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1.  Harmonization and Semantic Annotation of Data Dictionaries from the Pharmacogenomics Research Network: a case study 
Journal of biomedical informatics  2012;46(2):286-293.
The Pharmacogenomics Research Network (PGRN) is a collaborative partnership of research groups funded by NIH to discover and understand how genome contributes to an individual’s response to medication. Since traditional biomedical research studies and clinical trials are often conducted independently, common and standardized representations for data are seldom used. This leads to heterogeneity in data representation, which hinders data reuse, data integration and meta-analyses.
This study demonstrates harmonization and semantic annotation work for pharmacogenomics data dictionaries collected from PGRN research groups. A semi-automated system was developed to support the harmonization/annotation process, which includes four individual steps, 1) pre-processing PGRN variables; 2) decomposing and normalizing variable descriptions; 3) semantically annotating words and phrases using controlled terminologies; 4) grouping PGRN variables into categories based on the annotation results and semantic types, for total 1514 PGRN variables.
Our results demonstrate that there is a significant amount of variability in how pharmacogenomics data is represented and that additional standardization efforts are needed. This represents a critical first step toward identifying and creating data standards for pharmacogenomics studies.
PMCID: PMC3606279  PMID: 23201637
Data harmonization; semantic annotation; Pharmacogenomics
2.  Development and Validation of a Portable Platform for Deploying Decision-Support Algorithms in Prehospital Settings 
Applied Clinical Informatics  2013;4(3):392-402.
Advanced decision-support capabilities for prehospital trauma care may prove effective at improving patient care. Such functionality would be possible if an analysis platform were connected to a transport vital-signs monitor. In practice, there are technical challenges to implementing such a system. Not only must each individual component be reliable, but, in addition, the connectivity between components must be reliable.
We describe the development, validation, and deployment of the Automated Processing of Physiologic Registry for Assessment of Injury Severity (APPRAISE) platform, intended to serve as a test bed to help evaluate the performance of decision-support algorithms in a prehospital environment.
We describe the hardware selected and the software implemented, and the procedures used for laboratory and field testing.
The APPRAISE platform met performance goals in both laboratory testing (using a vital-sign data simulator) and initial field testing. After its field testing, the platform has been in use on Boston MedFlight air ambulances since February of 2010.
These experiences may prove informative to other technology developers and to healthcare stakeholders seeking to invest in connected electronic systems for prehospital as well as in-hospital use. Our experiences illustrate two sets of important questions: are the individual components reliable (e.g., physical integrity, power, core functionality, and end-user interaction) and is the connectivity between components reliable (e.g., communication protocols and the metadata necessary for data interpretation)? While all potential operational issues cannot be fully anticipated and eliminated during development, thoughtful design and phased testing steps can reduce, if not eliminate, technical surprises.
PMCID: PMC3799209  PMID: 24155791
Decision-support algorithms; prehospital care; device connectivity; vital-sign data; combat casualty care
3.  KRAS Testing for Anti-EGFR Therapy in Advanced Colorectal Cancer 
Executive Summary
In February 2010, the Medical Advisory Secretariat (MAS) began work on evidence-based reviews of the literature surrounding three pharmacogenomic tests. This project came about when Cancer Care Ontario (CCO) asked MAS to provide evidence-based analyses on the effectiveness and cost-effectiveness of three oncology pharmacogenomic tests currently in use in Ontario.
Evidence-based analyses have been prepared for each of these technologies. These have been completed in conjunction with internal and external stakeholders, including a Provincial Expert Panel on Pharmacogenomics (PEPP). Within the PEPP, subgroup committees were developed for each disease area. For each technology, an economic analysis was also completed by the Toronto Health Economics and Technology Assessment Collaborative (THETA) and is summarized within the reports.
The following reports can be publicly accessed at the MAS website at: or at
Gene Expression Profiling for Guiding Adjuvant Chemotherapy Decisions in Women with Early Breast Cancer: An Evidence-Based and Economic Analysis
Epidermal Growth Factor Receptor Mutation (EGFR) Testing for Prediction of Response to EGFR-Targeting Tyrosine Kinase Inhibitor (TKI) Drugs in Patients with Advanced Non-Small-Cell Lung Cancer: an Evidence-Based and Economic Analysis
K-RAS testing in Treatment Decisions for Advanced Colorectal Cancer: an Evidence-Based and Economic Analysis.
The objective of this systematic review is to determine the predictive value of KRAS testing in the treatment of metastatic colorectal cancer (mCRC) with two anti-EGFR agents, cetuximab and panitumumab. Economic analyses are also being conducted to evaluate the cost-effectiveness of KRAS testing.
Clinical Need: Condition and Target Population
Metastatic colorectal cancer (mCRC) is usually defined as stage IV disease according to the American Joint Committee on Cancer tumour node metastasis (TNM) system or stage D in the Duke’s classification system. Patients with advanced colorectal cancer (mCRC) either present with metastatic disease or develop it through disease progression.
KRAS (Kristen-RAS, a member of the rat sarcoma virus (ras) gene family of oncogenes) is frequently mutated in epithelial cancers such as colorectal cancer, with mutations occurring in mutational hotspots (codons 12 and 13) of the KRAS protein. Involved in EGFR-mediated signalling of cellular processes such as cell proliferation, resistance to apoptosis, enhanced cell motility and neoangiogenesis, a mutation in the KRAS gene is believed to be involved in cancer pathogenesis. Such a mutation is also hypothesized to be involved in resistance to targeted anti-EGFR (epidermal growth factor receptor with tyrosine kinase activity) treatments such as cetuximab and panitumumab, hence, the important in evaluating the evidence on the predictive value of KRAS testing in this context.
KRAS Mutation Testing in Advanced Colorectal Cancer
Both cetuximab and panitumumab are indicated by Health Canada in the treatment of patients with metastatic colorectal cancer whose tumours are WT for the KRAS gene. Cetuximab may be offered as monotherapy in patients intolerant to irinotecan-based chemotherapy or in patients who have failed both irinotecan and oxaliplatin-based regimens and who received a fluoropyrimidine. It can also be administered in combination with irinotecan in patients refractory to other irinotecan-based chemotherapy regimens. Panitumumab is only indicated as a single agent after failure of fluoropyrimidine-, oxaliplatin-, and irinotecan-containing chemotherapy regimens.
In Ontario, patients with advanced colorectal cancer who are refractory to chemotherapy may be offered the targeted anti-EGFR treatments cetuximab or panitumumab. Eligibility for these treatments is based on the KRAS status of their tumour, derived from tissue collected from surgical or biopsy specimens. It is believed that KRAS status is not affected by treatments, therefore, for patients for whom surgical tissue is available for KRAS testing, additional biopsies prior to treatment with these targeted agents is not necessary. For patients that have not undergone surgery or for whom surgical tissue is not available, a biopsy of either the primary or metastatic site is required to determine their KRAS status. This is possible as status at the metastatic and primary tumour sites is considered to be similar.
Research Question
To determine if there is predictive value of KRAS testing in guiding treatment decisions with anti-EGFR targeted therapies in advanced colorectal cancer patients refractory to chemotherapy.
Research Methods
Literature Search
The Medical Advisory Secretariat followed its standard procedures and on May 18, 2010, searched the following electronic databases: Ovid MEDLINE, EMBASE, Ovid MEDLINE In-Process & Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and The International Network of Agencies for Health Technology Assessment database.
