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: 10502411
• 负责人: GREGORY F. COOPER
• 金额: $ 61.32万
• 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/10502411
• 关键词: Acute Respiratory Distress Syndrome; Acute respiratory failure; Award; Bayesian Method; Big Data; COVID-19 patient; Caring; Clinical Trials; Companions; Conduct Clinical Trials; Cost Measures; Critical Illness; Data; Data Science; Data Set; Deterioration; Electronic Health Record; Enrollment; Equilibrium; 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; Randomized Clinical Trials; Research Priority; Respiratory Failure; SARS-CoV-2 infection; Selection for Treatments; Statistical Methods; Strategic vision; System; Time; Training; Translational Research; Treatment outcome; United States; United States National Institutes of Health; Vision; base; clinical care; cost; design; high risk; improved; improved outcome; in silico; individualized medicine; innovation; mortality; novel; personalized medicine; personalized predictions; prevent; 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.

摘要 在美国，超过790，000名患者因急性呼吸衰竭（ARF）接受机械通气 每年花费270亿美元。这些患者的住院死亡率接近35%， 危重病患者，如急性呼吸窘迫综合征（ARDS），死亡率可接近50%。 在一些患者中，按照指南进行肺保护性通气或俯卧位护理可以挽救生命， 然而，在许多其他国家，个性化治疗是难以捉摸的。需要在杠杆化方面取得进展 数据科学的机会，以改善呼吸衰竭的结果。生成的主要方法 新的证据是随机临床试验（RCT）。然而，它们往往成本高昂，需要多年时间，而且可能会 在床边加快学习和实施的速度。此外，随机对照试验通常招募中度 高成本的患者数量（100到1000），并测量有限范围的协变量（10到100）。因此，在本发明中， 它们不会像NHLBI工作组所呼吁的那样预测高度个性化的治疗效果 研究优先事项小组。 相比之下，来自电子健康记录（EHR）的真实世界证据包括许多患者（通常为数百万） 和协变量（通常为1000）。它们本质上是可推广的，成本较低，获得的时间较短， 进行RCT。然而，EHR数据的治疗效果估计往往是有偏差的， 混杂，当治疗及其效应均受到一种或多种因素的因果影响时发生 事件该项目使用两个特定目标来解决这些挑战。目标1：开发和评估 一种新的方法，使用来自RCT和EHR的数据对治疗效果进行个性化预测。它 使用“嵌入式”随机对照试验，其中临床试验发生在卫生系统的常规护理背景下。 当使用EHR数据预测治疗时，嵌入的RCT数据用于控制混杂因素 方面的影响.目标2将这些方法应用于UPMC的两个嵌入式RCT，这些RCT正在研究治疗方法， 可能有助于预防ARF。OPTIMISE C-19试验正在研究非住院患者的单克隆抗体治疗 SARS-CoV-2感染者。PeriOp试验将研究围手术期干预，以改善 大手术后的术后结果。要研究的假设是， 这些方法将更准确地预测急性呼吸衰竭的治疗效果和其他结果 比单独使用临床试验或EHR数据更有效。这些结果将支持这些方法 产生治疗效果的个体化预测，可以为临床护理提供信息，以帮助预防ARF。