Omics‐Based Systems Vaccinology for Vaccine Target Identification

Preclinical Research Omics technologies include genomics, transcriptomics, proteomics, metabolomics, and immunomics. These technologies have been used in vaccine research, which can be summarized using the term “vaccinomics.” These omics technologies combined with advanced bioinformatics analysis form the core of “systems vaccinology.” Omics technologies provide powerful methods in vaccine target identification. The genomics‐based reverse vaccinology starts with predicting vaccine protein candidates through in silico bioinformatics analysis of genome sequences. The VIOLIN V axign vaccine design program ( http://www.violinet.org/vaxign ) is the first web‐based vaccine target prediction software based on the reverse vaccinology strategy. Systematic transcriptomics and proteomics analyses facilitate rational vaccine target identification by detesting genome‐wide gene expression profiles. Immunomics is the study of the set of antigens recognized by host immune systems and has also been used for efficient vaccine target prediction. With the large amount of omics data available, it is necessary to integrate various vaccine data using ontologies, including the G ene O ntology ( GO ) and V accine O ntology ( VO ), for more efficient vaccine target prediction and assessment. All these omics technologies combined with advanced bioinformatics analysis methods for a systems biology‐based vaccine target prediction strategy. This article reviews the various omics technologies and how they can be used in vaccine target identification.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94576/1/ddr21049.pd

Ancestry in translational genomic medicine: handle with care

Disparities in health outcomes of members of different ancestral or ethnic groups can be observed in both developed and developing countries and continue to be a global concern. Genomic medicine can help toward closing this gap by expanding the knowledge on novel alleles related to disease risk and drug response, their frequencies, and their relation with disease and drug-response phenotypes, in as many countries and ethnic groups as possible. Without such knowledge, genomic medicine cannot deliver upon its promise of contributing to health for all. However, the use of ancestry or ethnicity-related genetic information as a selection criterion for assigning varying levels of access to health care is condemnable. Translational genomic medicine will allow for individualized clinical decision making - doing away with the use of race, ethnicity or ancestry as a proxy

Protocol for serious fall injury adjudication in the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study

Background: This paper describes a protocol for determining the incidence of serious fall injuries for Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE), a large, multicenter pragmatic clinical trial with limited resources for event adjudication. We describe how administrative data (from participating health systems and Medicare claims) can be used to confirm participant-reported events, with more time- and resource-intensive full-text medical record data used only on an as-needed basis. Methods: STRIDE is a pragmatic cluster-randomized controlled trial involving 5451 participants age \u3e /= 70 and at increased risk for falls, served by 86 primary care practices in 10 US health systems. The STRIDE intervention involves a nurse falls care manager who assesses a participant\u27s underlying risks for falls, suggests interventions using motivational interviewing, and then creates, implements and longitudinally follows up on an individualized care plan with the participant (and caregiver when appropriate), in partnership with the participant\u27s primary care provider. STRIDE\u27s primary outcome is serious fall injuries, defined as a fall resulting in: (1) medical attention billable according to Medicare guidelines with a) fracture (excluding isolated thoracic vertebral and/or lumbar vertebral fracture), b) joint dislocation, or c) cut requiring closure; OR (2) overnight hospitalization with a) head injury, b) sprain or strain, c) bruising or swelling, or d) other injury determined to be serious (i.e., burn, rhabdomyolysis, or internal injury). Two sources of data are required to confirm a serious fall injury. The primary data source is the participant\u27s self-report of a fall leading to medical attention, identified during telephone interview every 4 months, with the confirmatory source being (1) administrative data capturing encounters at the participating health systems or Medicare claims and/or (2) the full text of medical records requested only as needed. Discussion: Adjudication is ongoing, with over 1000 potentially qualifying events adjudicated to date. Administrative data can be successfully used for adjudication, as part of a hybrid approach that retrieves full-text medical records only when needed. With the continued refinement and availability of administrative data sources, future studies may be able to use administrative data completely in lieu of medical record review to maximize the quality of adjudication with finite resources. Trial registration: ClinicalTrials.gov (NCT02475850)

Device Therapies Among Patients Receiving Primary Prevention Implantable Cardioverter-Defibrillators in the Cardiovascular Research Network

