Opportunities and challenges for biomarker discovery using electronic health record data

Electronic health records (EHRs) have become increasingly relied upon as a source for biomedical research. One important research application of EHRs is the identification of biomarkers associated with specific patient states, especially within complex conditions. However, using EHRs for biomarker identification can be challenging because the EHR was not designed with research as the primary focus. Despite this challenge, the EHR offers huge potential for biomarker discovery research to transform our understanding of disease etiology and treatment and generate biological insights informing precision medicine initiatives. This review paper provides an in-depth analysis of how EHR data is currently used for phenotyping and identifying molecular biomarkers, current challenges and limitations, and strategies we can take to mitigate challenges going forward.     

Using automated electronic medical record data extraction to model ALS survival and progression

Background

To assess the feasibility of using automated capture of Electronic Medical Record (EMR) data to build predictive models for amyotrophic lateral sclerosis (ALS) outcomes.

Methods

We used an Informatics for Integrating Biology and the Bedside search discovery tool to identify and extract data from 354 ALS patients from the University of Kansas Medical Center EMR. The completeness and integrity of the data extraction were verified by manual chart review. A linear mixed model was used to model disease progression. Cox proportional hazards models were used to investigate the effects of BMI, gender, and age on survival.

Results

Data extracted from the EMR was sufficient to create simple models of disease progression and survival. Several key variables of interest were unavailable without including a manual chart review. The average ALS Functional Rating Scale – Revised (ALSFRS-R) baseline score at first clinical visit was 34.08, and average decline was ??0.64 per month. Median survival was 27?months after first visit. Higher baseline ALSFRS-R score and BMI were associated with improved survival, higher baseline age was associated with decreased survival.

Conclusions

This study serves to show that EMR-captured data can be extracted and used to track outcomes in an ALS clinic setting, potentially important for post-marketing research of new drugs, or as historical controls for future studies. However, as automated EMR-based data extraction becomes more widely used there will be a need to standardize ALS data elements and clinical forms for data capture so data can be pooled across academic centers.

     

Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data

Genomic medicine research requires substantial time and resources to obtain phenotype data. The electronic health record offers potential efficiencies in addressing these temporal and economic challenges, but few studies have explored the feasibility of using such data for genetics research. The main objective of this study was to determine the association of two genetic variants located on chromosome 9p21 conferring susceptibility to coronary heart disease and type 2 diabetes with a variety of clinical phenotypes derived from the electronic health record in a population of morbidly obese patients. Data on more than 100 clinical measures including diagnoses, laboratory values, and medications were extracted from the electronic health records of a total of 709 morbidly obese (body mass index (BMI) >/= 40 kg/m(2)) patients. Two common single nucleotide polymorphisms located at chromosome 9p21 recently linked to coronary heart disease and type 2 diabetes (McPherson et al. Science 316:1488-1491, 2007; Saxena et al. Science 316:1331-1336, 2007; Scott et al. Science 316:1341-1345, 2007) were genotyped to assess statistical association with clinical phenotypes. Neither the type 2 diabetes variant nor the coronary heart disease variant was related to any expected clinical phenotype, although high-risk type 2 diabetes/coronary heart disease compound genotypes were associated with several coronary heart disease phenotypes. Electronic health records may be efficient sources of data for validation studies of genetic associations.     

TabSQL: a MySQL tool to facilitate mapping user data to public databases

Background  

With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases.     

Automated identification of subpopulations in flow cytometric list mode data using cluster analysis

The application of K-means (ISODATA) cluster analysis to flow cytometric data is described. The results of analyses of flow cytometric data for mixtures of fluorescent microspheres and samples of peripheral blood mononuclear cells are presented. A method for simultaneously displaying list mode data for any number of parameters, which had previously been applied to a continuous set of parameters such as multi-angle light scattering data, is used to present the results of cluster analysis on physically unrelated parameters; this method allows rapid evaluation of the success of subpopulation identification. The factors that influence automated identification of subpopulations are examined, and methods for determining optimal values for these factors are described.     

The implications of electronic health record for personalized medicine

The emerging concept of an electronic health record (EHR) targeted at a patient centric, cross-institutional and longitudinal information entity (possibly spanning the individuals lifetime) has great promise for personalized medicine. In fact, it is probably the only vehicle through which we may truly realize the personalization of medicine beyond population-based genetic profiles that are expected to become part of medication and treatment indications in the near future. The new EHR standards include mechanisms that integrate clinical data with genomic testing results obtained through applying research-type procedures, such as full DNA sequencing, to an individual patient. Although the most optimal process for the utilization of integrated clinical-genomic data in the EHR framework is still unclear, the new Health Level Seven (HL7) Clinical Genomics Draft Standard for Trial Use suggests using the 'encapsulate & bubble-up' approach, which includes two main phases: the encapsulation of raw genomic data and bubbling-up the most clinically significant portions of that data, while associating it with clinical phenotypes residing in the individual's EHR.     

Automated identification of single nucleotide polymorphisms from sequencing data

The single nucleotide polymorphism (SNP) is the difference of the DNA sequence between individuals and provides abundant information about genetic variation. Large scale discovery of high frequency SNPs is being undertaken using various methods. However, the publicly available SNP data sometimes need to be verified. If only a particular gene locus is concerned, locus-specific polymerase chain reaction amplification may be useful. Problem of this method is that the secondary peak has to be measured. We have analyzed trace data from conventional sequencing equipment and found an applicable rule to discern SNPs from noise. The rule is applied to multiply aligned sequences with a trace and the peak height of the traces are compared between samples. We have developed software that integrates this function to automatically identify SNPs. The software works accurately for high quality sequences and also can detect SNPs in low quality sequences. Further, it can determine allele frequency, display this information as a bar graph and assign corresponding nucleotide combinations. It is also designed for a person to verify and edit sequences easily on the screen. It is very useful for identifying de novo SNPs in a DNA fragment of interest.     

