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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. 23:692-700
- Publication Year :
- 2016
- Publisher :
- Oxford University Press (OUP), 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.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.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 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.
- Subjects :
- 0301 basic medicine
Oncology
medicine.medical_specialty
Lung Neoplasms
Genotype
Information Storage and Retrieval
Health Informatics
Kaplan-Meier Estimate
Bioinformatics
Cohort Studies
03 medical and health sciences
0302 clinical medicine
Neoplasms
Internal medicine
Tobacco Smoking
Precision Medicine Informatics
medicine
Electronic Health Records
Humans
Epidermal growth factor receptor
Precision Medicine
Lung cancer
Proportional Hazards Models
Epidermal Growth Factor
biology
business.industry
Proportional hazards model
Hazard ratio
Computational Biology
Retrospective cohort study
DNA, Neoplasm
medicine.disease
Precision medicine
030104 developmental biology
030220 oncology & carcinogenesis
Mutation
biology.protein
business
Algorithms
GNAQ
Follow-Up Studies
Cohort study
Subjects
Details
- ISSN :
- 1527974X and 10675027
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- Journal of the American Medical Informatics Association
- Accession number :
- edsair.doi.dedup.....9cdd991299366b262c98418bbef7b7aa