A community approach to mortality prediction in sepsis via gene expression analysis

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

Making genomic medicine evidence-based and patient-centered: a structured review and landscape analysis of comparative effectiveness research

Comparative effectiveness research (CER) in genomic medicine (GM) measures the clinical utility of using genomic information to guide clinical care in comparison to appropriate alternatives. We summarized findings of high-quality systematic reviews that compared the analytic and clinical validity and clinical utility of GM tests. We focused on clinical utility findings to summarize CER-derived evidence about GM and identify evidence gaps and future research needs. We abstracted key elements of study design, GM interventions, results, and study quality ratings from 21 systematic reviews published in 2010 through 2015. More than half (N = 13) of the reviews were of cancer-related tests. All reviews identified potentially important clinical applications of the GM interventions, but most had significant methodological weaknesses that largely precluded any conclusions about clinical utility. Twelve reviews discussed the importance of patient-centered outcomes, although few described evidence about the impact of genomic medicine on these outcomes. In summary, we found a very limited body of evidence about the effect of using genomic tests on health outcomes and many evidence gaps for CER to address.Genet Med advance online publication 13 April 2017.

Damschroder L, Cooper-DeHoff RM, Denny JC, Ginsburg GS, Guan Y, Horowitz CR, Levy KD, Levy MA, Madden EB, Matheny ME, Pollin TI, Pratt VM, Rosenman M, Voils CI, W. Weitzel K, Wilke RA, Ryanne Wu R, Orlando LA. Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network

BACKGROUND

To realize potential public health benefits from genetic and genomic innovations, understanding how best to implement the innovations into clinical care is important. The objective of this study was to synthesize data on challenges identified by six diverse projects that are part of a National Human Genome Research Institute (NHGRI)-funded network focused on implementing genomics into practice and strategies to overcome these challenges.

METHODS

We used a multiple-case study approach with each project considered as a case and qualitative methods to elicit and describe themes related to implementation challenges and strategies. We describe challenges and strategies in an implementation framework and typology to enable consistent definitions and cross-case comparisons. Strategies were linked to challenges based on expert review and shared themes.

RESULTS

Three challenges were identified by all six projects, and strategies to address these challenges varied across the projects. One common challenge was to increase the relative priority of integrating genomics within the health system electronic health record (EHR). Four projects used data warehousing techniques to accomplish the integration. The second common challenge was to strengthen clinicians' knowledge and beliefs about genomic medicine. To overcome this challenge, all projects developed educational materials and conducted meetings and outreach focused on genomic education for clinicians. The third challenge was engaging patients in the genomic medicine projects. Strategies to overcome this challenge included use of mass media to spread the word, actively involving patients in implementation (e.g., a patient advisory board), and preparing patients to be active participants in their healthcare decisions.

CONCLUSIONS

This is the first collaborative evaluation focusing on the description of genomic medicine innovations implemented in multiple real-world clinical settings. Findings suggest that strategies to facilitate integration of genomic data within existing EHRs and educate stakeholders about the value of genomic services are considered important for effective implementation. Future work could build on these findings to evaluate which strategies are optimal under what conditions. This information will be useful for guiding translation of discoveries to clinical care, which, in turn, can provide data to inform continual improvement of genomic innovations and their applications.

Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection

Infection of respiratory mucosa with viral pathogens triggers complex immunologic events in the affected host. We sought to characterize this response through proteomic analysis of nasopharyngeal lavage in human subjects experimentally challenged with influenza A/H3N2 or human rhinovirus, and to develop targeted assays measuring peptides involved in this host response allowing classification of acute respiratory virus infection. Unbiased proteomic discovery analysis identified 3285 peptides corresponding to 438 unique proteins, and revealed that infection with H3N2 induces significant alterations in protein expression. These include proteins involved in acute inflammatory response, innate immune response, and the complement cascade. These data provide insights into the nature of the biological response to viral infection of the upper respiratory tract, and the proteins that are dysregulated by viral infection form the basis of signature that accurately classifies the infected state. Verification of this signature using targeted mass spectrometry in independent cohorts of subjects challenged with influenza or rhinovirus demonstrates that it performs with high accuracy (0.8623 AUROC, 75% TPR, 97.46% TNR). With further development as a clinical diagnostic, this signature may have utility in rapid screening for emerging infections, avoidance of inappropriate antibacterial therapy, and more rapid implementation of appropriate therapeutic and public health strategies.

Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis

Sepsis is a deleterious inflammatory response to infection with high mortality. Reliable sepsis biomarkers could improve diagnosis, prognosis, and treatment. Integration of human genetics, patient metabolite and cytokine measurements, and testing in a mouse model demonstrate that the methionine salvage pathway is a regulator of sepsis that can accurately predict prognosis in patients. Pathway-based genome-wide association analysis of nontyphoidal Salmonella bacteremia showed a strong enrichment for single-nucleotide polymorphisms near the components of the methionine salvage pathway. Measurement of the pathway's substrate, methylthioadenosine (MTA), in two cohorts of sepsis patients demonstrated increased plasma MTA in nonsurvivors. Plasma MTA was correlated with levels of inflammatory cytokines, indicating that elevated MTA marks a subset of patients with excessive inflammation. A machine-learning model combining MTA and other variables yielded approximately 80% accuracy (area under the curve) in predicting death. Furthermore, mice infected with Salmonella had prolonged survival when MTA was administered before infection, suggesting that manipulating MTA levels could regulate the severity of the inflammatory response. Our results demonstrate how combining genetic data, biomolecule measurements, and animal models can shape our understanding of disease and lead to new biomarkers for patient stratification and potential therapeutic targeting.

