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Extracting Typical and Atypical Disease Progression Patterns from Multi-Site EHR

从多站点 EHR 中提取典型和非典型疾病进展模式

基本信息

批准号：
8884195
负责人：
GYORGY SIMON
金额：
$ 32.52万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-07-15 至 2016-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8884195
关键词：
Accounting Affect Blood Vessels Caring Cataloging Catalogs Centers for Disease Control and Prevention (U.S.)Chronic Chronic Disease Clinic Clinical Data Clinical Decision Support Systems Complications of Diabetes Mellitus Data Data Set Databases Development Diabetes Mellitus Disease Disease Progression Epidemiology Etiology Healthcare Heart Diseases Human Hyperlipidemia Hypertension Individual Institutes Kidney Diseases Knowledge Left Light Link Maps Medical History Medical History Taking Metabolic syndrome Methodology Minnesota Neuropathy Non-Insulin-Dependent Diabetes Mellitus Obesity Onset of illness Outcome Outpatients Patient Care Patients Pattern Pharmaceutical Preparations Population Prevalence Prevention Recording of previous events Reporting Retinal Diseases Risk Risk Estimate Risk Factors Secure Site Stroke Time Universities Validation Vascular Diseases clinical practice cohort follow-up interest scaffold

项目摘要

DESCRIPTION (provided by applicant): Typical progression patterns-sequences and timing of the conditions that patients progress through from a healthy state to a complication of diabetes or hypertension- can represent distinct disease mechanisms, knowledge that would be tremendously useful in optimizing care and in understanding the etiology of diabetes and hypertension. Patient coverage and follow-up times of EHR data available to most institutes do not allow for observing patients from the onset of the disease to the complications. We aim to reconstruct the progression patterns from a unique combinations of two data sets: the University of Minnesota clinical data repository of 2,000,000 patients with relatively short follow-up times and the Mayo Clinic's exceptionally clean and complete Rochester Epidemiology Project (REP) data set covering 100,000 patients with long follow-up. First, we extract the individual patients' trajectories. From these trajectories, we extract all progression pairs, sequences of two directly or indirectly subsequent conditions. We also estimate and the risks of complications this progression confers upon the patient, as well as the progression time distribution between the pair of conditions. We represent these pairs as a typical progression and a catalog of exceptions (atypical pairs between the same two conditions that differ in history, medication or other details and have significantly different outcomes). Finally, using the Mayo Clinic data with its long follow- up times as scaffolding, we reconstruct the progression patterns from the progression pairs.

描述（由申请人提供）：典型的进展模式——患者从健康状态进展到糖尿病或高血压并发症的顺序和时间——可以代表不同的疾病机制，这些知识对于优化护理和理解糖尿病和高血压的病因学将非常有用。大多数机构可获得的 EHR 数据的患者覆盖范围和随访时间不允许观察患者从疾病发作到并发症的整个过程。我们的目标是通过两个数据集的独特组合来重建进展模式：明尼苏达大学的临床数据库包含 2,000,000 名患者的随访时间相对较短的数据集，以及 Mayo Clinic 极其干净且完整的罗切斯特流行病学项目 (REP) 数据集，涵盖 100,000 名患者且随访时间较长。首先，我们提取个体患者的轨迹。从这些轨迹中，我们提取所有的进展对，即两个直接或间接后续条件的序列。我们还估计了这种进展给患者带来的并发症的风险，以及两种情况之间的进展时间分布。我们将这些对表示为典型的进展和例外目录（在病史、药物或其他细节上不同的相同两种情况之间的非典型对并产生显着不同的结果）。最后，使用梅奥诊所的数据及其较长的随访时间作为脚手架，我们从进展对重建进展模式。