iGAS: A framework for using electronic intraoperative medical records for genomic discovery

Matthew A Levin; Thomas T Joseph; Janina M Jeff; Rajiv Nadukuru; Stephen B Ellis; Erwin P Bottinger; Eimear E Kenny

doi:10.1016/j.jbi.2017.02.005

iGAS: A framework for using electronic intraoperative medical records for genomic discovery

J Biomed Inform. 2017 Mar:67:80-89. doi: 10.1016/j.jbi.2017.02.005. Epub 2017 Feb 11.

Authors

Matthew A Levin¹, Thomas T Joseph², Janina M Jeff³, Rajiv Nadukuru³, Stephen B Ellis³, Erwin P Bottinger³, Eimear E Kenny⁴

Affiliations

¹ 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. Electronic address: Mathew.Levin@mssm.edu.
² Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA.
³ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
⁴ 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; The Icahn Institute of Multiscale Biology and Genomics, Icahn School of Medicine at Mount Sinai, New York, USA; The Center for Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, USA. Electronic address: Eimear.Kenny@mssm.edu.

PMID: 28193464
DOI: 10.1016/j.jbi.2017.02.005

Abstract

Objective: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

MeSH terms

Anesthesia, General / adverse effects*
Data Collection
Databases, Factual
Delivery of Health Care
Electronic Health Records*
Genome
Genomics / trends*
Humans
Information Storage and Retrieval
Surgical Procedures, Operative / adverse effects*