Primary care providers' use of pharmacist support for delivery of pharmacogenetic testing

AIM

To investigate provider utilization of pharmacist support in the delivery of pharmacogenetic testing in a primary care setting.

METHODS

Two primary care clinics within Duke University Health System participated in the study between December 2012 and July 2013. One clinic was provided with an in-house pharmacist and the second clinic had an on-call pharmacist.

RESULTS

Providers in the in-house pharmacist arm consulted with the pharmacist for 13 of 15 cases, or about one of every four patients tested compared with one of every 7.5 patients in the on-call pharmacist arm. A total of 63 tests were ordered, 48 by providers in the pharmacist-in-house arm.

CONCLUSION

These findings suggest that the availability of an in-house pharmacist increases the likelihood of pharmacogenetic test utilization.

The effective rate of influenza reassortment is limited during human infection

We characterise the evolutionary dynamics of influenza infection described by viral sequence data collected from two challenge studies conducted in human hosts. Viral sequence data were collected at regular intervals from infected hosts. Changes in the sequence data observed across time show that the within-host evolution of the virus was driven by the reversion of variants acquired during previous passaging of the virus. Treatment of some patients with oseltamivir on the first day of infection did not lead to the emergence of drug resistance variants in patients. Using an evolutionary model, we inferred the effective rate of reassortment between viral segments, measuring the extent to which randomly chosen viruses within the host exchange genetic material. We find strong evidence that the rate of effective reassortment is low, such that genetic associations between polymorphic loci in different segments are preserved during the course of an infection in a manner not compatible with epistasis. Combining our evidence with that of previous studies we suggest that spatial heterogeneity in the viral population may reduce the extent to which reassortment is observed. Our results do not contradict previous findings of high rates of viral reassortment in vitro and in small animal studies, but indicate that in human hosts the effective rate of reassortment may be substantially more limited.

The influenza virus is an important cause of disease in the human population. During the course of an infection the virus can evolve rapidly. An important mechanism of viral evolution is reassortment, whereby different segments of the influenza genome are shuffled with other segments, producing new viral combinations. Here we study natural selection and reassortment during the course of infections occurring in human hosts. Examining viral genome sequence data from these infections, we note that genetic variants that were acquired during the growth of viruses in culture are selected against in the human host. In addition, we find evidence that the effective rate of reassortment is low. We suggest that the spatial separation between viruses in different parts of the host airway may limit the extent to which genetically distinct segments reassort with one another. Within the global population of influenza viruses, reassortment remains an important factor. However, reassortment is not so rapid as to exclude the possibility of interactions between genome segments affecting the course of influenza evolution during a single infection.

An age- and sex-specific gene expression score is associated with revascularization and coronary artery disease: Insights from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial

BACKGROUND

Identifying predictors of coronary artery disease (CAD)-related procedures and events remains a priority.

METHODS

We measured an age- and sex-specific gene expression score (ASGES) previously validated to detect obstructive CAD (oCAD) in symptomatic nondiabetic patients in the PROMISE trial. The outcomes were oCAD (≥70% stenosis in ≥1 vessel or ≥50% left main stenosis on CT angiography [CTA]) and a composite endpoint of death, myocardial infarction, revascularization, or unstable angina.

RESULTS

The ASGES was determined in 2370 nondiabetic participants (47.5% male, median age 59.5 years, median follow-up 25 months), including 1137 with CTA data. An ASGES >15 was associated with oCAD (odds ratio 2.5 [95% CI 1.6-3.8], P<.001) and the composite endpoint (hazard ratio [HR] 2.6 [95% CI 1.8-3.9], P<.001) in unadjusted analyses. After adjustment for Framingham risk, an ASGES >15 remained associated with the composite endpoint (P=.02); the only component that was associated was revascularization (adjusted HR 2.69 [95% CI 1.52-4.79], P<.001). Compared to noninvasive testing, the ASGES improved prediction for the composite (increase in c-statistic=0.036; continuous net reclassification index=43.2%). Patients with an ASGES ≤15 had a composite endpoint rate no different from those with negative noninvasive test results (3.2% vs. 2.6%, P=.29).

CONCLUSIONS

A blood-based genomic test for detecting oCAD significantly predicts near-term revascularization procedures, but not non-revascularization events. Larger studies will be needed to clarify the risk with non-revascularization events.

