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CUSTOM-SEQ: a prototype for oncology rapid learning in a comprehensive EHR environment.
- Source :
-
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2016 Jul; Vol. 23 (4), pp. 692-700. Date of Electronic Publication: 2016 Mar 23. - Publication Year :
- 2016
-
Abstract
- Background: As targeted cancer therapies and molecular profiling become widespread, the era of "precision oncology" is at hand. However, cancer genomes are complex, making mutation-specific outcomes difficult to track. We created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data.<br />Methods: Patients with cancer genotyping were included, and clinical data was extracted through a variety of algorithms. Results were refreshed regularly and injected into a standard reporting platform. Significant results were highlighted for visual cueing. A subset was additionally stratified by stage, smoking status, and treatment exposure.<br />Results: By August 2015, 4310 patients with a median follow-up of 17 months had sufficient data for survival calculation. As expected, epidermal growth factor receptor (EGFR) mutations in lung cancer were associated with superior overall survival, hazard ratio (HR) = 0.53 (P < .001), validating the approach. Guanine nucleotide binding protein (G protein), q polypeptide (GNAQ) mutations in melanoma were associated with inferior overall survival, a novel finding (HR = 3.42, P < .001). Smoking status was not prognostic for epidermal growth factor receptor-mutated lung cancer patients, who also lived significantly longer than their counterparts, even with advanced disease (HR = 0.54, P = .001).<br />Interpretation: CUSTOM-SEQ represents a novel rapid learning system for a precision oncology environment. Retrospective studies are often limited by study of specific time periods and can lead to incomplete conclusions. Because data is continuously updated in CUSTOM-SEQ, the evidence base is constantly growing. Future work will allow users to interactively explore populations by demographics and treatment exposure, in order to further investigate significant mutation-specific signals.<br /> (© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
- Subjects :
- Cohort Studies
Computational Biology
DNA, Neoplasm
Epidermal Growth Factor genetics
Follow-Up Studies
Genotype
Humans
Information Storage and Retrieval
Kaplan-Meier Estimate
Lung Neoplasms mortality
Neoplasms mortality
Precision Medicine
Proportional Hazards Models
Tobacco Smoking
Algorithms
Electronic Health Records
Lung Neoplasms genetics
Mutation
Neoplasms genetics
Subjects
Details
- Language :
- English
- ISSN :
- 1527-974X
- Volume :
- 23
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Journal of the American Medical Informatics Association : JAMIA
- Publication Type :
- Academic Journal
- Accession number :
- 27008846
- Full Text :
- https://doi.org/10.1093/jamia/ocw008