Recent studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in complex diseases. Such studies herald the future for genomic medicine and the opportunity for personalized prognosis in a variety of clinical contexts that utilize genomescale molecular information. Several key areas represent logical and critical next steps in the use of complex genomic profiling data towards the goal of personalized medicine. First, analyses should be geared toward the development of molecular profiles that predict future events – such as major clinical events or the response, resistance, or adverse reaction to therapy. Secondly, these must move into actual clinical practice by forming the basis for the next generation of clinical trials that will employ these methodologies to stratify patients. Lastly, there remain formidable challenges is in the translation of genomic technologies into clinical medicine that will need to be addressed: professional and public education, health outcomes research, reimbursement, regulatory oversight and privacy protection.
genomic medicine, personalized medicine, human genome.
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.
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.
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).
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.
To describe the rationale and design of a pilot program to implement and evaluate pharmacogenetic (PGx) testing in a primary care setting.
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.
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.
We anticipate that providers in the pharmacist-initiated group will be more likely to order PGx testing than providers in the physician-initiated group.
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.
clinical utility; pharmacist support; pharmacogenetics; pharmacogenetic testing; primary care
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.
personalized medicine; genetic tests; genomic tests; clinical implementation; pharmacogenetics; knowledge gaps; physician education; return of research results
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 109 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 (106 CFU) to severe pneumonia (109 CFU). Selected BALF and plasma cytokine levels and RNA profiles were associated with severe pneumonia and may provide clinically useful parameters after validation.
cytokines; biological markers; gene expression; Streptococcus pneumoniae; sepsis
Due to the lack of precise markers indicative of its occurrence and progression, coronary artery disease (CAD), the most common type of heart diseases, is currently associated with high mortality in the United States. To systemically identify novel protein biomarkers associated with CAD progression for early diagnosis and possible therapeutic intervention, we employed an iTRAQ-based quantitative proteomic approach to analyze the proteome changes in the plasma collected from a pair of wild type versus apolipoprotein E knockout (APOE −/−) mice which were fed with a high fat diet. In a multiplex manner ITRAQ serves as the quantitative ‘in-spectra’ marker for ‘cross-sample’ comparisons to determine the differentially expressed/secreted proteins caused by APOE knock-out. To obtain the most comprehensive proteomic datasets from this CAD-associated mouse model we applied both MALDI and ESI-based mass spectrometric (MS) platforms coupled with two different schemes of multidimensional liquid chromatography (2D-LC) separation. We then comparatively analyzed a series of the plasma samples collected at six and twelve weeks after the mice were fed with fat diets, where the 6-week or 12-week time point represents the early or intermediate phase of the fat-induced CAD, respectively. We then categorized those proteins showing abundance changes in accordance with APOE depletion. Several proteins such as the gamma and beta chains of fibrinogen, apolipoprotein B, apolipoprotein C-I, and thrombospondin-4 were among the previously known CAD markers identified by other methods. Our results suggested that these unbiased proteomic methods are both feasible and a practical means of discovering potential biomarkers associated with CAD progression.
Genetic information, typically communicated in-person by genetic counselors, can be challenging to comprehend; delivery of this information online -- as is becoming more common -- has the potential of increasing these challenges.
To address the impact of the mode of delivery of genomic risk information, 300 individuals were recruited from the general public and randomized to receive genomic risk information for Type 2 diabetes mellitus (T2DM) in-person from a board-certified genetic counselor or online through the testing company’s website.
Participants were asked to indicate their genomic risk and overall lifetime risk as reported on their test report as well as to interpret their genomic risk (increased, decreased, or same as population). For each question, 59% of participants correctly indicated their risk. Participants who received their results in-person were more likely than those who reviewed their results on-line to correctly interpret their genomic risk (72% vs. 47%, p = 0.0002) and report their actual genomic risk (69% vs. 49%, p=0.002).
The delivery of personal genomic risk through a trained health professional resulted in significantly higher comprehension. Therefore, if online delivery of genomic test results is to become more widespread, further evaluation of this method of communication may be needed to ensure the effective presentation of results to promote comprehension.
genomic risk; risk comprehension; risk communication; risk perception
The biopsy collection data from two lung cancer trials that required fresh tumor samples be obtained for microarray analysis were reviewed. In the trial for advanced disease, microarray data were obtained on 50 patient samples, giving an overall success rate of 60.2%. The majority of the specimens were obtained through CT-guided lung biopsies (N=30). In the trial for early-stage patients, 28 tissue specimens were collected from excess tumor after surgical resection with a success rate of 85.7%. This tissue procurement program documents the feasibility in obtaining fresh tumor specimens prospectively that could be used for molecular testing.