BACKGROUND: Primary prevention implantable cardioverter-defibrillators (ICDs) reduce mortality in selected patients with left ventricular systolic dysfunction by delivering therapies (antitachycardia pacing or shocks) to terminate potentially lethal arrhythmias; inappropriate therapies also occur. We assessed device therapies among adults receiving primary prevention ICDs in 7 healthcare systems. METHODS AND RESULTS: We linked medical record data, adjudicated device therapies, and the National Cardiovascular Data Registry ICD Registry. Survival analysis evaluated therapy probability and predictors after ICD implant from 2006 to 2009, with attention to Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups: left ventricular ejection fraction, 31% to 35%; nonischemic cardiomyopathy \u3c 9 months\u27 duration; and New York Heart Association class IV heart failure with cardiac resynchronization therapy defibrillator. Among 2540 patients, 35% were \u3c 65 years old, 26% were women, and 59% were white. During 27 (median) months, 738 (29%) received \u3e /=1 therapy. Three-year therapy risk was 36% (appropriate, 24%; inappropriate, 12%). Appropriate therapy was more common in men (adjusted hazard ratio [HR], 1.84; 95% confidence interval [CI], 1.43-2.35). Inappropriate therapy was more common in patients with atrial fibrillation (adjusted HR, 2.20; 95% CI, 1.68-2.87), but less common among patients \u3e /=65 years old versus younger (adjusted HR, 0.72; 95% CI, 0.54-0.95) and in recent implants (eg, in 2009 versus 2006; adjusted HR, 0.66; 95% CI, 0.46-0.95). In Centers for Medicare and Medicaid Services Coverage With Evidence Development analysis, inappropriate therapy was less common with cardiac resynchronization therapy defibrillator versus single chamber (adjusted HR, 0.55; 95% CI, 0.36-0.84); therapy risk did not otherwise differ for Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups. CONCLUSIONS: In this community cohort of primary prevention patients receiving ICD, therapy delivery varied across demographic and clinical characteristics, but did not differ meaningfully for Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups

Grant Application Review: The Case of Transparency

The Legitimacy of Peer Reviewing Transparency Policies at Funding Agencies Incremental Perspective: How to Improve Effectiveness and Robustness Further through Transparency The Radical Perspective: Transformative Potential of Transparency Acknowledgments Reference

Atrial fibrillation and outcomes in heart failure with preserved versus reduced left ventricular ejection fraction

BACKGROUND: Atrial fibrillation (AF) and heart failure (HF) are 2 of the most common cardiovascular conditions nationally and AF frequently complicates HF. We examined how AF has impacts on adverse outcomes in HF-PEF versus HF-REF within a large, contemporary cohort. METHODS AND RESULTS: We identified all adults diagnosed with HF-PEF or HF-REF based on hospital discharge and ambulatory visit diagnoses and relevant imaging results for 2005-2008 from 4 health plans in the Cardiovascular Research Network. Data on demographic features, diagnoses, procedures, outpatient pharmacy use, and laboratory results were ascertained from health plan databases. Hospitalizations for HF, stroke, and any reason were identified from hospital discharge and billing claims databases. Deaths were ascertained from health plan and state death files. Among 23 644 patients with HF, 11 429 (48.3%) had documented AF (9081 preexisting, 2348 incident). Compared with patients who did not have AF, patients with AF had higher adjusted rates of ischemic stroke (hazard ratio [HR] 2.47 for incident AF; HR 1.57 for preexisting AF), hospitalization for HF (HR 2.00 for incident AF; HR 1.22 for preexisting AF), all-cause hospitalization (HR 1.45 for incident AF; HR 1.15 for preexisting AF), and death (incident AF HR 1.67; preexisting AF HR 1.13). The associations of AF with these outcomes were similar for HF-PEF and HF-REF, with the exception of ischemic stroke. CONCLUSIONS: AF is a potent risk factor for adverse outcomes in patients with HF-PEF or HF-REF. Effective interventions are needed to improve the prognosis of these high-risk patients

Educating the Next Generation of Pharmacogenomics Experts: Global Educational Needs and Concepts