Automated identification of reference genes based on RNA-seq data

Background

Gene expression analyses demand appropriate reference genes (RGs) for normalization, in order to obtain reliable assessments. Ideally, RG expression levels should remain constant in all cells, tissues or experimental conditions under study. Housekeeping genes traditionally fulfilled this requirement, but they have been reported to be less invariant than expected; therefore, RGs should be tested and validated for every particular situation. Microarray data have been used to propose new RGs, but only a limited set of model species and conditions are available; on the contrary, RNA-seq experiments are more and more frequent and constitute a new source of candidate RGs.

Results

An automated workflow based on mapped NGS reads has been constructed to obtain highly and invariantly expressed RGs based on a normalized expression in reads per mapped million and the coefficient of variation. This workflow has been tested with Roche/454 reads from reproductive tissues of olive tree (Olea europaea L.), as well as with Illumina paired-end reads from two different accessions of Arabidopsis thaliana and three different human cancers (prostate, small-cell cancer lung and lung adenocarcinoma). Candidate RGs have been proposed for each species and many of them have been previously reported as RGs in literature. Experimental validation of significant RGs in olive tree is provided to support the algorithm.

Conclusion

Regardless sequencing technology, number of replicates, and library sizes, when RNA-seq experiments are designed and performed, the same datasets can be analyzed with our workflow to extract suitable RGs for subsequent PCR validation. Moreover, different subset of experimental conditions can provide different suitable RGs.

     

Erroneous information in the epidemiological surveillance of hepatitis B

False positive results obtained in the determination of the immune markers of hepatitis A by the methods of the passive reverse hemagglutination test and the enzyme immunoassay distort the course of the registration of the epidemic process and deteriorate the quality of the antiepidemic measures ensured to the population. The necessity of the standardization of available test systems is postulated.     

Cellular immune response to hepatitis B virus-encoded antigens in acute and chronic hepatitis B virus infection

The proliferative response of PBMC to hepatitis B virus (HBV) envelope, core, and e Ag was analyzed prospectively in 21 patients with acute self-limited HBV infection and compared with the response of patients with chronic HBV infection and different levels of HBV replication (i.e., hepatitis e Ag (HBeAg)- or anti-HBe-positive) and liver damage (i.e., chronic active hepatitis or chronic asymptomatic carriers). Our results indicate that: 1) HBV-infected subjects who develop a self-limited acute hepatitis show a vigorous PBMC response to hepatitis B core Ag and HBeAg, as expression of T cell activation; 2) appearance of a detectable lymphocyte response to HBV nucleocapsid Ag is temporally associated with the clearance of HBV envelope Ag; 3) in patients with chronic HBV infection the level of T cell responsiveness to hepatitis B core Ag and to HBeAg is significantly lower than that observed during acute infection; 4) T cell sensitization to HBV envelope Ag in acute and chronic HBV infection is usually undetectable and when measurable is expressed transiently and at low levels. These results may reflect immune events of pathogenetic relevance with respect to evolution of disease and viral clearance.     

Disease risk analysis: A paradigm for using health-based data to inform primate conservation and public health

Risk analysis is a multidisciplinary process used to evaluate existing knowledge in order to prioritize risks associated with the spread of disease. A principle aim of risk analysis is to facilitate the development of cost-effective management strategies. Risk analysis calls for a multidisciplinary approach to piece together and integrate the numerous factors that influence disease transmission. The seven papers included in this volume of AJP present current primatological research as viewed through the prism of risk analysis. Issues such as interspecies disease transmission, public health, and conservation of endangered species are addressed, and risk analysis is put forward as a possible paradigm to promote understanding of infectious disease and its impact on nonhuman primate and human populations.     

Challenges of the medical entomology for the surveillance in public health in Colombia: reflections on the state of malaria

The relevance of the medical entomology was considered with respect to current framework of malaria control programs in Colombia. A responsibility is indicated for balancing control efforts along with providing information on the malaria vectors. This knowledge must be acquired in order to focus the related activities that are required. The malaria control program must be based on results of local entomological surveillance, and the data must be in a form to give practical answers to questions regarding the control program. Difficulties in undertaking the required studies are described, particularly regarding the taxonomic identification of Colombian Anopheles in Colombia and which of these can be incriminated as malaria vectors.     

Integration of data from remote monitoring systems and programmers into the hospital electronic health record system based on international standards

Remote follow-up of implanted ICDs may offer a solution to the problem of overcrowded outpatient clinics. All major device companies have developed a remote follow-up solution. Data obtained from the remote follow-up systems are stored in a central database system, operated and owned by the device company and accessible for the physician or technician. However, the problem now arises that part of the patient's clinical information is stored in the local electronic health record (EHR) system in the hospital, while another part is only available in the remote monitoring database. This may potentially result in patient safety issues. Ideally all information should become available in the EHR system. IHE (Integrating the Healthcare Enterprise) is an initiative to improve the way computer systems in healthcare share information. To address the requirement of integrating remote monitoring data in the local EHR, the IHE Implantable Device Cardiac Observation (IDCO) profile has been developed. In our hospital, we have implemented the IHE IDCO profile to import data from the remote databases from two device vendors into the departmental Cardiology Information System. Data are exchanged via an HL7/XML communication protocol, as defined in the IHE IDCO profile.