The subject headings and keywords searched included colorectal cancer, cetuximab, panitumumab, and KRAS testing. The search was further restricted to English-language articles published between January 1, 2009 and May 18, 2010 resulting in 1335 articles for review. Excluded were case reports, comments, editorials, nonsystematic reviews, and letters. Studies published from January 1, 2005 to December 31, 2008 were identified in a health technology assessment conducted by the Agency for Healthcare Research and Quality (AHRQ), published in 2010. In total, 14 observational studies were identified for inclusion in this EBA: 4 for cetuximab monotherapy, 7 for the cetuximab-irinotecan combination therapy, and 3 to be included in the review for panitumumab monotherapy
Inclusion Criteria
English-language articles, and English or French-language HTAs published from January 2005 to May 2010, inclusive.
Randomized controlled trials (RCTs) or observational studies, including single arm treatment studies that include KRAS testing.
Studies with data on main outcomes of interest, overall and progression-free survival.
Studies of third line treatment with cetuximab or panitumumab in patients with advanced colorectal cancer refractory to chemotherapy.
For the cetuximab-irinotecan evaluation, studies in which at least 70% of patients in the study received this combination therapy.
Exclusion Criteria
Studies whose entire sample was included in subsequent publications which have been included in this EBA.
Studies in pediatric populations.
Case reports, comments, editorials, or letters.
Outcomes of Interest
Overall survival (OS), median
Progression-free-survival (PFS), median.
Response rates.
Adverse event rates.
Quality of life (QOL).
Summary of Findings of Systematic Review
Cetuximab or Panitumumab Monotherapy
Based on moderate GRADE observational evidence, there is improvement in PFS and OS favouring patients without the KRAS mutation (KRAS wildtype, or KRAS WT) compared to those with the mutation.
Cetuximab-Irinotecan Combination Therapy
There is low GRADE evidence that testing for KRAS may optimize survival benefits in patients without the KRAS mutation (KRAS wildtype, or KRAS WT) compared to those with the mutation.
However, cetuximab-irinotecan combination treatments based on KRAS status discount any effect of cetuximab in possibly reversing resistance to irinotecan in patients with the mutation, as observed effects were lower than for patients without the mutation. Clinical experts have raised concerns about the biological plausibility of this observation and this conclusion would, therefore, be regarded as hypothesis generating.
Economic Analysis
Cost-effectiveness and budget impact analyses were conducted incorporating estimates of effectiveness from this systematic review. Evaluation of relative cost-effectiveness, based on a decision-analytic cost-utility analysis, assessed testing for KRAS genetic mutations versus no testing in the context of treatment with cetuximab monotherapy, panitumumab monotherapy, cetuximab in combination with irinotecan, and best supportive care.
Of importance to note is that the cost-effectiveness analysis focused on the impact of testing for KRAS mutations compared to no testing in the context of different treatment options, and does not assess the cost-effectiveness of the drug treatments alone.
KRAS status is predictive of outcomes in cetuximab and panitumumab monotherapy, and in cetuximab-irinotecan combination therapy.
While KRAS testing is cost-effective for all strategies considered, it is not equally cost-effective for all treatment options.
PMCID: PMC3377508  PMID: 23074403
4.  A Comparison of Acute Hemorrhagic Stroke Outcomes in two Populations: The Crete-Boston Study 
While corticosteroid use in Acute Hemorrhagic Stroke (AHS) is not widely adopted, management with intravenous dexamethasone (IVDxM) has been standard of care at the University Hospital of Heraklion, Crete (UH-Crete) with observed outcomes superior to those reported in literature. To explore this further, we conducted a retrospective, multivariable-adjusted two-center study.
We studied 391 AHS cases admitted to UH-Crete between 1/1997 and 7/2010 and compared them with 510 AHS cases admitted to Massachusetts General Hospital, Boston from 1/2003 to 9/2009. Of the Cretan cases, 340 received a tapering scheme of IVDxM, starting with 16–32 mg/day, while the Boston patients were managed without steroids.
The two cohorts had comparable demographics and stroke severity on admission, although anticoagulation was more frequent in Boston. The in-hospital mortality was significantly lower on Crete (23.8%, n=340) than in Boston (38.0 %, n=510; p<0.001) as was the 30-day mortality (Crete: 25.4%, n=307; Boston: 39.4%, n=510; p<0.001). Exclusion of patients on anticoagulants showed even greater differences (30-day mortality: Crete 20.8%; n=259; Boston 37.0%; n=359; p<0.001). The improved survival on Crete was observed three days after initiation of IVDxM and was pronounced for deep-seated hemorrhages. After adjusting for AHS volume/location, GCS, hypertension, diabetes mellitus, smoking, coronary artery disease and statin, antiplatelet and anticoagulant use, IVDxM treatment was associated with better functional outcomes and significantly lower risk of death at 30-days (odds ratio 0.357; 95% C.I. 0.174–0.732).
This study suggests that IVDxM improves outcome in AHS and supports a randomized clinical trial using this approach.
PMCID: PMC3226858  PMID: 22020030
ICH; Dexamethasone; stroke management
5.  An Ontology-Based, Mobile-Optimized System for Pharmacogenomic Decision Support at the Point-of-Care 
PLoS ONE  2014;9(5):e93769.
The development of genotyping and genetic sequencing techniques and their evolution towards low costs and quick turnaround have encouraged a wide range of applications. One of the most promising applications is pharmacogenomics, where genetic profiles are used to predict the most suitable drugs and drug dosages for the individual patient. This approach aims to ensure appropriate medical treatment and avoid, or properly manage, undesired side effects.
We developed the Medicine Safety Code (MSC) service, a novel pharmacogenomics decision support system, to provide physicians and patients with the ability to represent pharmacogenomic data in computable form and to provide pharmacogenomic guidance at the point-of-care. Pharmacogenomic data of individual patients are encoded as Quick Response (QR) codes and can be decoded and interpreted with common mobile devices without requiring a centralized repository for storing genetic patient data. In this paper, we present the first fully functional release of this system and describe its architecture, which utilizes Web Ontology Language 2 (OWL 2) ontologies to formalize pharmacogenomic knowledge and to provide clinical decision support functionalities.
The MSC system provides a novel approach for enabling the implementation of personalized medicine in clinical routine.
PMCID: PMC4008421  PMID: 24787444
6.  Pharmacogenomics and individualized medicine: Translating science into practice 
Research on genes and medications has advanced our understanding of the genetic basis of individual drug responses. The aim of pharmacogenomics is to develop strategies for individualizing therapy for patients, to optimize outcome through knowledge of human genome variability and its influence on drug response. Pharmacogenomics research is translational in nature and ranges from discovery of genotype-phenotype relationships to clinical trials which provide proof of clinical impact. Advances in pharmacogenomics offer significant potential for subsequent clinical application in individual patients; however, the translation of pharmacogenomics research findings into clinical practice has been slow. Key components to successful clinical implementation of pharmacogenomics will include consistent interpretation of pharmacogenomic test results, availability of clinical guidelines for prescribing based on test results, and knowledge-based decision support systems.
PMCID: PMC3589526  PMID: 22948889
pharmacogenomics; genetic testing; personalized medicine
7.  Electronic Health Record Design and Implementation for Pharmacogenomics: a Local Perspective 
The design of electronic health records (EHR) to translate genomic medicine into clinical care is crucial to successful introduction of new genomic services, yet there are few published guides to implementation.
The design, implemented features, and evolution of a locally developed EHR that supports a large pharmacogenomics program at a tertiary care academic medical center was tracked over a 4-year development period.