Impact of Genetic Testing and Family Health History Based Risk Counseling on Behavior Change and Cognitive Precursors for Type 2 Diabetes

Family health history (FHH) in the context of risk assessment has been shown to positively impact risk perception and behavior change. The added value of genetic risk testing is less certain. The aim of this study was to determine the impact of Type 2 Diabetes (T2D) FHH and genetic risk counseling on behavior and its cognitive precursors. Subjects were non-diabetic patients randomized to counseling that included FHH +/- T2D genetic testing. Measurements included weight, BMI, fasting glucose at baseline and 12 months and behavioral and cognitive precursor (T2D risk perception and control over disease development) surveys at baseline, 3, and 12 months. 391 subjects enrolled of which 312 completed the study. Behavioral and clinical outcomes did not differ across FHH or genetic risk but cognitive precursors did. Higher FHH risk was associated with a stronger perceived T2D risk (p_Kendall < 0.001) and with a perception of "serious" risk (p_Kendall < 0.001). Genetic risk did not influence risk perception, but was correlated with an increase in perception of "serious" risk for moderate (p_Kendall = 0.04) and average FHH risk subjects (p_Kendall = 0.01), though not for the high FHH risk group. Perceived control over T2D risk was high and not affected by FHH or genetic risk. FHH appears to have a strong impact on cognitive precursors of behavior change, suggesting it could be leveraged to enhance risk counseling, particularly when lifestyle change is desirable. Genetic risk was able to alter perceptions about the seriousness of T2D risk in those with moderate and average FHH risk, suggesting that FHH could be used to selectively identify individuals who may benefit from genetic risk testing.

Patient experiences with pharmacogenetic testing in a primary care setting

AIM

To investigate patient experiences with pharmacogenetic (PGx) testing.

METHODS

Patients were offered PGx testing through a study on pharmacist-assisted delivery of PGx testing and invited to complete pre- and post-testing surveys about their experience.

RESULTS

Of 63 patients tested, 17 completed the baseline survey (27%). Interest in testing was mostly impacted by desire to inform selection of best treatment (n = 13). Seven of 12 patients that completed the follow-up survey indicated that their provider discussed the test result with them. Five patients understood their test result very or somewhat well. All would be likely to have PGx testing again.

CONCLUSION

Patients perceived PGx testing to be useful, though more effort may be needed to improve patient-provider communication of test results.

Opportunities for the Cardiovascular Community in the Precision Medicine Initiative

The Precision Medicine Initiative recently announced by President Barack Obama seeks to move the field of precision medicine more rapidly into clinical care. Precision medicine revolves around the concept of integrating individual-level data including genomics, biomarkers, lifestyle and other environmental factors, wearable device physiological data, and information from electronic health records to ultimately provide better clinical care to individual patients. The Precision Medicine Initiative as currently structured will primarily fund efforts in cancer genomics with longer-term goals of advancing precision medicine to all areas of health, and will be supported through creation of a 1 million person cohort study across the United States. This focused effort on precision medicine provides scientists, clinicians, and patients within the cardiovascular community an opportunity to work together boldly to advance clinical care; the community needs to be aware and engaged in the process as it progresses. This article provides a framework for potential involvement of the cardiovascular community in the Precision Medicine Initiative, while highlighting significant challenges for its successful implementation.

An individualized predictor of health and disease using paired reference and target samples

Background

Consider the problem of designing a panel of complex biomarkers to predict a patient’s health or disease state when one can pair his or her current test sample, called a target sample, with the patient’s previously acquired healthy sample, called a reference sample. As contrasted to a population averaged reference this reference sample is individualized. Automated predictor algorithms that compare and contrast the paired samples to each other could result in a new generation of test panels that compare to a person’s healthy reference to enhance predictive accuracy. This paper develops such an individualized predictor and illustrates the added value of including the healthy reference for design of predictive gene expression panels.

Results

The objective is to predict each subject’s state of infection, e.g., neither exposed nor infected, exposed but not infected, pre-acute phase of infection, acute phase of infection, post-acute phase of infection. Using gene microarray data collected in a large scale serially sampled respiratory virus challenge study we quantify the diagnostic advantage of pairing a person’s baseline reference with his or her target sample. The full study consists of 2886 microarray chips assaying 12,023 genes of 151 human volunteer subjects under 4 different inoculation regimes (HRV, RSV, H1N1, H3N2). We train (with cross-validation) reference-aided sparse multi-class classifier algorithms on this data to show that inclusion of a subject’s reference sample can improve prediction accuracy by as much as 14 %, for the H3N2 cohort, and by at least 6 %, for the H1N1 cohort. Remarkably, these gains in accuracy are achieved by using smaller panels of genes, e.g., 39 % fewer for H3N2 and 31 % fewer for H1N1. The biomarkers selected by the predictors fall into two categories: 1) contrasting genes that tend to differentially express between target and reference samples over the population; 2) reinforcement genes that remain constant over the two samples, which function as housekeeping normalization genes. Many of these genes are common to all 4 viruses and their roles in the predictor elucidate the function that they play in differentiating the different states of host immune response.

Conclusions

If one uses a suitable mathematical prediction algorithm, inclusion of a healthy reference in biomarker diagnostic testing can potentially improve accuracy of disease prediction with fewer biomarkers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0889-9) contains supplementary material, which is available to authorized users.

Host gene expression classifiers diagnose acute respiratory illness etiology

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.

