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-患者从健康状态进展到糖尿病或高血压并发症的疾病的顺序和时间-可以代表不同的疾病机制，这些知识在优化护理和理解糖尿病和高血压的病因方面非常有用。大多数研究机构可获得的EHR数据的患者覆盖范围和随访时间不允许从疾病发作到并发症观察患者。我们的目标是从两个数据集的独特组合中重建进展模式：明尼苏达大学临床数据库的2，000，000例患者，随访时间相对较短，马约诊所非常干净和完整的罗切斯特流行病学项目（REP）数据集，涵盖100，000例患者，随访时间较长。首先，我们提取个体患者的轨迹。从这些轨迹，我们提取所有的进展对，两个直接或间接的后续条件的序列。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。我们将这些配对表示为典型进展和例外目录（在相同的两种情况之间的非典型配对，其在历史、药物或其他细节上不同 并有显著不同的结果）。最后，使用马约诊所的数据及其较长的随访时间作为脚手架，我们从进展对中重建了进展模式。

 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.