Developers and program staff created EHR mechanisms for ordering a pharmacogenomics panel in advance of clinical need (preemptive genotyping) and in response to a specific drug indication. Genetic data from panel-based genotyping were sequestered from the EHR until drug-gene interactions (DGIs) met evidentiary standards and deemed clinically actionable. A service to translate genotype to predicted drug response phenotype populated a summary of DGIs, triggered inpatient and outpatient clinical decision support, updated laboratory records, and created gene results within online personal health records.
The design of a locally developed EHR supporting pharmacogenomics has generalizable utility. The challenge of representing genomic data in a comprehensible and clinically actionable format is discussed along with reflection on the scalability of the model to larger sets of genomic data.
PMCID: PMC3925979  PMID: 24009000
8.  Semantically enabling pharmacogenomic data for the realization of personalized medicine 
Pharmacogenomics  2012;13(2):201-212.
Understanding how each individual’s genetics and physiology influences pharmaceutical response is crucial to the realization of personalized medicine and the discovery and validation of pharmacogenomic biomarkers is key to its success. However, integration of genotype and phenotype knowledge in medical information systems remains a critical challenge. The inability to easily and accurately integrate the results of biomolecular studies with patients’ medical records and clinical reports prevents us from realizing the full potential of pharmacogenomic knowledge for both drug development and clinical practice. Herein, we describe approaches using Semantic Web technologies, in which pharmacogenomic knowledge relevant to drug development and medical decision support is represented in such a way that it can be efficiently accessed both by software and human experts. We suggest that this approach increases the utility of data, and that such computational technologies will become an essential part of personalized medicine, alongside diagnostics and pharmaceutical products.
PMCID: PMC3957334  PMID: 22256869
clinical decision support systems; knowledge representation; ontologies; personalized medicine; pharmacogenomics; translational medicine
9.  Implementation of workflow engine technology to deliver basic clinical decision support functionality 
Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic.
We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture.
We describe an implementation of a free workflow technology software suite (available at and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.
PMCID: PMC3079703  PMID: 21477364
10.  Pharmacogenomic knowledge gaps and educational resource needs among physicians in selected specialties 
The use of pharmacogenomic testing in the clinical setting has the potential to improve the safety and effectiveness of drug therapy, yet studies have revealed that physicians lack knowledge about the topic of pharmacogenomics, and are not prepared to implement it in the clinical setting. This study further explores the pharmacogenomic knowledge deficit and educational resource needs among physicians.
Materials and methods
Surveys of primary care physicians, cardiologists, and psychiatrists were conducted.
Few physicians reported familiarity with the topic of pharmacogenomics, but more reported confidence in their knowledge about the influence of genetics on drug therapy. Only a small minority had undergone formal training in pharmacogenomics, and a majority reported being unsure what type of pharmacogenomic tests were appropriate to order for the clinical situation. Respondents indicated that an ideal pharmacogenomic educational resource should be electronic and include such components as how to interpret pharmacogenomic test results, recommendations for prescribing, population subgroups most likely to be affected, and contact information for laboratories offering pharmacogenomic testing.
Physicians continue to demonstrate pharmacogenomic knowledge gaps, and are unsure about how to use pharmacogenomic testing in clinical practice. Educational resources that are clinically oriented and easily accessible are preferred by physicians, and may best support appropriate clinical implementation of pharmacogenomics.
PMCID: PMC4100727  PMID: 25045280
pharmacogenomics; knowledge gap; drug response; educational resource
11.  DMET-Analyzer: automatic analysis of Affymetrix DMET Data 
BMC Bioinformatics  2012;13:258.
Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs.
We developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i) to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists) to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different case studies regarding the analysis of clinical datasets produced in the University Hospital of Catanzaro, Italy.
DMET Analyzer is a novel tool able to automatically analyse data produced by the DMET-platform in case-control association studies. Using such tool user may avoid wasting time in the manual execution of multiple statistical tests avoiding possible errors and reducing the amount of time needed for a whole experiment. Moreover annotations and the direct link to external databases may increase the biological knowledge extracted. The system is freely available for academic purposes at:
PMCID: PMC3496574  PMID: 23035929
12.  Using Clinical Element Models for Pharmacogenomic Study Data Standardization  
Standardized representations for pharmacogenomics data are seldom used, which leads to data heterogeneity and hinders data reuse and integration. In this study, we attempted to represent data elements from the Pharmacogenomics Research Network (PGRN) that are related to four categories, patient, drug, disease and laboratory, in a standard way using Clinical Element Models (CEMs), which have been adopted in the Strategic Health IT Advanced Research Project, secondary use of EHR (SHARPn) as a library of common logical models that facilitate consistent data representation, interpretation, and exchange within and across heterogeneous sources and applications. This was accomplished by grouping PGRN data elements into categories based on UMLS semantic type, then mapping each to one or more CEM attributes using a web-based tool that was developed to support curation activities. This study demonstrates the successful application of SHARPn CEMs to the pharmacogenomic domain. It also identified several categories of data elements that are not currently supported by SHARPn CEMs, which represent opportunities for further development and collaboration.
PMCID: PMC3845787  PMID: 24303283
13.  The Role of the Pediatric Pharmacist in Personalized Medicine and Clinical Pharmacogenomics for Children 
With the initiatives by the National Institutes of Health and the Food and Drug Administration, pharmacogenomics has now moved from the laboratory to the patient bedside. Over 100 drug-products now contain pharmacogenomic information as part of their labeling. Many of these are commonly used in the pediatric population. Direct-to-consumer genetic test kits also require intervention and guidance from healthcare professionals. This increased trend towards personalized medicine mandates that healthcare professionals develop a working knowledge about pharmacogenomics and its application towards patient care. Because pharmacogenomic testing can provide patient-specific predictors for response to and safety of medications, pharmacists are positioned to play an active role in pharmacogenomic testing, clinical interpretation of results, and recommendations for individualization of drug therapy. Opportunities for pharmacists exist in both inpatient and outpatient settings, such as pharmacist-managed clinical pharmacogenomics consultation services and educating patients and families about pharmacogenomic testing. In addition to clinical roles, pharmacists may also be involved in genetically-influenced drug discovery and development. Given the potential for genetic and age-dependent factors to influence drug selection and dosing, pediatric pharmacists should be involved in the development of dosing recommendations and interprofessional practice guidelines regarding pharmacogenomic testing in pediatric patients. Opportunities to become knowledgeable and competent in pharmacogenomics span from coursework as part of the pharmacy curriculum to postgraduate education (e.g., residencies, fellowships, continuing education). However, there exists a need for additional postgraduate learning opportunities for practicing pharmacists. As a result, the Pediatric Pharmacy Advocacy Group (PPAG) acknowledges a need for increased education of both student and practicing pharmacists, with consideration of special patient populations, such as infants and children. PPAG endorses and advocates for the involvement of pediatric pharmacists in pharmacogenomic testing and in using those results to provide safe and effective medication use in pediatric patients of all ages. Additionally, PPAG strongly encourages pediatric pharmacists to take responsibility for educating patients and their families about the importance of pharmacogenomic testing and its role in the safe and effective use of medications.
PMCID: PMC3208440  PMID: 22477836
pediatrics; personalized medicine; pharmacist; pharmacogenomics; testing
14.  Mapping the Incidentalome: Estimating Incidental Findings Generated Through Clinical Pharmacogenomics Testing 
Greater clinical validity and economic feasibility are driving the more widespread use of multiplex genetic technologies in routine clinical care, especially for pharmacogenomics applications. Empirical data on the numbers and types of incidental findings generated through such testing are needed so that policies and practices for their clinical use can be developed. Of particular importance are disparities in findings relevant to different ancestry groups.
The Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment Resource (PREDICT) is an institutional program to implement prospective clinical genotyping of 34 pharmacogenomic-related genes to guide drug selection and dosing. We curated 5566 journal articles to quantify and characterize the incidental, non-pharmacogenomic genotype-phenotype associations that could be generated through this clinical genotyping project.
We identified 372 putative incidental genotype-phenotype associations that might be revealed in patients undergoing clinical genotyping for pharmacogenomic purposes. Of these, 287 associations were supported by at least one study demonstrating an odds ratio ≥2.0 or ≤0.5. Numbers of potentially relevant findings varied widely by ancestry group.
Rigorous clinical policies for the clinical management of incidental findings are needed since the sheer number of significant findings could prove overwhelming to healthcare institutions, providers, and patients.
PMCID: PMC3648626  PMID: 23196672
Incidental Findings; Incidentalome; Pharmacogenomics; Disease Susceptibility; Genomics
15.  Pharmacogenomics: Application to the Management of Cardiovascular Disease 
The past decade has seen substantial advances in cardiovascular pharmacogenomics. Genetic determinants of response to clopidogrel and warfarin have been defined, resulting in changes to the product labels for these drugs that suggest the use of genetic information as a guide for therapy. Genetic tests are available, as are guidelines for incorporation of genetic information into patient-care decisions. These guidelines and the literature supporting them are reviewed herein. Significant advances have also been made in the pharmacogenomics of statin-induced myopathy and the response to β-blockers in heart failure, although the clinical applications of these findings are less clear. Other areas hold promise, including the pharmacogenomics of antihypertensive drugs, aspirin, and drug-induced long-QT syndrome (diLQTS). The potential value of pharmacogenomics in the discovery and development of new drugs is also described. In summary, pharmacogenomics has current applications in the management of cardiovascular disease, with clinically relevant data continuing to mount.
PMCID: PMC3190669  PMID: 21918509
16.  Chapter 7: Pharmacogenomics 
PLoS Computational Biology  2012;8(12):e1002817.
There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.
PMCID: PMC3531317  PMID: 23300409
17.  Assessing the cost-effectiveness of pharmacogenomics 
AAPS PharmSci  2000;2(3):80-90.
The use of pharmacogenomics to individualize drug therapy offers the potential to improve drug effectiveness, reduce adverse side effects, and provide cost-effective pharmaceutical care. However, the combinations of disease, drug, and genetic test characteristics that will provide clinically useful and economically feasible therapeutic interventions have not been clearly elucidated. The purpose of this paper was to develop a framework for evaluating the potential cost-effectiveness of pharmacogenomic strategies that will help scientists better understand the strategic implications of their research assist in the design of clinical trials, and provide a guide for health care providers making reimbursement decisions. We reviewed concepts of cost-effectiveness analysis and pharmacogenomics and identified 5 primary characteristics that will enhance the cost-effectiveness of pharmacogenomics: 1) there are severe clinical or economic consequence that are avoided through the use of pharmacogenomics, 2) monitoring drug response using current methods is difficult, 3) a well-established association between genotype and clinical phenotype exists, 4) there is a rapid and relatively inexpensive genetic test, and 5) the variant gene is relatively common. We use this framework to evaluate several examples of pharmacogenomics. We found that pharmacogenomics offers great potential to improve patients' health in a cost-effective manner. However, pharmacogenomics will not be applied to all currently marketed drugs, and careful evaluations are needed on a case-by-case basis before investing resources in research and development of pharmacogenomic-based therapeutics and making reimbursement decisions.
PMCID: PMC2761139  PMID: 11741245
18.  Pharmacogenomics in Early Phase Oncology Clinical Trials: Is There a Sweet Spot in Phase II? 
Clinical Cancer Research  2012;18(10):2809-2816.
Many clinical trials of oncology drugs now include at least a consideration of pharmacogenomics, the study of germline or acquired genetic factors governing a drug's response and toxicity. Besides the potential benefit to patients from the consideration of personalized pharmacogenomic information when making treatment decisions, there is a clear incentive for oncology drug developers to incorporate pharmacogenomic factors in the drug development process since pharmacogenomic biomarkers may allow predictive characterization of sub-populations within a disease that may particularly respond, or may allow preidentification of patients at highest risk for adverse events. There is, however, a lack of agreement in actual practice as to where in the oncology clinical drug development process pharmacogenomic studies should be incorporated. In this article, we examine the recent growth of pharmacogenomics in oncology clinical trials, especially in early phase studies, and examine several critical questions facing the incorporation of pharmacogenomics in early oncologic drug development. We show that phase II clinical trials in particular have a favorable track record for demonstrating positive pharmacogenomic signals, worthy of additional follow-up and validation, and that the phase II setting holds significant promise for potentially accelerating and informing future phase III trials. We conclude that phase II trials offer an ideal “sweet spot” for routine incorporation of pharmacogenomic questions in oncology drug development.
PMCID: PMC3354016  PMID: 22427349
phase II; oncology; clinical trials; pharmacogenomics; biomarker development
19.  Internet-Based Device-Assisted Remote Monitoring of Cardiovascular Implantable Electronic Devices 
Executive Summary
The objective of this Medical Advisory Secretariat (MAS) report was to conduct a systematic review of the available published evidence on the safety, effectiveness, and cost-effectiveness of Internet-based device-assisted remote monitoring systems (RMSs) for therapeutic cardiac implantable electronic devices (CIEDs) such as pacemakers (PMs), implantable cardioverter-defibrillators (ICDs), and cardiac resynchronization therapy (CRT) devices. The MAS evidence-based review was performed to support public financing decisions.
Clinical Need: Condition and Target Population
Sudden cardiac death (SCD) is a major cause of fatalities in developed countries. In the United States almost half a million people die of SCD annually, resulting in more deaths than stroke, lung cancer, breast cancer, and AIDS combined. In Canada each year more than 40,000 people die from a cardiovascular related cause; approximately half of these deaths are attributable to SCD.
Most cases of SCD occur in the general population typically in those without a known history of heart disease. Most SCDs are caused by cardiac arrhythmia, an abnormal heart rhythm caused by malfunctions of the heart’s electrical system. Up to half of patients with significant heart failure (HF) also have advanced conduction abnormalities.
Cardiac arrhythmias are managed by a variety of drugs, ablative procedures, and therapeutic CIEDs. The range of CIEDs includes pacemakers (PMs), implantable cardioverter-defibrillators (ICDs), and cardiac resynchronization therapy (CRT) devices. Bradycardia is the main indication for PMs and individuals at high risk for SCD are often treated by ICDs.
Heart failure (HF) is also a significant health problem and is the most frequent cause of hospitalization in those over 65 years of age. Patients with moderate to severe HF may also have cardiac arrhythmias, although the cause may be related more to heart pump or haemodynamic failure. The presence of HF, however, increases the risk of SCD five-fold, regardless of aetiology. Patients with HF who remain highly symptomatic despite optimal drug therapy are sometimes also treated with CRT devices.