A Genomic Signature of Influenza Infection Shows Potential for Presymptomatic Detection, Guiding Early Therapy, and Monitoring Clinical Responses

Early, presymptomatic intervention with oseltamivir (corresponding to the onset of a published host-based genomic signature of influenza infection) resulted in decreased overall influenza symptoms (aggregate symptom scores of 23.5 vs 46.3), more rapid resolution of clinical disease (20 hours earlier), reduced viral shedding (total median tissue culture infectious dose [TCID₅₀] 7.4 vs 9.7), and significantly reduced expression of several inflammatory cytokines (interferon-γ, tumor necrosis factor-α, interleukin-6, and others). The host genomic response to influenza infection is robust and may provide the means for early detection, more timely therapeutic interventions, a meaningful reduction in clinical disease, and an effective molecular means to track response to therapy.

SLCO1B1 genetic variants, long-term low-density lipoprotein cholesterol levels and clinical events in patients following cardiac catheterization

AIM

SLCO1B1 variants are associated with intermediate outcomes that may increase risk of death/myocardial infarction (MI) in statin-treated patients.

PATIENTS & METHODS

In high-risk Caucasians undergoing cardiac catheterization, we tested the association between rs4149056/625T>C and rs2306283/492A>G with low-density lipoprotein cholesterol (LDL-c) over 3 years (n = 1402) and death/MI over 6 years (n = 2994), accounting for statin use or type during follow-up.

RESULTS

Carriers of the rs4149056 C allele had 6.2 ± 1.7 mg/dl higher LDL-c per C allele (p < 0.001) but were not at higher risk for death/MI (p = 0.9). We found no associations between rs2306283 and LDL-c or death/MI (p > 0.6).

CONCLUSION

Functional SLCO1B1 variants are not associated with death/MI in patients commonly treated with statins, despite higher LDL-c in carriers of the rs4149056 C allele.

Transcriptomic Analysis of the Host Response and Innate Resilience to Enterotoxigenic Escherichia coli Infection in Humans

BACKGROUND

Enterotoxigenic Escherichia coli (ETEC) is a globally prevalent cause of diarrhea. Though usually self-limited, it can be severe and debilitating. Little is known about the host transcriptional response to infection. We report the first gene expression analysis of the human host response to experimental challenge with ETEC.

METHODS

We challenged 30 healthy adults with an unattenuated ETEC strain, and collected serial blood samples shortly after inoculation and daily for 8 days. We performed gene expression analysis on whole peripheral blood RNA samples from subjects in whom severe symptoms developed (n = 6) and a subset of those who remained asymptomatic (n = 6) despite shedding.

RESULTS

Compared with baseline, symptomatic subjects demonstrated significantly different expression of 406 genes highlighting increased immune response and decreased protein synthesis. Compared with asymptomatic subjects, symptomatic subjects differentially expressed 254 genes primarily associated with immune response. This comparison also revealed 29 genes differentially expressed between groups at baseline, suggesting innate resilience to infection. Drug repositioning analysis identified several drug classes with potential utility in augmenting immune response or mitigating symptoms.

CONCLUSIONS

There are statistically significant and biologically plausible differences in host gene expression induced by ETEC infection. Differential baseline expression of some genes may indicate resilience to infection.

The IGNITE network: a model for genomic medicine implementation and research

BACKGROUND

Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility.

METHODS

To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org ) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches.

RESULTS

This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years.

CONCLUSIONS

The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these interventions. Through these efforts and collaboration with other stakeholders, IGNITE is poised to have a significant impact on the acceleration of genomic information into medical practice.

Differential evolution of peripheral cytokine levels in symptomatic and asymptomatic responses to experimental influenza virus challenge

Exposure to influenza virus triggers a complex cascade of events in the human host. In order to understand more clearly the evolution of this intricate response over time, human volunteers were inoculated with influenza A/Wisconsin/67/2005 (H3N2), and then had serial peripheral blood samples drawn and tested for the presence of 25 major human cytokines. Nine of 17 (53%) inoculated subjects developed symptomatic influenza infection. Individuals who will go on to become symptomatic demonstrate increased circulating levels of interleukin (IL)-6, IL-8, IL-15, monocyte chemotactic protein (MCP)-1 and interferon (IFN) gamma-induced protein (IP)-10 as early as 12-29 h post-inoculation (during the presymptomatic phase), whereas challenged patients who remain asymptomatic do not. Overall, the immunological pathways of leucocyte recruitment, Toll-like receptor (TLR)-signalling, innate anti-viral immunity and fever production are all over-represented in symptomatic individuals very early in disease, but are also dynamic and evolve continuously over time. Comparison with simultaneous peripheral blood genomics demonstrates that some inflammatory mediators (MCP-1, IP-10, IL-15) are being expressed actively in circulating cells, while others (IL-6, IL-8, IFN-α and IFN-γ) are probable effectors produced locally at the site of infection. Interestingly, asymptomatic exposed subjects are not quiescent either immunologically or genomically, but instead exhibit early and persistent down-regulation of important inflammatory mediators in the periphery. The host inflammatory response to influenza infection is variable but robust, and evolves over time. These results offer critical insight into pathways driving influenza-related symptomatology and offer the potential to contribute to early detection and differentiation of infected hosts.

