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

摘要 美国超过79万名急性呼吸衰竭(ARF)患者接受机械通气 每年耗资270亿美元。这些患者的住院死亡率接近35%， 重症患者，如急性呼吸窘迫综合征(ARDS)，死亡率可接近50%。 在一些患者中，采用肺保护性通风或俯卧位的指南适当的护理将挽救生命， 然而，在许多其他国家，个体化治疗是难以捉摸的。需要在杠杆化方面取得进展 数据科学中的机会，以改善呼吸衰竭的结果。生成的主要方法是 新的证据是随机临床试验(RCT)。然而，它们往往代价高昂，耗时多年，而且可能 在床边加快学习和实施的速度很慢。此外，RCT通常招收一名中等水平的 高成本的患者数量(100到1000秒)，并测量有限范围的协变量(10到100秒)。因此， 它们不会像NHLBI工作所要求的那样，导致高度个性化的治疗效果的预测 研究优先事项小组。 相比之下，来自电子健康记录(EHR)的真实证据包括许多患者(通常是数百万人) 和协变量(通常是1000)。它们与生俱来的通用性、更低的成本和更不及时的收购 进行随机对照试验。然而，从EHR数据对治疗效果的估计往往是有偏差的，因为 混淆，当一种治疗方法及其效果都受到一种或多种因素的因果影响时就会发生这种情况(S) 事件。该项目使用两个具体目标来解决这些挑战。目标1建议开发和评估 一种使用随机对照试验和电子病历数据对治疗效果进行个体化预测的新方法。它 使用“嵌入式”随机对照试验，其中临床试验在卫生系统的日常护理环境中进行。 当使用EHR数据预测治疗时，嵌入的RCT数据被应用于混杂控制 效果。Aim 2将把这些方法应用于UPMC的两个嵌入式RCT，这两个RCT正在研究 可能有助于预防ARF。优化型C-19试验正在研究非住院患者的单抗疗法 SARS-CoV-2感染患者。Periop试验将研究围手术期干预措施，以改善 大手术后的预后。要调查的假设是，拟议的新的 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。 而不是仅使用临床试验或EHR数据。这样的结果将支持这些方法 产生个体化的治疗效果预测，为临床护理提供信息，帮助预防ARF。

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.