With an increasing prevalence of age-related conditions such as chronic HF and the expanding indications for ICD therapy, the rate of ICD placement has been dramatically increasing. The appropriate indications for ICD placement, as well as the rate of ICD placement, are increasingly an issue. In the United States, after the introduction of expanded coverage of ICDs, a national ICD registry was created in 2005 to track these devices. A recent survey based on this national ICD registry reported that 22.5% (25,145) of patients had received a non-evidence based ICD and that these patients experienced significantly higher in-hospital mortality and post-procedural complications.
In addition to the increased ICD device placement and the upfront device costs, there is the need for lifelong follow-up or surveillance, placing a significant burden on patients and device clinics. In 2007, over 1.6 million CIEDs were implanted in Europe and the United States, which translates to over 5.5 million patient encounters per year if the recommended follow-up practices are considered. A safe and effective RMS could potentially improve the efficiency of long-term follow-up of patients and their CIEDs.
In addition to being therapeutic devices, CIEDs have extensive diagnostic abilities. All CIEDs can be interrogated and reprogrammed during an in-clinic visit using an inductive programming wand. Remote monitoring would allow patients to transmit information recorded in their devices from the comfort of their own homes. Currently most ICD devices also have the potential to be remotely monitored. Remote monitoring (RM) can be used to check system integrity, to alert on arrhythmic episodes, and to potentially replace in-clinic follow-ups and manage disease remotely. They do not currently have the capability of being reprogrammed remotely, although this feature is being tested in pilot settings.
Every RMS is specifically designed by a manufacturer for their cardiac implant devices. For Internet-based device-assisted RMSs, this customization includes details such as web application, multiplatform sensors, custom algorithms, programming information, and types and methods of alerting patients and/or physicians. The addition of peripherals for monitoring weight and pressure or communicating with patients through the onsite communicators also varies by manufacturer. Internet-based device-assisted RMSs for CIEDs are intended to function as a surveillance system rather than an emergency system.
Health care providers therefore need to learn each application, and as more than one application may be used at one site, multiple applications may need to be reviewed for alarms. All RMSs deliver system integrity alerting; however, some systems seem to be better geared to fast arrhythmic alerting, whereas other systems appear to be more intended for remote follow-up or supplemental remote disease management. The different RMSs may therefore have different impacts on workflow organization because of their varying frequency of interrogation and methods of alerts. The integration of these proprietary RM web-based registry systems with hospital-based electronic health record systems has so far not been commonly implemented.
Currently there are 2 general types of RMSs: those that transmit device diagnostic information automatically and without patient assistance to secure Internet-based registry systems, and those that require patient assistance to transmit information. Both systems employ the use of preprogrammed alerts that are either transmitted automatically or at regular scheduled intervals to patients and/or physicians.
The current web applications, programming, and registry systems differ greatly between the manufacturers of transmitting cardiac devices. In Canada there are currently 4 manufacturers—Medtronic Inc., Biotronik, Boston Scientific Corp., and St Jude Medical Inc.—which have regulatory approval for remote transmitting CIEDs. Remote monitoring systems are proprietary to the manufacturer of the implant device. An RMS for one device will not work with another device, and the RMS may not work with all versions of the manufacturer’s devices.
All Internet-based device-assisted RMSs have common components. The implanted device is equipped with a micro-antenna that communicates with a small external device (at bedside or wearable) commonly known as the transmitter. Transmitters are able to interrogate programmed parameters and diagnostic data stored in the patients’ implant device. The information transfer to the communicator can occur at preset time intervals with the participation of the patient (waving a wand over the device) or it can be sent automatically (wirelessly) without their participation. The encrypted data are then uploaded to an Internet-based database on a secure central server. The data processing facilities at the central database, depending on the clinical urgency, can trigger an alert for the physician(s) that can be sent via email, fax, text message, or phone. The details are also posted on the secure website for viewing by the physician (or their delegate) at their convenience.
Research Questions
The research directions and specific research questions for this evidence review were as follows:
To identify the Internet-based device-assisted RMSs available for follow-up of patients with therapeutic CIEDs such as PMs, ICDs, and CRT devices.
To identify the potential risks, operational issues, or organizational issues related to Internet-based device-assisted RM for CIEDs.
To evaluate the safety, acceptability, and effectiveness of Internet-based device-assisted RMSs for CIEDs such as PMs, ICDs, and CRT devices.
To evaluate the safety, effectiveness, and cost-effectiveness of Internet-based device-assisted RMSs for CIEDs compared to usual outpatient in-office monitoring strategies.
To evaluate the resource implications or budget impact of RMSs for CIEDs in Ontario, Canada.
Research Methods
Literature Search
The review included a systematic review of published scientific literature and consultations with experts and manufacturers of all 4 approved RMSs for CIEDs in Canada. Information on CIED cardiac implant clinics was also obtained from Provincial Programs, a division within the Ministry of Health and Long-Term Care with a mandate for cardiac implant specialty care. Various administrative databases and registries were used to outline the current clinical follow-up burden of CIEDs in Ontario. The provincial population-based ICD database developed and maintained by the Institute for Clinical Evaluative Sciences (ICES) was used to review the current follow-up practices with Ontario patients implanted with ICD devices.
Search Strategy
A literature search was performed on September 21, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from 1950 to September 2010. Search alerts were generated and reviewed for additional relevant literature until December 31, 2010. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria full-text articles were obtained. Reference lists were also examined for any additional relevant studies not identified through the search.
Inclusion Criteria
published between 1950 and September 2010;
English language full-reports and human studies;
original reports including clinical evaluations of Internet-based device-assisted RMSs for CIEDs in clinical settings;
reports including standardized measurements on outcome events such as technical success, safety, effectiveness, cost, measures of health care utilization, morbidity, mortality, quality of life or patient satisfaction;
randomized controlled trials (RCTs), systematic reviews and meta-analyses, cohort and controlled clinical studies.
Exclusion Criteria
non-systematic reviews, letters, comments and editorials;
reports not involving standardized outcome events;
clinical reports not involving Internet-based device assisted RM systems for CIEDs in clinical settings;
reports involving studies testing or validating algorithms without RM;
studies with small samples (<10 subjects).
Outcomes of Interest
The outcomes of interest included: technical outcomes, emergency department visits, complications, major adverse events, symptoms, hospital admissions, clinic visits (scheduled and/or unscheduled), survival, morbidity (disease progression, stroke, etc.), patient satisfaction, and quality of life.
Summary of Findings
The MAS evidence review was performed to review available evidence on Internet-based device-assisted RMSs for CIEDs published until September 2010. The search identified 6 systematic reviews, 7 randomized controlled trials, and 19 reports for 16 cohort studies—3 of these being registry-based and 4 being multi-centered. The evidence is summarized in the 3 sections that follow.
1. Effectiveness of Remote Monitoring Systems of CIEDs for Cardiac Arrhythmia and Device Functioning
In total, 15 reports on 13 cohort studies involving investigations with 4 different RMSs for CIEDs in cardiology implant clinic groups were identified in the review. The 4 RMSs were: Care Link Network® (Medtronic Inc,, Minneapolis, MN, USA); Home Monitoring® (Biotronic, Berlin, Germany); House Call 11® (St Jude Medical Inc., St Pauls, MN, USA); and a manufacturer-independent RMS. Eight of these reports were with the Home Monitoring® RMS (12,949 patients), 3 were with the Care Link® RMS (167 patients), 1 was with the House Call 11® RMS (124 patients), and 1 was with a manufacturer-independent RMS (44 patients). All of the studies, except for 2 in the United States, (1 with Home Monitoring® and 1 with House Call 11®), were performed in European countries.