Lung cancer; Gene profiling; Bioinformatics
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.
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.
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.
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.
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.
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.
To develop RNA profiles that could serve as novel biomarkers for the response to aspirin.
Aspirin reduces death and myocardial infarction (MI) suggesting that aspirin interacts with biological pathways that may underlie these events.
We administered aspirin, followed by whole blood RNA microarray profiling, in a discovery cohort of healthy volunteers (HV1,n=50), and two validation cohorts of volunteers (HV2,n=53) or outpatient cardiology patients (OPC, n=25). Platelet function was assessed by platelet function score (PFS; HV1/HV2) or VerifyNow Aspirin (OPC). Bayesian sparse factor analysis identified sets of coexpressed transcripts, which were examined for association with PFS in HV1 and validated in HV2 and OPC. Proteomic analysis confirmed the association of validated transcripts in platelet proteins. Validated gene sets were tested for association with death/MI in two patient cohorts (n=587, total) from RNA samples collected at cardiac catheterization.
A set of 60 co-expressed genes named the “aspirin response signature” (ARS) was associated with PFS in HV1 (r = −0.31, p = 0.03), HV2 (r = −0.34, Bonferroni p = 0.03), and OPC (p = 0.046). Corresponding proteins for 17 ARS genes were identified in the platelet proteome, of which, six were associated with PFS. The ARS was associated with death/MI in both patient cohorts (odds ratio = 1.2, p = 0.01 and hazard ratio = 1.5, p = 0.001), independent of cardiovascular risk factors. Compared with traditional risk factors, reclassification (net reclassification index = 31 - 37%, p ≤ 0.0002) was improved by including the ARS or one of its genes, ITGA2B.
RNA profiles of platelet-specific genes are novel biomarkers for identifying those do not response adequately to aspirin and who are at risk for death/MI.
aspirin; platelets; genes; myocardial infarction; biomarkers
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.
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.
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.
RNA-Sequencing; Genomics; Mapping
In this paper, we describe a surface-enhanced Raman scattering (SERS)-based detection approach, referred to as “molecular sentinel” (MS) plasmonic nanoprobes, to detect an RNA target related to viral infection. The MS method is essentially a label-free technique incorporating the SERS effect modulation scheme associated with silver nanoparticles and Raman dye-labeled DNA hairpin probes. Hybridization with target sequences opens the hairpin and spatially separates the Raman label from the silver surface thus reducing the SERS signal of the label. Herein, we have developed a MS nanoprobe to detect the human radical S-adenosyl methionine domain containing 2 (RSAD2) RNA target as a model system for method demonstration. The human RSAD2 gene has recently emerged as a novel host-response biomarker for diagnosis of respiratory infections. Our results showed that the RSAD2 MS nanoprobes exhibits high specificity and can detect as low as 1 nM target sequences. With the use of a portable Raman spectrometer and total RNA samples, we have also demonstrated for the first time the potential of the MS nanoprobe technology for detection of host-response RNA biomarkers for infectious disease diagnostics.
Surface-enhanced Raman scattering; SERS; nanoprobe; infectious disease detection
A major promise of genomic research is information that can transform health care and public health through earlier diagnosis, more effective prevention and treatment of disease, and avoidance of drug side effects. Although there is interest in the early adoption of emerging genomic applications in cancer prevention and treatment, there are substantial evidence gaps that are further compounded by the difficulties of designing adequately powered studies to generate this evidence, thus limiting the uptake of these tools into clinical practice. Comparative effectiveness research (CER) is intended to generate evidence on the “real-world” effectiveness compared with existing standards of care so informed decisions can be made to improve health care. Capitalizing on funding opportunities from the American Recovery and Reinvestment Act of 2009, the National Cancer Institute funded seven research teams to conduct CER in genomic and precision medicine and sponsored a workshop on CER on May 30, 2012, in Bethesda, Maryland. This report highlights research findings from those research teams, challenges to conducting CER, the barriers to implementation in clinical practice, and research priorities and opportunities in CER in genomic and precision medicine. Workshop participants strongly emphasized the need for conducting CER for promising molecularly targeted therapies, developing and supporting an integrated clinical network for open-access resources, supporting bioinformatics and computer science research, providing training and education programs in CER, and conducting research in economic and decision modeling.
