Improving the Diagnosis of Liver Disease in Primary Care Patients with Abnormal Liver Function

改善肝功能异常的初级保健患者的肝病诊断

• 批准号: 10163178
• 负责人: Andrew David Schreiner
• 金额: $ 17.25万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-17 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10163178
• 关键词: Acute Liver Failure; Address; Age; Alcohol consumption; Alcoholic Liver Diseases; Alcohols; Americas; Antiviral Therapy; Biometry; Cardiovascular Diseases; Caring; Cause of Death; Chronic; Cirrhosis; Clinic; Clinical; Clinical Management; Communities; Data; Death Rate; Diagnosis; Diagnostic; Diagnostic Errors; Disease; Early Diagnosis; Economic Policy; Electronic Health Record; Europe; Funding; Goals; Health Policy; Health Services Research; Healthcare; Heart Diseases; Hepatitis C Therapy; Hepatology; Herbal supplement; Hyperglycemia; Hypertension; Individual; Infection; Information Technology; Institute of Medicine (U.S.); Intervention; Laboratories; Laboratory Study; Lead; Link; Liver; Liver Failure; Liver Function Tests; Liver diseases; Malignant Neoplasms; Malignant neoplasm of liver; Medicine; Mentorship; Modeling; Obesity; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Pharmaceutical Preparations; Pharmacology; Play; Portal Hypertension; Positioning Attribute; Predictive Analytics; Primary Care Physician; Primary Health Care; Process; Recording of previous events; Research; Resources; Risk; Risk Estimate; Role; Testing; Time; Training; United States; Viral hepatitis; Work; accurate diagnosis; base; career; clinical risk; clinically significant; effectiveness evaluation; experience; health care delivery; health economics; improved; intravenous drug use; liver function; liver injury; mortality; non-alcoholic fatty liver disease; novel strategies; predictive modeling; primary care setting; psychosocial; support tools

Abstract Improving the Diagnosis of Liver Disease in Primary Care Patients with Abnormal Liver Function Tests Through Predictive Modeling Reducing diagnostic error has been identified by the Institute of Medicine as a top national priority. Diagnostic errors pervade all of healthcare, with the average individual experiencing one major error during their lifetime. Therefore, improving the diagnostic process and reducing diagnostic error is not only highly appropriate for all patients, but will play a crucial role in optimizing the quality and value of healthcare delivery in the United States.1 Liver disease, with complications including acute liver failure, cirrhosis, and liver cancer ranks as a leading cause of death in America and over recent years has had a significant climb in age-adjusted mortality, while death rates from heart disease and cancer have fallen.2 Despite the increasing preventability of liver-related conditions through early recognition and treatment, the toll of chronic and end stage liver disease continues to rise.3 The traditional diagnostic process, a synthesis of information gathered from history, physical exam, and laboratory testing, performs poorly in the detection of early liver disease.4,5 Instead, clinicians rely more heavily on laboratory studies, and liver function tests (LFTs) in particular.6 Abnormal LFTs are among the most frequently encountered findings in medicine.7,8 Currently, primary care clinicians currently lack the ability to consistently identify liver-related disease from these abnormalities.9-12 Preliminary data in primary care emphasize the immense scope of the problem; in studies from Europe, LFTs have been found elsewhere to be abnormal in nearly 1 in 5 people.13,14 In our preliminary studies, we have up to 40% of patients seen in an academic primary care clinic possessed at least one abnormal LFT. Further, these abnormal liver tests are inappropriately or inadequately followed-up. These data and our own experience indicate that primary care physicians (PCPs) lack the resources to reliably identify and accurately diagnose liver-related diseases amongst these many abnormal LFTs. In this proposal, the candidate and his mentorship team seek to harness inter-professional teamwork and information technology to reduce diagnostic error. They will identify clinical and demographic variables of patients with abnormal LFTs associated with specific liver-related diagnoses in primary care (Aim 1). Additionally, they will develop and validate a predictive model to identify patients with abnormal LFTs at risk for liver-related diagnoses (Aim 2). Lastly, they will create a decision support tool application to aid PCPs confronted with abnormal LFTs to promptly and accurately diagnose liver disease (Aim 3).

抽象的 改善肝功能检查异常的初级保健患者的肝病诊断 通过预测建模 减少诊断错误已被医学研究所确定为国家的首要任务。诊断 错误遍及所有医疗保健领域，平均每个人在一生中都会经历一次重大错误。 因此，改进诊断流程、减少诊断错误不仅非常适合所有人 患者，但将在优化美国医疗保健服务的质量和价值方面发挥至关重要的作用 状态.1 肝脏疾病及其并发症，包括急性肝功能衰竭、肝硬化和肝癌，被列为主要疾病 近年来，美国的死因年龄调整死亡率显着上升，而 心脏病和癌症的死亡率有所下降。2 尽管肝脏相关疾病的可预防性不断提高 通过早期识别和治疗，慢性和终末期肝病的死亡人数继续上升 崛起.3 传统的诊断过程是综合从病史、体格检查和诊断中收集的信息 实验室检测在早期肝病检测方面表现不佳。4,5 相反，临床医生更加依赖 实验室研究，尤其是肝功能测试 (LFT)。6 LFT 异常是最常见的 医学上经常遇到的发现。7,8 目前，初级保健临床医生缺乏能力 从这些异常中持续识别肝脏相关疾病。9-12 初级保健的初步数据强调了该问题的广泛性；在欧洲的研究中，LFT 在其他地方发现近五分之一的人存在异常。13,14 在我们的初步研究中，我们发现 在学术初级保健诊所就诊的患者中，40% 的患者至少有一项 LFT 异常。更远， 这些异常的肝脏检查没有得到适当或不充分的随访。这些数据和我们自己的 经验表明，初级保健医生 (PCP) 缺乏资源来可靠地识别和准确地识别 在众多异常 LFT 中诊断肝脏相关疾病。 在此提案中，候选人及其导师团队寻求利用跨专业团队合作和 信息技术减少诊断错误。他们将识别临床和人口统计学变量 初级保健中 LFT 异常与特定肝脏相关诊断相关的患者（目标 1）。 此外，他们将开发并验证一个预测模型，以识别 LFT 异常且有以下风险的患者： 肝脏相关诊断（目标 2）。最后，他们将创建一个决策支持工具应用程序来帮助 PCP 面对 LFT 异常，及时准确诊断肝病（目标 3）。