Early Detection of Heart Failure via the Electronic Health Record in Primary Care

描述（由申请人提供）：心力衰竭（HF）的患病率已经增加，并将在未来30年继续增加，给个人和社会带来深刻的负担。早期发现心衰可能有助于减轻这一负担。本提案的目的是开发稳健的预测模型，利用纵向电子健康记录（EHR）。我们的长期目标是使用这种模型在早期发现HF（例如，AHA/ACA A期或B期），而不是通常在初级保健中发现。我们已经完成了广泛的初步工作，使用了10年来初级保健患者的纵向电子病历数据。使用文本挖掘和机器学习工具，我们发现早在更具体的诊断研究完成之前，Framingham标准就被记录在电子病历中。这些症状在诊断前2 - 4年的心力衰竭病例中比对照组更为常见。此外，电子病历中常规获取的临床、实验室、诊断和其他数据可以预测未来的心衰诊断。我们建议将这项工作扩展到HF的早期检测，目的如下：1)制定更敏感和具体的标准，用于HF的早期检测中使用Framingham HF体征和症状。我们已经证明，在HF诊断前1-4年，Framingham体征和症状的阳性和阴性肯定是有用的。我们建议解决以下问题：a)哪些弗雷明汉体征和症状及其组合对早期发现最有用？b)体征和症状之间是否存在时间序列和相关性，以提高检测的准确性？c)不同HF亚型的诊断标准有何不同？我们假设，分析常规记录的体征和症状数据将在实际诊断前1至2年提高检测HF的准确性。2)确定常用固定现场EHR数据与文本数据相结合对心衰预测诊断准确率的差异性提高，提高心衰的早期发现。我们的初步工作表明，纵向电子病历数据（如临床、实验室、健康行为、诊断、护理使用等）对预测未来的心衰诊断是有用的。基于这些发现，我们认识到需要越来越复杂的分析来确定如何使用这些数据来优化预测能力。我们假设具体的模型和这些模型的性能会因HF亚型而异；3)确定数字ECG相关测量如何单独使用或与其他数据结合使用，以提高心衰的早期发现。实时访问数字心电图数据提供了独特的机会，可以提取多种措施，这些措施可能在早期发现心衰的初级保健中有用；4)制定初级保健中早期发现心衰的初步操作方案。我们需要考虑如何利用模型的输出来支持临床指导和共同决策。此外，需要为数据丰富和数据贫乏的环境开发模型。拟议工作的长期目标与国家在临床实践中采用电子病历和有意义地使用这种技术的优先事项有关。

DESCRIPTION (provided by applicant): Heart failure (HF) prevalence has increased and will continue so over the next 30 years with a profound individual and societal burden. Early detection of HF may be useful in mitigating this burden. The purpose of this proposal is to develop robust predictive models that make use of longitudinal electronic health record (EHR). Our long term goal is to use such models to detect HF at an earlier stage (e.g., AHA/ACA Stages A or B) than usually occurs in primary care. We have completed extensive preliminary work using 10 years of longitudinal EHR data on primary care patients. Using text mining and machine learning tools we have found that Framingham criteria are documented in the EHR long before more specific diagnostic studies are done. These symptoms are considerably more common among incident HF cases than controls two to four years before diagnosis. Moreover, clinical, laboratory, diagnostic, and other data routinely captured in the EHR predicts future HF diagnosis. We propose to extend this work on early detection of HF with the following aims: 1) To develop more sensitive and specific criteria for use of Framingham HF signs and symptoms in the early detection of HF. We have shown that positive and negative affirmation of Framingham signs and symptoms are useful in HF detection 1-4 years before diagnosis. We propose to address the following: a) Which Framingham signs and symptoms and combinations thereof are most useful for early detection? b) Are there temporal sequences and correlations among signs and symptoms that improve accuracy of detection? c) How do the criteria vary by HF subtype? We hypothesize that analysis of routinely documented signs and symptoms data will yield a clinically meaningful improvement in the accuracy of detecting HF 1 to 2 years before actual diagnosis; 2) To determine the differential improvement in accuracy of predicting diagnosis of HF by combining common fixed field EHR data with text data to improve early detection of HF. Our preliminary work indicates that longitudinal EHR data (e.g., clinical, laboratory, health behaviors, diagnoses, use of care, etc) are useful in predicting future HF diagnosis. Based on these findings, we recognize an increasingly sophisticated analysis will be required to identify how to use these data to optimize predictive power. We hypothesize that the specific models and the performance of these models will vary by HF subtypes of HF; 3) To determine how digital ECG related measures can be used alone and in combination with other data to improve early detection of HF. Real time access to digital ECG data affords unique opportunities to extract a diversity of measures that may be useful in primary care in the early detection of HF; and 4) To develop preliminary operational protocols for early detection of HF in primary care. We will need to consider how the output from the model can be used to support clinical guidance and shared decision-making. Moreover, models need to be developed for data rich and data poor settings. The long term goal of the proposed work is relevant to the national priority for adoption of EHRs in clinical practice and for meaningful use of such technology.