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.
pharmacogenetics; personalized medicine; medication adherence; risk assessment; health behavior; hyperlipidemia
Type 2 diabetes (T2D) and coronary heart disease (CHD) are prevalent chronic diseases from which military personnel are not exempt. While many genetic markers for these diseases have been identified, the clinical utility of genetic risk testing for multifactorial diseases such as these has not been established. The need for a behavioral intervention such as health coaching following a risk counseling intervention for T2D or CHD also has not been explored. Here we present the rationale, design, and protocol for evaluating the clinical utility of genetic risk testing and health coaching for active duty US Air Force (AF) retirees and beneficiaries.
Primary Study Objectives:
Determine the direct and interactive effects of health coaching and providing genetic risk information when added to standard risk counseling for CHD and T2D on health behaviors and clinical risk markers.
Four-group (2 X 2 factorial) randomized controlled trial.
Two AF primary care clinical settings on the west coast of the United States.
Adult AF primary care patients.
All participants will have a risk counseling visit with a clinic provider to discuss personal risk factors for T2D and CHD. Half of the participants (two groups) will also learn of their genetic risk testing results for T2D and CHD in this risk counseling session. Participants randomized to the two groups receiving health coaching will then receive telephonic health coaching over 6 months.
Main Outcome Measures:
Behavioral measures (self-reported dietary intake, physical activity, smoking cessation, medication adherence); clinical outcomes (AF composite fitness scores, weight, waist circumference, blood pressure, fasting glucose, lipids, T2D/CHD risk scores) and psychosocial measures (self-efficacy, worry, perceived risk) will be collected at baseline and 6 weeks, and 3, 6, and 12 months.
This study tests novel strategies deployed within existing AF primary care to increase adherence to evidence-based diet, physical activity, smoking cessation, and medication recommendations for CHD and T2D risk reduction through methods of patient engagement and self-management support.
Health coaching; genomics; chronic disease; behavior change; diabetes; coronary heart disease
Background: Variable health literacy and genetic knowledge may pose significant challenges to engaging the general public in personal genomics, specifically with respect to promoting risk comprehension and healthy behaviors. Methods: We are conducting a multistage study of individual responses to genomic risk information for Type 2 diabetes mellitus. A total of 300 individuals were recruited from the general public in Durham, North Carolina: 60% self-identified as White; 70% female; and 65% have a college degree. As part of the baseline survey, we assessed genetic knowledge and attitudes toward genetic testing. Results: Scores of factual knowledge of genetics ranged from 50% to 100% (average=84%), with significant differences in relation to racial groups, the education level, and age. Scores were significantly higher on questions pertaining to the inheritance and causes of disease (mean score 90%) compared to scientific questions (mean score 77.4%). Scores on the knowledge survey were significantly higher than scores from European populations. Participants' perceived knowledge of the social consequences of genetic testing was significantly lower than their perceived knowledge of the medical uses of testing. More than half agreed with the statement that testing may affect a person's ability to obtain health insurance (51.3%) and 16% were worried about the consequences of testing for chances of finding a job. Conclusions: Despite the relatively high educational status and genetic knowledge of the study population, we find an imbalance of knowledge between scientific and medical concepts related to genetics as well as between the medical applications and societal consequences of testing, suggesting that more effort is needed to present the benefits, risks, and limitations of genetic testing, particularly, at the social and personal levels, to ensure informed decision making.
In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving towards more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating “big data” science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.
big data; clinical trials; cohort studies; epidemiology; genomics; medicine; public health; technologies; training; translational research
Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features, and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would die differed markedly from those who would survive. The different profiles of proteins and metabolites clustered into fatty acid transport and β-oxidation, gluconeogenesis and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of seven metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.
Studies have shown that the quality of family health history (FHH) collection in primary care is inadequate to assess disease risk. To use FHH for risk assessment, collected data must have adequate detail. To address this issue, we developed a patient facing FHH assessment tool, MeTree. In this paper we report the content and quality of the FHH collected using MeTree.
Design: A hybrid implementation-effectiveness study. Patients were recruited from 2009 to 2012. Setting: Two community primary care clinics in Greensboro, NC. Participants: All non-adopted adult English speaking patients with upcoming appointments were invited to participate. Intervention: Education about and collection of FHH with entry into MeTree. Measures: We report the proportion of pedigrees that were high-quality. High-quality pedigrees are defined as having all the following criteria: (1) three generations of relatives, (2) relatives’ lineage, (3) relatives’ gender, (4) an up-to-date FHH, (5) pertinent negatives noted, (6) age of disease onset in affected relatives, and for deceased relatives, (7) the age and (8) cause of death (Prim Care31:479–495, 2004.).