The RMSs in the studies were evaluated with different cardiac implant device populations: ICDs only (6 studies), ICD and CRT devices (3 studies), PM and ICD and CRT devices (4 studies), and PMs only (2 studies). The patient populations were predominately male (range, 52%–87%) in all studies, with mean ages ranging from 58 to 76 years. One study population was unique in that RMSs were evaluated for ICDs implanted solely for primary prevention in young patients (mean age, 44 years) with Brugada syndrome, which carries an inherited increased genetic risk for sudden heart attack in young adults.
Most of the cohort studies reported on the feasibility of RMSs in clinical settings with limited follow-up. In the short follow-up periods of the studies, the majority of the events were related to detection of medical events rather than system configuration or device abnormalities. The results of the studies are summarized below:
The interrogation of devices on the web platform, both for continuous and scheduled transmissions, was significantly quicker with remote follow-up, both for nurses and physicians.
In a case-control study focusing on a Brugada population–based registry with patients followed-up remotely, there were significantly fewer outpatient visits and greater detection of inappropriate shocks. One death occurred in the control group not followed remotely and post-mortem analysis indicated early signs of lead failure prior to the event.
Two studies examined the role of RMSs in following ICD leads under regulatory advisory in a European clinical setting and noted:
– Fewer inappropriate shocks were administered in the RM group.
– Urgent in-office interrogations and surgical revisions were performed within 12 days of remote alerts.
– No signs of lead fracture were detected at in-office follow-up; all were detected at remote follow-up.
Only 1 study reported evaluating quality of life in patients followed up remotely at 3 and 6 months; no values were reported.
Patient satisfaction was evaluated in 5 cohort studies, all in short term follow-up: 1 for the Home Monitoring® RMS, 3 for the Care Link® RMS, and 1 for the House Call 11® RMS.
– Patients reported receiving a sense of security from the transmitter, a good relationship with nurses and physicians, positive implications for their health, and satisfaction with RM and organization of services.
– Although patients reported that the system was easy to implement and required less than 10 minutes to transmit information, a variable proportion of patients (range, 9% 39%) reported that they needed the assistance of a caregiver for their transmission.
– The majority of patients would recommend RM to other ICD patients.
– Patients with hearing or other physical or mental conditions hindering the use of the system were excluded from studies, but the frequency of this was not reported.
Physician satisfaction was evaluated in 3 studies, all with the Care Link® RMS:
– Physicians reported an ease of use and high satisfaction with a generally short-term use of the RMS.
– Physicians reported being able to address the problems in unscheduled patient transmissions or physician initiated transmissions remotely, and were able to handle the majority of the troubleshooting calls remotely.
– Both nurses and physicians reported a high level of satisfaction with the web registry system.
2. Effectiveness of Remote Monitoring Systems in Heart Failure Patients for Cardiac Arrhythmia and Heart Failure Episodes
Remote follow-up of HF patients implanted with ICD or CRT devices, generally managed in specialized HF clinics, was evaluated in 3 cohort studies: 1 involved the Home Monitoring® RMS and 2 involved the Care Link® RMS. In these RMSs, in addition to the standard diagnostic features, the cardiac devices continuously assess other variables such as patient activity, mean heart rate, and heart rate variability. Intra-thoracic impedance, a proxy measure for lung fluid overload, was also measured in the Care Link® studies. The overall diagnostic performance of these measures cannot be evaluated, as the information was not reported for patients who did not experience intra-thoracic impedance threshold crossings or did not undergo interventions. The trial results involved descriptive information on transmissions and alerts in patients experiencing high morbidity and hospitalization in the short study periods.
3. Comparative Effectiveness of Remote Monitoring Systems for CIEDs
Seven RCTs were identified evaluating RMSs for CIEDs: 2 were for PMs (1276 patients) and 5 were for ICD/CRT devices (3733 patients). Studies performed in the clinical setting in the United States involved both the Care Link® RMS and the Home Monitoring® RMS, whereas all studies performed in European countries involved only the Home Monitoring® RMS.
3A. Randomized Controlled Trials of Remote Monitoring Systems for Pacemakers
Two trials, both multicenter RCTs, were conducted in different countries with different RMSs and study objectives. The PREFER trial was a large trial (897 patients) performed in the United States examining the ability of Care Link®, an Internet-based remote PM interrogation system, to detect clinically actionable events (CAEs) sooner than the current in-office follow-up supplemented with transtelephonic monitoring transmissions, a limited form of remote device interrogation. The trial results are summarized below:
In the 375-day mean follow-up, 382 patients were identified with at least 1 CAE—111 patients in the control arm and 271 in the remote arm.
The event rate detected per patient for every type of CAE, except for loss of atrial capture, was higher in the remote arm than the control arm.
The median time to first detection of CAEs (4.9 vs. 6.3 months) was significantly shorter in the RMS group compared to the control group (P < 0.0001).
Additionally, only 2% (3/190) of the CAEs in the control arm were detected during a transtelephonic monitoring transmission (the rest were detected at in-office follow-ups), whereas 66% (446/676) of the CAEs were detected during remote interrogation.
The second study, the OEDIPE trial, was a smaller trial (379 patients) performed in France evaluating the ability of the Home Monitoring® RMS to shorten PM post-operative hospitalization while preserving the safety of conventional management of longer hospital stays.
Implementation and operationalization of the RMS was reported to be successful in 91% (346/379) of the patients and represented 8144 transmissions.
In the RM group 6.5% of patients failed to send messages (10 due to improper use of the transmitter, 2 with unmanageable stress). Of the 172 patients transmitting, 108 patients sent a total of 167 warnings during the trial, with a greater proportion of warnings being attributed to medical rather than technical causes.
Forty percent had no warning message transmission and among these, 6 patients experienced a major adverse event and 1 patient experienced a non-major adverse event. Of the 6 patients having a major adverse event, 5 contacted their physician.
The mean medical reaction time was faster in the RM group (6.5 ± 7.6 days vs. 11.4 ± 11.6 days).
The mean duration of hospitalization was significantly shorter (P < 0.001) for the RM group than the control group (3.2 ± 3.2 days vs. 4.8 ± 3.7 days).
Quality of life estimates by the SF-36 questionnaire were similar for the 2 groups at 1-month follow-up.
3B. Randomized Controlled Trials Evaluating Remote Monitoring Systems for ICD or CRT Devices
The 5 studies evaluating the impact of RMSs with ICD/CRT devices were conducted in the United States and in European countries and involved 2 RMSs—Care Link® and Home Monitoring ®. The objectives of the trials varied and 3 of the trials were smaller pilot investigations.
The first of the smaller studies (151 patients) evaluated patient satisfaction, achievement of patient outcomes, and the cost-effectiveness of the Care Link® RMS compared to quarterly in-office device interrogations with 1-year follow-up.
Individual outcomes such as hospitalizations, emergency department visits, and unscheduled clinic visits were not significantly different between the study groups.
Except for a significantly higher detection of atrial fibrillation in the RM group, data on ICD detection and therapy were similar in the study groups.
Health-related quality of life evaluated by the EuroQoL at 6-month or 12-month follow-up was not different between study groups.
Patients were more satisfied with their ICD care in the clinic follow-up group than in the remote follow-up group at 6-month follow-up, but were equally satisfied at 12- month follow-up.
The second small pilot trial (20 patients) examined the impact of RM follow-up with the House Call 11® system on work schedules and cost savings in patients randomized to 2 study arms varying in the degree of remote follow-up.
