Levin Matthew A, Joseph Thomas T, Jeff Janina M, Nadukuru Rajiv, Ellis Stephen B, Bottinger Erwin P, Kenny Eimear E
Department of Anesthesiology, Division of Cardiothoracic Anesthesia, Icahn School of Medicine at Mount Sinai, New York, USA; Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA.
J Biomed Inform. 2017 Mar;67:80-89. doi: 10.1016/j.jbi.2017.02.005. Epub 2017 Feb 11.
Design and implement a HIPAA and Integrating the Healthcare Enterprise (IHE) profile compliant automated pipeline, the integrated Genomics Anesthesia System (iGAS), linking genomic data from the Mount Sinai Health System (MSHS) BioMe biobank to electronic anesthesia records, including physiological data collected during the perioperative period. The resulting repository of multi-dimensional data can be used for precision medicine analysis of physiological readouts, acute medical conditions, and adverse events that can occur during surgery.
A structured pipeline was developed atop our existing anesthesia data warehouse using open-source tools. The pipeline is automated using scheduled tasks. The pipeline runs weekly, and finds and identifies all new and existing anesthetic records for BioMe participants.
The pipeline went live in June 2015 with 49.2% (n=15,673) of BioMe participants linked to 40,947 anesthetics. The pipeline runs weekly in minimal time. After eighteen months, an additional 3671 participants were enrolled in BioMe and the number of matched anesthetic records grew 21% to 49,545. Overall percentage of BioMe patients with anesthetics remained similar at 51.1% (n=18,128). Seven patients opted out during this time. The median number of anesthetics per participant was 2 (range 1-144). Collectively, there were over 35 million physiologic data points and 480,000 medication administrations linked to genomic data. To date, two projects are using the pipeline at MSHS.
Automated integration of biobank and anesthetic data sources is feasible and practical. This integration enables large-scale genomic analyses that might inform variable physiological response to anesthetic and surgical stress, and examine genetic factors underlying adverse outcomes during and after surgery.
设计并实施一个符合健康保险流通与责任法案（HIPAA）及整合医疗企业（IHE）规范的自动化流程，即整合基因组麻醉系统（iGAS），将西奈山医疗系统（MSHS）生物样本库的基因组数据与电子麻醉记录相连接，包括围手术期收集的生理数据。由此产生的多维数据存储库可用于对生理读数、急性病症以及手术期间可能发生的不良事件进行精准医学分析。
利用开源工具在我们现有的麻醉数据仓库之上开发了一个结构化流程。该流程通过定时任务实现自动化。该流程每周运行一次，查找并识别生物样本库参与者的所有新的和现有的麻醉记录。
该流程于2015年6月上线，49.2%（n = 15,673）的生物样本库参与者与40,947例麻醉记录相连接。该流程每周运行时间最短。18个月后，又有3671名参与者加入生物样本库，匹配的麻醉记录数量增长了21%，达到49,545条。有麻醉记录的生物样本库患者的总体比例保持在51.1%（n = 18,128）左右。在此期间有7名患者选择退出。每位参与者的麻醉记录中位数为2（范围为1 - 144）。总体而言，有超过3500万个生理数据点以及48万次给药与基因组数据相关联。到目前为止，MSHS有两个项目正在使用该流程。
生物样本库和麻醉数据源的自动整合是可行且实用的。这种整合能够进行大规模基因组分析，从而为麻醉和手术应激的可变生理反应提供信息，并研究手术期间及术后不良结局的潜在遗传因素。