Protocol for the "Implementation, adoption, and utility of family history in diverse care settings" study

Background

Risk assessment with a thorough family health history is recommended by numerous organizations and is now a required component of the annual physical for Medicare beneficiaries under the Affordable Care Act. However, there are several barriers to incorporating robust risk assessments into routine care. MeTree, a web-based patient-facing health risk assessment tool, was developed with the aim of overcoming these barriers. In order to better understand what factors will be instrumental for broader adoption of risk assessment programs like MeTree in clinical settings, we obtained funding to perform a type III hybrid implementation-effectiveness study in primary care clinics at five diverse healthcare systems. Here, we describe the study’s protocol.

Methods/design

MeTree collects personal medical information and a three-generation family health history from patients on 98 conditions. Using algorithms built entirely from current clinical guidelines, it provides clinical decision support to providers and patients on 30 conditions. All adult patients with an upcoming well-visit appointment at one of the 20 intervention clinics are eligible to participate. Patient-oriented risk reports are provided in real time. Provider-oriented risk reports are uploaded to the electronic medical record for review at the time of the appointment. Implementation outcomes are enrollment rate of clinics, providers, and patients (enrolled vs approached) and their representativeness compared to the underlying population. Primary effectiveness outcomes are the percent of participants newly identified as being at increased risk for one of the clinical decision support conditions and the percent with appropriate risk-based screening. Secondary outcomes include percent change in those meeting goals for a healthy lifestyle (diet, exercise, and smoking). Outcomes are measured through electronic medical record data abstraction, patient surveys, and surveys/qualitative interviews of clinical staff.

Discussion

This study evaluates factors that are critical to successful implementation of a web-based risk assessment tool into routine clinical care in a variety of healthcare settings. The result will identify resource needs and potential barriers and solutions to implementation in each setting as well as an understanding potential effectiveness.

Trial registration

NCT01956773

Pilot study of pharmacist-assisted delivery of pharmacogenetic testing in a primary care setting

AIM

To describe the rationale and design of a pilot program to implement and evaluate pharmacogenetic (PGx) testing in a primary care setting.

STUDY RATIONALE

Several factors have impeded the uptake of PGx testing, including lack of provider knowledge and challenges with operationalizing PGx testing in a clinical practice setting.

STUDY DESIGN

We plan to compare two strategies for the implementation of PGx testing: a pharmacist-initiated testing arm compared with a physician-initiated PGx testing arm. Providers in both groups will be required to attend an introduction to PGx seminar. Anticipated results: We anticipate that providers in the pharmacist-initiated group will be more likely to order PGx testing than providers in the physician-initiated group.

CONCLUSION

Overall, we aim to generate data that will inform an effective delivery model for PGx testing and to facilitate a seamless integration of PGx testing in primary care practices.

A Guide for a Cardiovascular Genomics Biorepository: the CATHGEN Experience

The CATHeterization GENetics (CATHGEN) biorepository was assembled in four phases. First, project start-up began in 2000. Second, between 2001 and 2010, we collected clinical data and biological samples from 9334 individuals undergoing cardiac catheterization. Samples were matched at the individual level to clinical data collected at the time of catheterization and stored in the Duke Databank for Cardiovascular Diseases (DDCD). Clinical data included the following: subject demographics (birth date, race, gender, etc.); cardiometabolic history including symptoms; coronary anatomy and cardiac function at catheterization; and fasting chemistry data. Third, as part of the DDCD regular follow-up protocol, yearly evaluations included interim information: vital status (verified via National Death Index search and supplemented by Social Security Death Index search), myocardial infarction (MI), stroke, rehospitalization, coronary revascularization procedures, medication use, and lifestyle habits including smoking. Fourth, samples were used to generate molecular data. CATHGEN offers the opportunity to discover biomarkers and explore mechanisms of cardiovascular disease.

Gene Expression Profiles Link Respiratory Viral Infection, Platelet Response to Aspirin, and Acute Myocardial Infarction

Background

Influenza infection is associated with myocardial infarction (MI), suggesting that respiratory viral infection may induce biologic pathways that contribute to MI. We tested the hypotheses that 1) a validated blood gene expression signature of respiratory viral infection (viral GES) was associated with MI and 2) respiratory viral exposure changes levels of a validated platelet gene expression signature (platelet GES) of platelet function in response to aspirin that is associated with MI.

Methods

A previously defined viral GES was projected into blood RNA data from 594 patients undergoing elective cardiac catheterization and used to classify patients as having evidence of viral infection or not and tested for association with acute MI using logistic regression. A previously defined platelet GES was projected into blood RNA data from 81 healthy subjects before and after exposure to four respiratory viruses: Respiratory Syncytial Virus (RSV) (n=20), Human Rhinovirus (HRV) (n=20), Influenza A virus subtype H1N1 (H1N1) (n=24), Influenza A Virus subtype H3N2 (H3N2) (n=17). We tested for the change in platelet GES with viral exposure using linear mixed-effects regression and by symptom status.

Results

In the catheterization cohort, 32 patients had evidence of viral infection based upon the viral GES, of which 25% (8/32) had MI versus 12.2% (69/567) among those without evidence of viral infection (OR 2.3; CI [1.03-5.5], p=0.04). In the infection cohorts, only H1N1 exposure increased platelet GES over time (time course p-value = 1e-04).