The total time including device interrogation, transmission time, data analysis, and physician time required was significantly shorter for the RM follow-up group.
The in-clinic waiting time was eliminated for patients in the RM follow-up group.
The physician talk time was significantly reduced in the RM follow-up group (P < 0.05).
The time for the actual device interrogation did not differ in the study groups.
The third small trial (115 patients) examined the impact of RM with the Home Monitoring® system compared to scheduled trimonthly in-clinic visits on the number of unplanned visits, total costs, health-related quality of life (SF-36), and overall mortality.
There was a 63.2% reduction in in-office visits in the RM group.
Hospitalizations or overall mortality (values not stated) were not significantly different between the study groups.
Patient-induced visits were higher in the RM group than the in-clinic follow-up group.
The TRUST Trial
The TRUST trial was a large multicenter RCT conducted at 102 centers in the United States involving the Home Monitoring® RMS for ICD devices for 1450 patients. The primary objectives of the trial were to determine if remote follow-up could be safely substituted for in-office clinic follow-up (3 in-office visits replaced) and still enable earlier physician detection of clinically actionable events.
Adherence to the protocol follow-up schedule was significantly higher in the RM group than the in-office follow-up group (93.5% vs. 88.7%, P < 0.001).
Actionability of trimonthly scheduled checks was low (6.6%) in both study groups. Overall, actionable causes were reprogramming (76.2%), medication changes (24.8%), and lead/system revisions (4%), and these were not different between the 2 study groups.
The overall mean number of in-clinic and hospital visits was significantly lower in the RM group than the in-office follow-up group (2.1 per patient-year vs. 3.8 per patient-year, P < 0.001), representing a 45% visit reduction at 12 months.
The median time from onset of first arrhythmia to physician evaluation was significantly shorter (P < 0.001) in the RM group than in the in-office follow-up group for all arrhythmias (1 day vs. 35.5 days).
The median time to detect clinically asymptomatic arrhythmia events—atrial fibrillation (AF), ventricular fibrillation (VF), ventricular tachycardia (VT), and supra-ventricular tachycardia (SVT)—was also significantly shorter (P < 0.001) in the RM group compared to the in-office follow-up group (1 day vs. 41.5 days) and was significantly quicker for each of the clinical arrhythmia events—AF (5.5 days vs. 40 days), VT (1 day vs. 28 days), VF (1 day vs. 36 days), and SVT (2 days vs. 39 days).
System-related problems occurred infrequently in both groups—in 1.5% of patients (14/908) in the RM group and in 0.7% of patients (3/432) in the in-office follow-up group.
The overall adverse event rate over 12 months was not significantly different between the 2 groups and individual adverse events were also not significantly different between the RM group and the in-office follow-up group: death (3.4% vs. 4.9%), stroke (0.3% vs. 1.2%), and surgical intervention (6.6% vs. 4.9%), respectively.
The 12-month cumulative survival was 96.4% (95% confidence interval [CI], 95.5%–97.6%) in the RM group and 94.2% (95% confidence interval [CI], 91.8%–96.6%) in the in-office follow-up group, and was not significantly different between the 2 groups (P = 0.174).
The CONNECT trial, another major multicenter RCT, involved the Care Link® RMS for ICD/CRT devices in a15-month follow-up study of 1,997 patients at 133 sites in the United States. The primary objective of the trial was to determine whether automatically transmitted physician alerts decreased the time from the occurrence of clinically relevant events to medical decisions. The trial results are summarized below:
Of the 575 clinical alerts sent in the study, 246 did not trigger an automatic physician alert. Transmission failures were related to technical issues such as the alert not being programmed or not being reset, and/or a variety of patient factors such as not being at home and the monitor not being plugged in or set up.
The overall mean time from the clinically relevant event to the clinical decision was significantly shorter (P < 0.001) by 17.4 days in the remote follow-up group (4.6 days for 172 patients) than the in-office follow-up group (22 days for 145 patients).
– The median time to a clinical decision was shorter in the remote follow-up group than in the in-office follow-up group for an AT/AF burden greater than or equal to 12 hours (3 days vs. 24 days) and a fast VF rate greater than or equal to 120 beats per minute (4 days vs. 23 days).
Although infrequent, similar low numbers of events involving low battery and VF detection/therapy turned off were noted in both groups. More alerts, however, were noted for out-of-range lead impedance in the RM group (18 vs. 6 patients), and the time to detect these critical events was significantly shorter in the RM group (same day vs. 17 days).
Total in-office clinic visits were reduced by 38% from 6.27 visits per patient-year in the in-office follow-up group to 3.29 visits per patient-year in the remote follow-up group.
Health care utilization visits (N = 6,227) that included cardiovascular-related hospitalization, emergency department visits, and unscheduled clinic visits were not significantly higher in the remote follow-up group.
The overall mean length of hospitalization was significantly shorter (P = 0.002) for those in the remote follow-up group (3.3 days vs. 4.0 days) and was shorter both for patients with ICD (3.0 days vs. 3.6 days) and CRT (3.8 days vs. 4.7 days) implants.
The mortality rate between the study arms was not significantly different between the follow-up groups for the ICDs (P = 0.31) or the CRT devices with defribillator (P = 0.46).
There is limited clinical trial information on the effectiveness of RMSs for PMs. However, for RMSs for ICD devices, multiple cohort studies and 2 large multicenter RCTs demonstrated feasibility and significant reductions in in-office clinic follow-ups with RMSs in the first year post implantation. The detection rates of clinically significant events (and asymptomatic events) were higher, and the time to a clinical decision for these events was significantly shorter, in the remote follow-up groups than in the in-office follow-up groups. The earlier detection of clinical events in the remote follow-up groups, however, was not associated with lower morbidity or mortality rates in the 1-year follow-up. The substitution of almost all the first year in-office clinic follow-ups with RM was also not associated with an increased health care utilization such as emergency department visits or hospitalizations.
The follow-up in the trials was generally short-term, up to 1 year, and was a more limited assessment of potential longer term device/lead integrity complications or issues. None of the studies compared the different RMSs, particularly the different RMSs involving patient-scheduled transmissions or automatic transmissions. Patients’ acceptance of and satisfaction with RM were reported to be high, but the impact of RM on patients’ health-related quality of life, particularly the psychological aspects, was not evaluated thoroughly. Patients who are not technologically competent, having hearing or other physical/mental impairments, were identified as potentially disadvantaged with remote surveillance. Cohort studies consistently identified subgroups of patients who preferred in-office follow-up. The evaluation of costs and workflow impact to the health care system were evaluated in European or American clinical settings, and only in a limited way.
Internet-based device-assisted RMSs involve a new approach to monitoring patients, their disease progression, and their CIEDs. Remote monitoring also has the potential to improve the current postmarket surveillance systems of evolving CIEDs and their ongoing hardware and software modifications. At this point, however, there is insufficient information to evaluate the overall impact to the health care system, although the time saving and convenience to patients and physicians associated with a substitution of in-office follow-up by RM is more certain. The broader issues surrounding infrastructure, impacts on existing clinical care systems, and regulatory concerns need to be considered for the implementation of Internet-based RMSs in jurisdictions involving different clinical practices.
