■继发性高血压的准确识别和诊断至关重要,尤其是心血管心脏病仍然是导致死亡的主要原因。利用电子病历开发继发性高血压大数据智能平台,为未来的基础和临床研究做出贡献。
■使用医院数据,平台,在乌鲁木齐(UHDATA)命名为高血压DATAbase,纳入新疆维吾尔自治区人民医院自2004年12月以来诊断为高血压的患者。电子数据采集系统,数据库同步技术,和数据仓库技术(提取-转换-加载,ETL)为科研大数据平台,对医院各业务系统的数据进行同步提取。为平台建立了标准数据元素,包括人口统计和医学信息。为了便于研究,该数据库还链接到示例数据库系统,其中包括血液样本,尿液标本,和组织标本。
■从2004年12月17日至2022年8月31日,该平台总共增加了295,297名高血压患者,男性占53.76%,平均年龄59岁,14%患有继发性高血压。然而,75,802名患者访问了我们医院的高血压中心,43%(32,595例)被成功诊断为继发性高血压。该数据库包含1458个元素,平均填充率为90%。数据库可以连续包含新高血压患者的数据,并为现有高血压患者添加新数据,包括出院后的随访信息,数据库每2周更新一次。目前,一些基于该平台的研究已经发表。
■利用计算机信息技术,我们开发并实施了一个动态更新高血压患者电子病历的大型数据库,这有助于促进未来对继发性高血压的研究。
UNASSIGNED: The accurate identification and diagnosis of secondary hypertension is critical,especially while cardiovascular heart disease continues to be the leading cause of death. To develop a big data intelligence platform for secondary hypertension using electronic medical records to contribute to future basic and clinical research.
UNASSIGNED: Using hospital data, the platform, named Hypertension DATAbase at Urumchi (UHDATA), included patients diagnosed with hypertension at the People\'s Hospital of Xinjiang Uygur Autonomous Region since December 2004. The electronic data acquisition system, the database synchronization technology, and data warehouse technology (extract-transform-load, ETL) for the scientific research big data platform were used to synchronize and extract the data from each business system in the hospital. Standard data elements were established for the platform, including demographic and medical information. To facilitate the research, the database was also linked to the sample database system, which includes blood samples, urine specimens, and tissue specimens.
UNASSIGNED: From December 17, 2004, to August 31, 2022, a total of 295,297 hypertensive patients were added to the platform, with 53.76% being males, with a mean age of 59 years, and 14% with secondary hypertension. However, 75,802 patients visited the Hypertension Center at our hospital, with 43% (32,595 patients) being successfully diagnosed with secondary hypertension. The database contains 1458 elements, with an average fill rate of 90%. The database can continuously include the data for new hypertensive patients and add new data for existing hypertensive patients, including post-discharge follow-up information, and the database updates every 2 weeks. Presently, some studies that are based on the platform have been published.
UNASSIGNED: Using computer information technology, we developed and implemented a big database of dynamically updating electronic medical records for patients with hypertension, which is helpful in promoting future research on secondary hypertension.