Conclusions

A viral GES of non-specific, respiratory viral infection was associated with acute MI; 18% of the top 49 genes in the viral GES are involved with hemostasis and/or platelet aggregation. Separately, H1N1 exposure, but not exposure to other respiratory viruses, increased a platelet GES previously shown to be associated with MI. Together, these results highlight specific genes and pathways that link viral infection, platelet activation, and MI especially in the case of H1N1 influenza infection.

What was old is new again: using the host response to diagnose infectious disease

A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, focusing on both pathogen discovery and the host response. Here, we review how 'omics' technologies improve sepsis diagnosis, early pathogen identification and personalize therapy. Such host response diagnostics are possible due to the confluence of advanced laboratory techniques (e.g., transcriptomics, metabolomics, proteomics) along with advanced mathematical modeling such as machine learning techniques. The road ahead is promising, but obstacles remain before the impact of such advanced diagnostic modalities is felt at the bedside.

Making Personalized Health Care Even More Personalized: Insights From Activities of the IOM Genomics Roundtable

Genomic research has generated much new knowledge into mechanisms of human disease, with the potential to catalyze novel drug discovery and development, prenatal and neonatal screening, clinical pharmacogenomics, more sensitive risk prediction, and enhanced diagnostics. Genomic medicine, however, has been limited by critical evidence gaps, especially those related to clinical utility and applicability to diverse populations. Genomic medicine may have the greatest impact on health care if it is integrated into primary care, where most health care is received and where evidence supports the value of personalized medicine grounded in continuous healing relationships. Redesigned primary care is the most relevant setting for clinically useful genomic medicine research. Taking insights gained from the activities of the Institute of Medicine (IOM) Roundtable on Translating Genomic-Based Research for Health, we apply lessons learned from the patient-centered medical home national experience to implement genomic medicine in a patient-centered, learning health care system.

Gene Expression Signatures and the Spectrum of Coronary Artery Disease

Over the past 10-15 years, developments in gene expression profiling have opened new arenas for the discovery of important factors in the pathogenesis of numerous disease processes, including coronary artery disease. Messenger RNA and microRNA are differentially expressed in patients with coronary plaques, acute plaque rupture, and response to well-established treatments for acute coronary syndromes. In this review, we will explore recent developments in messenger RNA and microRNA technology at each stage of a patient's progression through the natural history of cardiovascular disease, including evaluation of risk factors, prediction and detection of coronary artery disease and acute coronary syndromes, and finally, response to treatments for coronary artery disease and its sequelae including congestive heart failure.

Global implementation of genomic medicine: We are not alone

Around the world, innovative genomic-medicine programs capitalize on singular capabilities arising from local health care systems, cultural or political milieus, and unusual selected risk alleles or disease burdens. Such individual efforts might benefit from the sharing of approaches and lessons learned in other locales. The U.S. National Human Genome Research Institute and the National Academy of Medicine recently brought together 25 of these groups to compare projects, to examine the current state of implementation and desired near-term capabilities, and to identify opportunities for collaboration that promote the responsible practice of genomic medicine. Efforts to coalesce these groups around concrete but compelling signature projects should accelerate the responsible implementation of genomic medicine in efforts to improve clinical care worldwide.

Patient beliefs and behaviors about genomic risk for type 2 diabetes: implications for prevention

Type 2 diabetes is a major health burden in the United States, and population trends suggest this burden will increase. High interest in, and increased availability of, testing for genetic risk of type 2 diabetes presents a new opportunity for reducing type 2 diabetes risk for many patients; however, to date, there is little evidence that genetic testing positively affects type 2 diabetes prevention. Genetic information may not fit patients' illness representations, which may reduce the chances of risk-reducing behavior changes. The present study aimed to examine illness representations in a clinical sample who are at risk for type 2 diabetes and interested in genetic testing. The authors used the Common Sense Model to analyze survey responses of 409 patients with type 2 diabetes risk factors. Patients were interested in genetic testing for type 2 diabetes risk and believed in its importance. Most patients believed that genetic factors are important to developing type 2 diabetes (67%), that diet and exercise are effective in preventing type 2 diabetes (95%), and that lifestyle changes are more effective than drugs (86%). Belief in genetic causality was not related to poorer self-reported health behaviors. These results suggest that patients' interest in genetic testing for type 2 diabetes might produce a teachable moment that clinicians can use to counsel behavior change.

Perspectives on genetic and genomic technologies in an academic medical center: the duke experience

In this age of personalized medicine, genetic and genomic testing is expected to become instrumental in health care delivery, but little is known about its actual implementation in clinical practice.

METHODS

We surveyed Duke faculty and healthcare providers to examine the extent of genetic and genomic testing adoption. We assessed providers' use of genetic and genomic testing options and indications in clinical practice, providers' awareness of pharmacogenetic applications, and providers' opinions on returning research-generated genetic test results to participants. Most clinician respondents currently use family history routinely in their clinical practice, but only 18 percent of clinicians use pharmacogenetics. Only two respondents correctly identified the number of drug package inserts with pharmacogenetic indications. We also found strong support for the return of genetic research results to participants. Our results demonstrate that while Duke healthcare providers are enthusiastic about genomic technologies, use of genomic tools outside of research has been limited. Respondents favor return of research-based genetic results to participants, but clinicians lack knowledge about pharmacogenetic applications. We identified challenges faced by this institution when implementing genetic and genomic testing into patient care that should inform a policy and education agenda to improve provider support and clinician-researcher partnerships.

