影响一个或多个时发生时发生 事件。该项目使用两个特定的目标来解决这些挑战。目标1提案以开发和评估 一种使用RCT和EHR的数据对治疗效果进行个性化预测的新方法。 使用“嵌入式” RCT，其中临床试验在卫生系统通常护理的背景下进行。 嵌入的RCT数据用于控制使用EHR数据预测治疗时混淆的控制 效果。 AIM 2将将这些方法应用于UPMC的两个嵌入式RCT，这些RCT正在研究治疗 可能有助于防止ARF。优化的C-19试验正在研究非医院的单克隆抗体治疗 SARS-COV-2感染患者。剧中试验将研究定期干预措施以改进 大手术后的术后结局。要研究的假设是拟议的新 方法将更准确地预测治疗对急性呼吸衰竭和其他结果的影响 而不是仅使用临床试验或仅使用EHR数据。这样的结果将提供这些方法的支持 产生对治疗效应的个性化预测，可以为临床护理提供帮助以防止ARF。