PMCID: PMC3377571  PMID: 23074419
20.  Design and Implementation of a Randomized Controlled Trial of Genomic Counseling for Patients with Chronic Disease 
We describe the development and implementation of a randomized controlled trial to investigate the impact of genomic counseling on a cohort of patients with heart failure (HF) or hypertension (HTN), managed at a large academic medical center, the Ohio State University Wexner Medical Center (OSUWMC). Our study is built upon the existing Coriell Personalized Medicine Collaborative (CPMC®). OSUWMC patient participants with chronic disease (CD) receive eight actionable complex disease and one pharmacogenomic test report through the CPMC® web portal. Participants are randomized to either the in-person post-test genomic counseling—active arm, versus web-based only return of results—control arm. Study-specific surveys measure: (1) change in risk perception; (2) knowledge retention; (3) perceived personal control; (4) health behavior change; and, for the active arm (5), overall satisfaction with genomic counseling. This ongoing partnership has spurred creation of both infrastructure and procedures necessary for the implementation of genomics and genomic counseling in clinical care and clinical research. This included creation of a comprehensive informed consent document and processes for prospective return of actionable results for multiple complex diseases and pharmacogenomics (PGx) through a web portal, and integration of genomic data files and clinical decision support into an EPIC-based electronic medical record. We present this partnership, the infrastructure, genomic counseling approach, and the challenges that arose in the design and conduct of this ongoing trial to inform subsequent collaborative efforts and best genomic counseling practices.
PMCID: PMC4051230  PMID: 24926413
implementation; genomics; medicine; randomized; patients; counseling; actionable; risk perception; pharmacogenomics
21.  Design and Implementation of a Randomized Controlled Trial of Genomic Counseling for Patients with Chronic Disease  
We describe the development and implementation of a randomized controlled trial to investigate the impact of genomic counseling on a cohort of patients with heart failure (HF) or hypertension (HTN), managed at a large academic medical center, the Ohio State University Wexner Medical Center (OSUWMC). Our study is built upon the existing Coriell Personalized Medicine Collaborative (CPMC®). OSUWMC patient participants with chronic disease (CD) receive eight actionable complex disease and one pharmacogenomic test report through the CPMC® web portal. Participants are randomized to either the in-person post-test genomic counseling—active arm, versus web-based only return of results—control arm. Study-specific surveys measure: (1) change in risk perception; (2) knowledge retention; (3) perceived personal control; (4) health behavior change; and, for the active arm (5), overall satisfaction with genomic counseling. This ongoing partnership has spurred creation of both infrastructure and procedures necessary for the implementation of genomics and genomic counseling in clinical care and clinical research. This included creation of a comprehensive informed consent document and processes for prospective return of actionable results for multiple complex diseases and pharmacogenomics (PGx) through a web portal, and integration of genomic data files and clinical decision support into an EPIC-based electronic medical record. We present this partnership, the infrastructure, genomic counseling approach, and the challenges that arose in the design and conduct of this ongoing trial to inform subsequent collaborative efforts and best genomic counseling practices.
PMCID: PMC4051230  PMID: 24926413
implementation; genomics; medicine; randomized; patients; counseling; actionable; risk perception; pharmacogenomics
22.  Pharmacogenomics of chemotherapeutic susceptibility and toxicity 
Genome Medicine  2012;4(11):90.
The goal of personalized medicine is to tailor a patient's treatment strategy on the basis of his or her unique genetic make-up. The field of oncology is beginning to incorporate many of the strategies of personalized medicine, especially within the realm of pharmacogenomics, which is the study of how inter-individual genetic variation determines drug response or toxicity. A main objective of pharmacogenomics is to facilitate physician decision-making regarding optimal drug selection, dose and treatment duration on a patient-by-patient basis. Recent advances in genome-wide genotyping and sequencing technologies have supported the discoveries of a number of pharmacogenetic markers that predict response to chemotherapy. However, effectively implementing these pharmacogenetic markers in the clinic remains a major challenge. This review focuses on the contribution of germline genetic variation to chemotherapeutic toxicity and response, and discusses the utility of genome-wide association studies and use of lymphoblastoid cell lines (LCLs) in pharmacogenomic studies. Furthermore, we highlight several recent examples of genetic variants associated with chemotherapeutic toxicity or response in both patient cohorts and LCLs, and discuss the challenges and future directions of pharmacogenomic discovery for cancer treatment.
PMCID: PMC3580423  PMID: 23199206
Pharmacogenomics; chemotherapeutics; genome-wide association studies; International HapMap Project; clinical translation
23.  A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks 
Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.
PMCID: PMC3070480  PMID: 21483730
systems biology; PBPK; software; multiscale; modeling; simulation; oncology; signal transduction
24.  Healthcare provider attitudes towards the problem list in an electronic health record: a mixed-methods qualitative study 
The problem list is a key part of the electronic health record (EHR) that allows practitioners to see a patient’s diagnoses and health issues. Yet, as the content of the problem list largely represents the subjective decisions of those who edit it, patients’ problem lists are often unreliable when shared across practitioners. The lack of standards for how the problem list is compiled in the EHR limits its effectiveness in improving patient care, particularly as a resource for clinical decision support and population management tools. The purpose of this study is to discover practitioner opinions towards the problem list and the logic behind their decisions during clinical situations.
Materials and methods
An observational cross-sectional study was conducted at two major Boston teaching hospitals. Practitioners’ opinions about the problem list were collected through both in-person interviews and an online questionnaire. Questions were framed using vignettes of clinical scenarios asking practitioners about their preferred actions towards the problem list.
These data confirmed prior research that practitioners differ in their opinions over managing the problem list, but in most responses to a questionnaire, there was a common approach among the relative majority of respondents. Further, basic demographic characteristics of providers (age, medical experience, etc.) did not appear to strongly affect attitudes towards the problem list.
The results supported the premise that policies and EHR tools are needed to bring about a common approach. Further, the findings helped identify what issues might benefit the most from a defined policy and the level of restriction a problem list policy should place on the addition of different types of information.
PMCID: PMC3534408  PMID: 23140312
Problem list; Problems; Electronic health record; Standardization; Provider attitudes; EHR
25.  Developing a Prototype System for Integrating Pharmacogenomics Findings into Clinical Practice 
Journal of personalized medicine  2012;2(4):241-256.
Findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy to improve efficacy and reduce adverse drug events. Researchers have identified factors influencing uptake of genomics in medicine, but little is known about the specific technical barriers to incorporating PGx into existing clinical frameworks. We present the design and development of a prototype PGx clinical decision support (CDS) system that builds on existing clinical infrastructure and incorporates semi-active and active CDS. Informing this work, we updated previous evaluations of PGx knowledge characteristics, and of how the CDS capabilities of three local clinical systems align with data and functional requirements for PGx CDS. We summarize characteristics of PGx knowledge and technical needs for implementing PGx CDS within existing clinical frameworks. PGx decision support rules derived from FDA drug labels primarily involve drug metabolizing genes, vary in maturity, and the majority support the post-analytic phase of genetic testing. Computerized provider order entry capabilities are key functional requirements for PGx CDS and were best supported by one of the three systems we evaluated. We identified two technical needs when building on this system, the need for (1) new or existing standards for data exchange to connect clinical data to PGx knowledge, and (2) a method for implementing semi-active CDS. Our analyses enhance our understanding of principles for designing and implementing CDS for drug therapy individualization and our current understanding of PGx characteristics in a clinical context. Characteristics of PGx knowledge and capabilities of current clinical systems can help govern decisions about CDS implementation, and can help guide decisions made by groups that develop and maintain knowledge resources such that delivery of content for clinical care is supported.
PMCID: PMC3670105  PMID: 23741623
electronic health records; clinical decision support systems; pharmacogenomics; personalized medicine; computerized provider order entry; knowledge representation

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