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

 描述(由申请人提供)：典型的进展模式--患者从健康状态发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，这些知识将对优化护理和了解糖尿病和高血压的病因非常有用。大多数研究所可获得的EHR数据的患者覆盖率和随访时间不允许观察患者从疾病开始到出现并发症。我们的目标是从两个数据集的独特组合中重建进展模式：明尼苏达大学2,000,000名随访时间相对较短的患者的临床数据库，以及梅奥诊所异常干净和完整的罗切斯特流行病学项目(REP)数据集，覆盖100,000名长期随访的患者。首先，我们提取个体患者的轨迹。从这些轨迹中，我们提取出所有的级数对，即两个直接或间接后续条件的序列。我们还估计了这一进展给患者带来的并发症的风险，以及这两种情况之间的进展时间分布。我们将这些配对表示为典型的进展和例外情况的目录(在病史、用药或其他细节上不同的相同两种情况之间的非典型配对 并产生显著不同的结果)。最后，我们以梅奥诊所的长期随访数据为支架，从进展对中重建进展模式。

 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.