Perceptions of Personalized Medicine in an Academic Health System: Educational Findings

OBJECTIVE

Prior reports demonstrate that personalized medicine implementation in clinical care is lacking. Given the program focus at Duke University on personalized medicine, we assessed health care providers' perspectives on their preparation and educational needs to effectively integrate personalized medicine tools and applications into their clinical practices.

METHODS

Data from 78 health care providers who participated in a larger study of personalized and precision medicine at Duke University were analyzed using Qualtrics (descriptive statistics). Individuals age 18 years and older were recruited for the larger study through broad email contacts across the university and health system. All participants completed an online 35-question survey that was developed, pilot-tested, and administered by a team of interdisciplinary researchers and clinicians at the Center for Applied Genomics and Precision Medicine.

RESULTS

Overall, providers reported being ill-equipped to implement personalized medicine in clinical practice. Many respondents identified educational resources as critical for strengthening personalized medicine implementation in both research and clinical practice. Responses did not differ significantly between specialists and primary providers or by years since completion of the medical degree.

CONCLUSIONS

Survey findings support prior calls for provider and patient education in personalized medicine. Respondents identified focus areas in training, education, and research for improving personalized medicine uptake. Given respondents' emphasis on educational needs, now may be an ideal time to address these needs in clinical training and public education programs.

An integrated transcriptome and expressed variant analysis of sepsis survival and death

Background

Sepsis, a leading cause of morbidity and mortality, is not a homogeneous disease but rather a syndrome encompassing many heterogeneous pathophysiologies. Patient factors including genetics predispose to poor outcomes, though current clinical characterizations fail to identify those at greatest risk of progression and mortality.

Methods

The Community Acquired Pneumonia and Sepsis Outcome Diagnostic study enrolled 1,152 subjects with suspected sepsis. We sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS) or sepsis (SIRS due to infection), including 78 sepsis survivors and 28 sepsis non-survivors who had previously undergone plasma proteomic and metabolomic profiling. Gene expression differences were identified between sepsis survivors, sepsis non-survivors, and SIRS followed by gene enrichment pathway analysis. Expressed sequence variants were identified followed by testing for association with sepsis outcomes.

Results

The expression of 338 genes differed between subjects with SIRS and those with sepsis, primarily reflecting immune activation in sepsis. Expression of 1,238 genes differed with sepsis outcome: non-survivors had lower expression of many immune function-related genes. Functional genetic variants associated with sepsis mortality were sought based on a common disease-rare variant hypothesis. VPS9D1, whose expression was increased in sepsis survivors, had a higher burden of missense variants in sepsis survivors. The presence of variants was associated with altered expression of 3,799 genes, primarily reflecting Golgi and endosome biology.

Conclusions

The activation of immune response-related genes seen in sepsis survivors was muted in sepsis non-survivors. The association of sepsis survival with a robust immune response and the presence of missense variants in VPS9D1 warrants replication and further functional studies.

Trial registration

ClinicalTrials.gov NCT00258869. Registered on 23 November 2005.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0111-5) contains supplementary material, which is available to authorized users.

The current epidemiology and clinical decisions surrounding acute respiratory infections

Acute respiratory infection (ARI) is a common diagnosis in outpatient and emergent care settings. Currently available diagnostics are limited, creating uncertainty in the use of antibacterial, antiviral, or supportive care. Up to 72% of ambulatory care patients with ARI are treated with an antibacterial, despite only a small fraction actually needing one. Antibiotic overuse is not restricted to ambulatory care: ARI accounts for approximately 5 million emergency department (ED) visits annually in the USA, where 52-61% of such patients receive antibiotics. Thus, an accurate test for the presence or absence of viral or bacterial infection is needed. In this review, we focus on recent research showing that the host-response (genomic, proteomic, or miRNA) can accomplish this task.

Development of a novel preclinical model of pneumococcal pneumonia in nonhuman primates

Pneumococcal pneumonia is a leading cause of bacterial infection and death worldwide. Current diagnostic tests for detecting Streptococcus pneumoniae can be unreliable and can mislead clinical decision-making and treatment. To address this concern, we developed a preclinical model of pneumococcal pneumonia in nonhuman primates useful for identifying novel biomarkers, diagnostic tests, and therapies for human S. pneumoniae infection. Adult colony-bred baboons (n = 15) were infected with escalating doses of S. pneumoniae (Serotype 19A-7). We characterized the pathophysiological and serological profiles of healthy and infected animals over 7 days. Pneumonia was prospectively defined by the presence of three criteria: (1) change in white blood cell count, (2) isolation of S. pneumoniae from bronchoalveolar lavage fluid (BALF) or blood, and (3) concurrent signs/symptoms of infection. Animals given 10(9) CFU consistently met our definition and developed a phenotype of tachypnea, tachycardia, fever, hypoxemia, and radiographic lobar infiltrates at 48 hours. BALF and plasma cytokines, including granulocyte colony-stimulating factor, IL-6, IL-10, and IL-1ra, peaked at 24 to 48 hours. At necropsy, there was lobar consolidation with frequent pleural involvement. Lung histopathology showed alveolar edema and macrophage influx in areas of organizing pneumonia. Hierarchical clustering of peripheral blood RNA data at 48 hours correctly identified animals with and without pneumonia. Dose-dependent inoculation of baboons with S. pneumoniae produces a host response ranging from spontaneous clearance (10(6) CFU) to severe pneumonia (10(9) CFU). Selected BALF and plasma cytokine levels and RNA profiles were associated with severe pneumonia and may provide clinically useful parameters after validation.

