Electronic Medical Records as a Tool in Cli nical Pharmacology: Opportunities and Ch allenges
報告人：蘇為碩 宋柏融 李曉婷 陳凱普 洪詩涵 2014/05/19
Introduction
蘇為碩
Electronic Medical Records
EMR v.s. Traditional Medical Records
Passive v.s. Active
EMR Capabilities
Preventive medical alerts
Aggregate outcomes for many patients
Public health surveillance
The era of big data
EMR with DNA information
Expansion of EMR systems
Combines DNA biorepositories with electronic medical record (E MR) systems for large-scale, high-throughput genetic research wi th the ultimate goal of returning genomic testing results to patie nts in a clinical care setting.
eMERGE
EMR as a Tool in Clinical Pharmacology
Clinical Pharmacology
Science of drugs and their clinical use
Connects the gap between medical practice and laboratory sc ience
What information can EMR provide?
Longitudinal record of health status
Ex: case of rofecoxib and myocardial infarction
Possibly with DNA information
EMR as a Tool in Clinical Pharmacology
EMR-based phenotyping
Manual v.s. Automated
Inclusion of longitudinal data
Enables studies of variability in phenotype
Gene-Disease associations
EMR-based discovery in clinical pharmacology
Approach to EMR-based phenotype
Traditional & Modern EMR-based phenotyping
Traditional approach for phenotyping has been successfully used in an EMR environment, but it is cumbersome and time-consumi ng and generally cannot generate very large cohorts.
Recent efforts have been focused on developing electronic algori thms for determining specific phenotypes from EMRs.
The criterion that we and others have adopted is that algorithms should have very high positive predictive values (PPVs, generally
>90–95%) to be able to identify cohorts for case–control studies.
A general processing
Developing an algorithm and deploy it in an EMR system until a set of cases (generally 50–100) is identified for manual review.
This manual review then determines the PPV and deficiencies in the electronic phenotyping algorithm.
The algorithm is then refined and the process iterated until the threshold PPV is attained.
Phenotype algorithms
Structured data, e.g., ICD-9 codes, lab results
Narrative data, e.g., various types of clinical notes, text messages between patients and care providers
ICD: The International Classification of Diseases (ICD) is the
standard diagnostic tool for epidemiology, health management and
clinical purposes.
Natural-language processing
Natural-language processing technologies that can extract structur ed information (e.g., smoker: yes or no) from unstructured narrativ e clinical text have been used in such algorithms.
The use of natural-language processing is especially important to d etect diseases and events occurring at facilities outside the recordi ng center and entered as part of the patient’s past medical history and for discovery of rare events that may not be represented in typ ical coding systems.
Features of EMRs
Longitudinal data ( 縱貫性研究 ) - Studies of variability in phenotype -- disease complications
-- tempo of disease progression -- response to drug exposures
Structured medication data
- inpatient Physician order entries and drug administration records
- outpatient Resides in the narrative text of clinic notes or interoffice communications
Advantage of longitudinal data
The ability to distinguish among related diagnoses - Data from the multiple visits represented in an EMR
Preliminary data indicate that phenotype definitions developed i n one EMR can be successfully deployed in others
Drug exposures-manual method
Change over time
-- Intolerance, insurance status -- Patient/provider preferences -- Desire to reach clinical targets -- Compliance
Manual method:
experts review different sources -- cumbersome and costly
-- limited sample size
Drug exposures-informatics method
These methods have successfully exploited the longitudinal nature of the E MR to identify drug-response phenotypes
-- cardiovascular events during clopidogrel therapy after coronary stenting.
Rigorous characterization of phenotype, misclassification bias is minimized greater insights into the true genetic architecture underlying
treatment response
Drug exposures-informatics method
-- an application to Warfarin
Collaboration
Among investigators with expertise in multiple disciplines
Biomedical informatics
Clinical pharmacology Epidemiology
Practitioners
Gene–disease associations
The genome-wide association study (GWAS) paradigm
whole genome association study
associations of phenotypes to single-nucleotide polymorphisms (SNPs)
S: SNPs
P: phenotypes
Figure from Chao, KM
Incorporate genome data with EMG
+ EMR
+ GWAS
Example:
Common in following diseases: atrial fibrillation ( 心房顫動 ), Cro hn’s disease, multiple sclerosis ( 多發性硬化症 ), rheumatoid art hritis ( 類風濕關節炎 ), or type 2 diabetes
-> common 21 SNPs
S: SNPs
P: phenotypes (diseases)
Figure from Chao, KM
EMR
Application of EMRs to pharmacogenomics
Discuss phenotypes with treated drugs to patient’s genomic data (SNPs)
Example:
Breast Cancer: during tamoxifen treatment, SNPs of Estrogen-rec eptor implicated as risk factors for venous thromboembolic dise
ase ( 靜脈血栓 )
Phenome scanning
Phenome-wide association study (PheWAS)
Concept: inverse of the GWAS paradigm
examines the relationship between a single genetic variant and a large range of phenotypes
GWAS and PheWAS
Example:
GWAS: examined 1,317 hypothyroidism cases and 5,053 controls -> FOXE1 SNPs
PheWAS: FOXE1 SNPs -> identified thyroiditis ( 甲狀腺炎 ), nodul ar and multinodular goiters ( 甲狀腺腫大 ), and thyrotoxicosis ( 甲狀腺毒症 )
Summary
Challenge
Methods to identify valid cases and controls are being refined
The use of these SNPs for pharmacogenomics has lagged further behind
the phenotyping for drug response is necessarily more compl ex
no human can be expected to keep track
Summary
Opportunity
Information exchange between EMRs
discovery of new drug actions and of genomic influences on dise ase phenotypes and drug responses
Genomic variation → prevention, prognosis and treatment
Application in Point of care system