Personalised Therapeutic

Patterns of Complex Comorbidity in Older Patients with Heart Failure

Background Heart failure (HF) carries a high burden of comorbidity with approximately one half of patients with HF having at least one additional comorbid condition present. Rates of comorbidity in patients with HF have steadily increased over the past 2 decades. Objective To examine patterns of comorbidity among older patients with HF in the Cardiovascular Research Network PRESERVE cohort. Methods PRESERVE Cohort Data are from the CVRN PRESERVE cohort which is a multicenter cohort of 37,054 patients [mean age = 74 years (SD = 12.4 yrs); 46% female] with HF diagnosed between 2005 and 2008 currently being conducted at 4 CVRN sites: KPNC, KPCO, KPNW, and FCHP. The primary data source for the PRESERVE cohort was the HMO Research Network Virtual Data Warehouse. Identification of Coexisting Diseases Coexisiting illnesses at the time of HF diagnosis were based on diagnoses and procedures mapped to relevant International Classification of Diseases, Ninth Edition (ICD-9) codes. For the purposes of characterizing clusters of comorbidities, we focused on coexisting conditions with a prevalence rate of ≥3%. Statistical Analysis We used the Agglomerative Clustering technique to characterize patterns of comorbidity. Over multiple iterations, each condition is clustered with the condition with which it has the highest squared correlation. This process is repeated to determine whether assigning a condition to a different cluster increases the amount of explained variance [ranging from 1.0 (all variance explained) to 0.0 (no variance explained)]. The conditions in each cluster are as correlated as possible among themselves and as uncorrelated as possible with conditions in other clusters. Results Burden of Comorbidity There was a high degree of comorbidity and multi-morbidity among patients with HF. (Table 1) Hypertension and arrhythmias were the comorbidities of HF that occurred most often in the absence of other chronic conditions (4.8% and 4.7%, respectively). The average number of comorbid conditions varied from 3.5 to 5.2. Patients with HF and unstable angina or other thromboembolic disorders had the highest multi-morbidity (mean = 5.2 conditions), whereas those with HF and hypertension had the lowest (mean = 3.5). Clustering of Comorbiditites A five-cluster structure was derived. Cluster 1: Dyslipidemia, Hypertension, Diabetes Mellitus, Visual Impairment Cluster 2: Acute Myocardial Infarction, Unstable Angina, Thromboembolic Disorder, Dementia Cluster 3: Aortic Valvular Disease, Cancer, Hearing Impairment, Arrthythmia Cluster 4: Peripheral Arterial Disease, Stroke Cluster 5: Lung Disease, Liver Disease, Depression Discussion and Conclusions Cluster analysis is an innovative approach to examining the co-occurrence of diseases and allows for identification of broad patterns of multi-morbidity beyond the pairings of diseases or disease counts. Patients with HF have a high rate of multi-morbidity, with an average of 4 co-occurring conditions. Intuitive and unintuitive patterns of clustering were identified. Randomized clinical trials in HF will need to include more diverse patient populations in order to adapt to the increasingly complex patient population. A cluster analysis approach to characterizing patterns of comorbidity may help indentify important patient subgroups

Hearing loss and cognitive decline among older adults with atrial fibrillation: the SAGE-AF study

Objective: To examine the association between hearing loss and cognitive function cross-sectionally and prospectively among older adults with atrial fibrillation (AF). Methods: Patients with AF \u3e /= 65-year-old (n = 1244) in the SAGE (Systematic Assessment of Geriatric Elements)-AF study were recruited from five internal medicine or cardiology clinics in Massachusetts and Georgia. Hearing was assessed by a structured questionnaire at baseline. Cognitive function was assessed by Montreal Cognitive Assessment (MoCA) at baseline and one year. Cognitive impairment was defined as score \u3c /= 23 on the MoCA. The associations between hearing loss and cognitive function were examined by multivariable adjusted logistic regression. Results: Participants with hearing loss (n = 451, 36%) were older, more likely to be male, and have depressive symptoms than patients without hearing loss. At baseline, 528 (42%) participants were cognitively impaired. Individuals with hearing loss were significantly more likely to have cognitive impairment at baseline [adjusted odds ratio (OR) = 1.37, 95% confidence interval (CI): 1.05-1.81]. Among the 662 participants who did not have cognitive impairment at baseline and attended the one-year follow-up visit, 106 (16%) developed incident cognitive impairment. Individuals with, versus those without, hearing loss were significantly more likely to develop incident cognitive impairment at one year (adjusted OR = 1.68, 95% CI: 1.07-2.64). Conclusions: Hearing loss is a prevalent but under-recognized factor associated with cognitive impairment in patients with AF. Assessment for hearing loss may be indicated among these patients to identify individuals at high-risk for adverse outcomes

Magnitude and Characteristics of Patients Who Survived an Acute Myocardial Infarction

BACKGROUND: The purpose of this study was to describe the magnitude and characteristics of patients who did not experience any significant major adverse cardiovascular event early (within 6 weeks) and late (during the first year) after hospital discharge for an acute myocardial infarction (AMI). METHODS AND RESULTS: Data from 12 243 patients discharged after an AMI from 233 sites across the United States in the TRANSLATE-ACS (Treatment With ADP Receptor Inhibitors: Longitudinal Assessment of Treatment Patterns and Events After Acute Coronary Syndrome) study were analyzed. Multivariable adjusted regression analyses modeled factors associated with 6-week and 1-year survivors who did not experience a recurrent AMI, stroke, unplanned coronary revascularization, or rehospitalization for unstable angina/chest pain during these time periods. The average age of this study population was 60.0 years, 72.0% were men, and 87.9% were white. In this population, 92.4% were classified as early low-risk survivors and 76.3% were classified as late low-risk survivors of an AMI. Factors associated with being an early and late postdischarge survivor included being male and having single-vessel coronary artery disease at the patient\u27s index hospitalization. Patients who were not first seen with any chronic health condition, had an index hospital stay of \u3c /=3 days, and had high baseline quality-of-life scores were more likely to be late low-risk survivors. CONCLUSIONS: Identifying low-risk survivors of an AMI may permit healthcare providers to focus more intensive efforts and interventions on those at higher risk of experiencing adverse cardiovascular events during the postdischarge transition period. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01088503