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）的患病率已经增加，并将在未来30年内继续增加，给个人和社会带来巨大负担。HF的早期检测可能有助于减轻这种负担。该提案的目的是开发利用纵向电子健康记录（EHR）的鲁棒预测模型。我们的长期目标是使用这样的模型在早期阶段检测HF（例如，AHA/ACA阶段A或B）比通常发生在初级保健。我们已经完成了广泛的初步工作，使用10年的纵向EHR数据的初级保健患者。通过使用文本挖掘和机器学习工具，我们发现，在进行更具体的诊断研究之前，EHR中就已经记录了Fragrance标准。这些症状在诊断前2 - 4年发生的HF病例中比对照组更为常见。此外，临床，实验室，诊断，和其他数据常规捕获的EHR预测未来的HF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）制定更敏感和特异性的标准，用于在HF的早期检测中使用Frachial HF体征和症状。我们已经证明，在诊断前1-4年，对心力衰竭体征和症状的阳性和阴性确认是有用的。我们建议解决以下问题：a）哪些症状和体征及其组合对早期检测最有用？B）在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）HF亚型的标准有何不同？我们假设，分析常规记录的体征和症状数据将产生一个有临床意义的改善，在实际诊断前1至2年检测HF的准确性; 2）确定差异性改善预测准确性HF结合常见的固定字段EHR数据与文本数据，以提高HF的早期检测。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、护理使用等）可用于预测未来的HF诊断。基于这些发现，我们认识到需要进行越来越复杂的分析，以确定如何使用这些数据来优化预测能力。我们假设特定模型和这些模型的性能将因HF的HF亚型而异; 3）确定如何单独使用数字ECG相关测量以及与其他数据结合使用，以改善HF的早期检测。对数字ECG数据的真实的实时访问提供了独特的机会来提取在初级护理中可能在HF的早期检测中有用的多种测量;以及4）开发用于初级护理中HF的早期检测的初步操作协议。我们需要考虑如何使用模型的输出来支持临床指导和共享决策。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在临床实践中采用EHR和有意义地使用这种技术的国家优先事项有关。