PA-20-070 "Development of evidence-based decision support for the management of COVID19"

摘要 新冠肺炎优化管理的一个关键是制定循证建议和 确保落实治疗建议的相关战略。这一点尤为重要 当证据涌现得像新冠肺炎一样快的时候。职系(建议的评级 评估、发展和评价)已成为对产品质量进行评级的主导系统 建议的证据和力度。由110多个专业组织认可，等级 已经编纂了临床实践指南(CPGs)小组应该考虑的关键规范性因素 对价。在过去4年我们的母公司R01资助(5R01HS024917)开展的活动中，我们 我发现，除了规范的年级因素外，重要的非年级因素也会影响群体 中央人民政府各小组的判决。我们现在建议利用来自家长R01赠款的这些发现来帮助 为新冠肺炎生成最优的管理策略。提供最合理的框架 为了管理新冠肺炎患者，我们建议制定基于年级的CPG，我们将在 芝加哥Rush医疗系统内的护理点的电子病历(EMR)。一个日益增长的 改善患者护理的流行策略是通过将CPG转换为临床路径来标准化护理 (CPS)，通常使用流程图或临床算法来提供有关一个疗程的详细步骤 针对特定临床问题或整个护理范围的管理。然而，尽管承诺 CPS及其越来越多的使用，目前还没有制定出指导其发展的理论框架。这 这意味着不可能严格分析CPGs/CPS的效率，也不可能分析它们对患者健康的影响 结果。我们假设，发展CPGs/CPS的坚实理论基础可以通过 将它们转换为快速节俭的决策树(FFT)。FFT被构造为一系列顺序- 有序的临床信息或“线索”，其关系由一系列IF-THEN语句定义。每一条线索都在 快速傅立叶变换可以正确或错误地将信号(例如，患者患有新冠肺炎)与噪声(例如，患者确实存在)进行分类 没有新冠肺炎)并且该分类模式可以被测量(例如，该信号是真的正信号还是 否定)。FFT的这一特性使它们能够集成到更广泛的信号理论框架中 检测和相关理论，进而允许它们所代表的临床策略的准确性 已评估。在这个应用中，我们建议为新冠肺炎开发等级的CPG(目标1)，翻译这些CPG 转换为CP，并将CP转换为FFT。随后，我们将在Rush EMR(目标2)中实施FFT， 并进行中断的时间序列，以评估基于职级的FFT对管理的影响 患有新冠肺炎的患者。建议的申请直接由家长R01拨款通知，并具有 潜在的直接和持续的影响，以改善新冠肺炎患者的临床管理。

SUMMARY A key to optimal management of COVID-19 is development of evidence-based recommendations and associated strategies to ensure implementation of treatment recommendations. This is particularly important when evidence is emerging as rapidly as is the case for COVID-19. GRADE (Grading of Recommendations Assessment, Development and Evaluation) has emerged as the leading system for rating the quality of evidence and strength of recommendations. Endorsed by more than 110 professional organizations, GRADE has codified key normative factors that clinical practice guidelines (CPGs) panels ought to take into consideration. During activities conducted over the last 4 years on our parent R01 grant (5R01HS024917), we have discovered that in addition to normative GRADE factors, important non-GRADE factors affect the group judgment of CPG panels. We now propose to leverage these findings from the parent R01 grant to help generate optimal management strategies for COVID-19. To provide the most rational framework for managing COVID-19 patients, we propose to develop GRADE-based CPGs that we will implement in the electronic medical record (EMR) at the point-of-care within the Rush health system in Chicago. An increasingly popular strategy for improving patient care is to standardize care by translating CPGs into clinical pathways (CPs), which typically use flow charts or clinical algorithms to provide detailed steps about a course of management for a particular clinical problem or an entire spectrum of care. However, despite the promise of CPs and their increasing use, no theoretical framework has been developed to guide their development. This means it is not possible to rigorously analyze the efficiency of CPGs/CPs, nor their influence on patient health outcomes. We hypothesize that solid theoretical grounds for developing CPGs/CPs can be provided by converting them into fast-and-frugal decision trees (FFTs). FFTs are constructed as a series of sequentially- ordered, clinical information or “cues” whose relation is defined by a series of if–then statements. Every cue in an FFT can correctly or incorrectly classify a signal (e.g., patient has COVID-19) vs. noise (e.g., patient does not have COVID-19) and this classification pattern can be measured (e.g., is the signal a true positive or negative). This property of FFTs allows them to be integrated within a broader theoretical framework of signal detection and related theories which, in turn, allows the accuracy of the clinical strategies they represent to be evaluated. In this application, we propose to develop GRADE CPGs for COVID-19 (Aim 1), translate the CPGs into CPs, and, convert the CPs into FFTs. Subsequently, we will implement FFTs in the Rush EMR (Aim 2), and conduct an interrupted time series to evaluate the effect of GRADE-based FFTs on management of patients with COVID-19. The proposed application is directly informed by the parent R01 grant and has potential for immediate and sustained impact to improve clinical management of patients with COVID-19.