Electronic Medical Records as a Tool in Clinical

Electronic Medical Records as a Tool in Clinical Pharmacology: Opportunities and Challenges 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 (EMR) systems for large-scale, high-throughput genetic research with the ultimate goal of returning genomic testing results to patients 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 science 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 timeconsuming and generally cannot generate very large cohorts. Recent efforts have been focused on developing electronic algorithms 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 structured information (e.g., smoker: yes or no) from unstructured narrative clinical text have been used in such algorithms. The use of natural-language processing is especially important to detect diseases and events occurring at facilities outside the recording center and entered as part of the patient’s past medical history and for discovery of rare events that may not be represented in typical 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 in 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 EMR 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 Practitioners Epidemiology Clinical pharmacology

Gene–disease associations EMR The genome-wide association study (GWAS) paradigm whole genome association study associations of phenotypes to single-nucleotide polymorphisms (SNPs) S: SNPs P: phenotypes Incorporate genome data with EMG Figure from Chao, KM

EMR GWAS Example: Common in following diseases: atrial fibrillation ( 心房顫動 ), Crohn’s disease, multiple sclerosis ( 多發性硬化症 ), rheumatoid arthritis ( 類風濕關節炎 ), or type 2 diabetes - common 21 SNPs S: SNPs P: phenotypes (diseases) Figure from Chao, KM

Application of EMRs to pharmacogenomics Discuss phenotypes with treated drugs to patient’s genomic data (SNPs) Example: Breast Cancer: during tamoxifen treatment, SNPs of Estrogenreceptor implicated as risk factors for venous thromboembolic disease ( 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 ), nodular 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 complex no human can be expected to keep track

Summary Opportunity Information exchange between EMRs discovery of new drug actions and of genomic influences on disease phenotypes and drug responses Genomic variation prevention, prognosis and treatment Application in Point of care system