Comparing reference-based RNA-Seq mapping methods for non-human primate data

Background

The application of next-generation sequencing technology to gene expression quantification analysis, namely, RNA-Sequencing, has transformed the way in which gene expression studies are conducted and analyzed. These advances are of particular interest to researchers studying organisms with missing or incomplete genomes, as the need for knowledge of sequence information is overcome. De novo assembly methods have gained widespread acceptance in the RNA-Seq community for organisms with no true reference genome or transcriptome. While such methods have tremendous utility, computational cost is still a significant challenge for organisms with large and complex genomes.

Results

In this manuscript, we present a comparison of four reference-based mapping methods for non-human primate data. We utilize TopHat2 and GSNAP for mapping to the human genome, and Bowtie2 and Stampy for mapping to the human genome and transcriptome for a total of six mapping approaches. For each of these methods, we explore mapping rates and locations, number of detected genes, correlations between computed expression values, and the utility of the resulting data for differential expression analysis.

Conclusions

We show that reference-based mapping methods indeed have utility in RNA-Seq analysis of mammalian data with no true reference, and the details of mapping methods should be carefully considered when doing so. Critical algorithm features include short seed sequences, the allowance of mismatches, and the allowance of gapped alignments in addition to splice junction gaps. Such features facilitate sensitive alignment of non-human primate RNA-Seq data to a human reference.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-570) contains supplementary material, which is available to authorized users.

A host-based RT-PCR gene expression signature to identify acute respiratory viral infection

Improved ways to diagnose acute respiratory viral infections could decrease inappropriate antibacterial use and serve as a vital triage mechanism in the event of a potential viral pandemic. Measurement of the host response to infection is an alternative to pathogen-based diagnostic testing and may improve diagnostic accuracy. We have developed a host-based assay with a reverse transcription polymerase chain reaction (RT-PCR) TaqMan low-density array (TLDA) platform for classifying respiratory viral infection. We developed the assay using two cohorts experimentally infected with influenza A H3N2/Wisconsin or influenza A H1N1/Brisbane, and validated the assay in a sample of adults presenting to the emergency department with fever (n = 102) and in healthy volunteers (n = 41). Peripheral blood RNA samples were obtained from individuals who underwent experimental viral challenge or who presented to the emergency department and had microbiologically proven viral respiratory infection or systemic bacterial infection. The selected gene set on the RT-PCR TLDA assay classified participants with experimentally induced influenza H3N2 and H1N1 infection with 100 and 87% accuracy, respectively. We validated this host gene expression signature in a cohort of 102 individuals arriving at the emergency department. The sensitivity of the RT-PCR test was 89% [95% confidence interval (CI), 72 to 98%], and the specificity was 94% (95% CI, 86 to 99%). These results show that RT-PCR-based detection of a host gene expression signature can classify individuals with respiratory viral infection and sets the stage for prospective evaluation of this diagnostic approach in a clinical setting.

Genetically guided statin therapy on statin perceptions, adherence, and cholesterol lowering: a pilot implementation study in primary care patients

Statin adherence is often limited by side effects. The SLCO1B1*5 variant is a risk factor for statin side effects and exhibits statin-specific effects: highest with simvastatin/atorvastatin and lowest with pravastatin/rosuvastatin. The effects of SLCO1B1*5 genotype guided statin therapy (GGST) are unknown. Primary care patients (n = 58) who were nonadherent to statins and their providers received SLCO1B1*5 genotyping and guided recommendations via the electronic medical record (EMR). The primary outcome was the change in Beliefs about Medications Questionnaire, which measured patients' perceived needs for statins and concerns about adverse effects, measured before and after SLCO1B1*5 results. Concurrent controls (n = 59) were identified through the EMR to compare secondary outcomes: new statin prescriptions, statin utilization, and change in LDL-cholesterol (LDL-c). GGST patients had trends (p = 0.2) towards improved statin necessity and concerns. The largest changes were the "need for statin to prevent sickness" (p < 0.001) and "concern for statin to disrupt life" (p = 0.006). GGST patients had more statin prescriptions (p < 0.001), higher statin use (p < 0.001), and greater decrease in LDL-c (p = 0.059) during follow-up. EMR delivery of SLCO1B1*5 results and recommendations is feasible in the primary care setting. This novel intervention may improve patients' perceptions of statins and physician behaviors that promote higher statin adherence and lower LDL-c.