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

通过初级保健中的电子健康记录及早发现心力衰竭

• 批准号: 8421618
• 负责人: WALTER F STEWART
• 金额: $ 55.7万
• 依托单位: CALIFORNIA PACIFIC MED CTR RES INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8421618
• 关键词: Accounting; Address; Admission activity; Adoption; Affect; Age; Caring; Cessation of life; Clinical; Complex; Costs and Benefits; Data; Detection; Diagnosis; Diagnostic; Direct Costs; Disease; Disease Progression; Documentation; Early Diagnosis; Electrocardiogram; Electronic Health Record; Employee Strikes; Failure; Future; Goals; Group Practice; Health; Health behavior; Heart failure; Hospitals; Individual; Intervention; Laboratories; Life Style; Machine Learning; Manuals; Measures; Medical; Medicare; Modeling; Morbidity - disease rate; Outcome; Output; Patient Monitoring; Patients; Pattern; Performance; Prevalence; Preventive; Primary Health Care; Process; Protocols documentation; Quality of life; Risk; Signal Transduction; Signs and Symptoms; Staging; Symptoms; System; Technology; Testing; Text; Time; Translating; Work; aging population; base; body system; case control; clinical practice; cost; cost effective; digital; improved; mortality; novel; predictive modeling; prevent; public health relevance; rapid growth; shared decision making; text searching; tool; trend

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.

描述（由申请人提供）：心力衰竭（HF）的患病率有所增加，并将在未来30年内继续持续下来，并承担着深远的个人和社会负担。早期发现HF可能有助于减轻这种负担。该提案的目的是开发使用纵向电子健康记录（EHR）的强大预测模型。我们的长期目标是使用此类模型在较早的阶段（例如，AHA/ACA阶段A或B）检测HF，而不是通常在初级保健中发生的。我们已经使用10年的初级保健患者的纵向EHR数据完成了广泛的初步工作。使用文本挖掘和机器学习工具，我们发现在更具体的诊断研究完成之前，在EHR中记录了Framingham标准。在诊断前两到四年，在事件HF病例中，这些症状在事件HF病例中比对照更为普遍。此外，EHR中常规捕获的临床，实验室，诊断和其他数据可预测未来的HF诊断。我们建议将这项工作扩展到早期检测到HF的目的：1）在早期检测到HF的早期检测中，开发更灵敏和具体的标准，用于使用Framingham HF征兆和症状。我们已经表明，弗雷明汉体征和症状的阳性和阴性肯定在诊断前1 - 4年有用。我们建议解决以下内容：a）哪些弗雷明汉体征和症状及其组合对于早期检测最有用？ b）在提高检测准确性的体征和症状之间是否存在时间序列和相关性？ c）标准如何因HF亚型而异？我们假设对常规记录的符号和症状数据的分析将在实际诊断前检测HF 1至2年的准确性上有意义地提高； 2）通过将共同的固定场EHR数据与文本数据相结合以改善HF的早期检测，以确定预测HF诊断的准确性的差异改善。我们的初步工作表明，纵向EHR数据（例如临床，实验室，健康行为，诊断，护理的使用等）对于预测未来的HF诊断很有用。基于这些发现，我们认识到将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。我们假设这些模型的特定模型和性能会因HF的HF亚型而异。 3）确定如何单独使用数字ECG相关的措施，并与其他数据结合使用以改善HF的早期检测。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，这些措施在早期发现HF时可能对初级保健有用； 4）制定初步操作方案，用于早期检测初级保健中的HF。我们将需要考虑如何使用该模型的产出来支持临床指导和共享决策。此外，需要开发模型，以使数据富含数据和数据较差的设置。拟议工作的长期目标与在临床实践中采用EHR和有意义地使用此类技术的国家优先事项有关。