Enrollment: 1,184. Participant demographics: age range 18-92 (mean 58.8, SD 11.79), 56% male, and 75% white. The median pedigree size was 21 (range 8-71) and the FHH entered into MeTree resulted in a database of 27,406 individuals. FHHs collected by MeTree were found to be high quality in 99.8% (N = 1,182/1,184) as compared to <4% at baseline. An average of 1.9 relatives per pedigree (range 0-50, SD 4.14) had no data reported. For pedigrees where at least one relative has no data (N = 497/1,184), 4.97 relatives per pedigree (range 1-50, SD 5.44) had no data. Talking with family members before using MeTree significantly decreased the proportion of relatives with no data reported (4.98% if you talked to your relative vs. 10.85% if you did not, p-value < 0.001.).
Using MeTree improves the quantity and quality of the FHH data that is collected and talking with relatives prior to the collection of FHH significantly improves the quantity and quality of the data provided. This allows more patients to be accurately risk stratified and offered appropriate preventive care guided by their risk level.
Family history; Data quality; Patient-centered
Despite stunning advances in our understanding of the genetics and the molecular basis for cancer, many patients with cancer are not yet receiving therapy tailored specifically to their tumor biology. The translation of these advances into clinical practice has been hindered, in part, by the lack of evidence for biomarkers supporting the personalized medicine approach. Most stakeholders agree that the translation of biomarkers into clinical care requires evidence of clinical utility. The highest level of evidence comes from randomized controlled clinical trials (RCTs). However, in many instances, there may be no RCTs that are feasible for assessing the clinical utility of potentially valuable genomic biomarkers. In the absence of RCTs, evidence generation will require well-designed cohort studies for comparative effectiveness research (CER) that link detailed clinical information to tumor biology and genomic data. CER also uses systematic reviews, evidence-quality appraisal, and health outcomes research to provide a methodologic framework for assessing biologic patient subgroups. Rapid learning health care (RLHC) is a model in which diverse data are made available, ideally in a robust and real-time fashion, potentially facilitating CER and personalized medicine. Nonetheless, to realize the full potential of personalized care using RLHC requires advances in CER and biostatistics methodology and the development of interoperable informatics systems, which has been recognized by the National Cancer Institute's program for CER and personalized medicine. The integration of CER methodology and genomics linked to RLHC should enhance, expedite, and expand the evidence generation required for fully realizing personalized cancer care.
We propose a mixture model for text data designed to capture underlying structure in the history of present illness section of electronic medical records data. Additionally, we propose a method to induce bias that leads to more homogeneous sets of diagnoses for patients in each cluster. We apply our model to a collection of electronic records from an emergency department and compare our results to three other relevant models in order to assess performance. Results using standard metrics demonstrate that patient clusters from our model are more homogeneous when compared to others, and qualitative analyses suggest that our approach leads to interpretable patient sub-populations when applied to real data. Finally, we demonstrate an example of our patient clustering model to identify adverse drug events.
Capturing the host response by using genomic technologies such as transcriptional profiling provides a new paradigm for classifying and diagnosing infectious disease and for potentially distinguishing infection from other causes of serious respiratory illness. This strategy has been used to define a blood-based RNA signature as a classifier for pandemic H1N1 influenza infection that is distinct from bacterial pneumonia and other inflammatory causes of respiratory disease. To realize the full potential of this approach as a diagnostic test will require additional independent validation of the results and studies to examine the specificity of this signature for viral versus bacterial infection or co-infection.
It is anticipated that as the range of drugs for which pharmacogenetic testing becomes available expands, primary care physicians (PCPs) will become major users of these tests. To assess their training, familiarity, and attitudes toward pharmacogenetic testing in order to identify barriers to uptake that may be addressed at this early stage of test use, we conducted a national survey of a sample of PCPs. Respondents were mostly white (79%), based primarily in community-based primary care (81%) and almost evenly divided between family medicine and internal medicine. The majority of respondents had heard of PGx testing and anticipated that these tests are or would soon become a valuable tool to inform drug response. However, only a minority of respondents (13%) indicated they felt comfortable ordering PGx tests and almost a quarter reported not having any education about pharmacogenetics.
Our results indicate that primary care practitioners envision a major role for themselves in the delivery of PGx testing but recognize their lack of adequate knowledge and experience about these tests. Development of effective tools for guiding PCPs in the use of PGx tests should be a high priority.