Christopher G. Chute

Validity of self-reported waist and hip circumferences in men and women.

Recent epidemiologic evidence indicates an association between fat distribution and many diseases. To assess the validity of circumference measurements obtained by self-report, the authors analyzed data from 123 men aged 4C75 years and 140 women aged 41–65 years, drawn from two large ongoing prospective studies. On mailed questionnaires, subjects were asked to measure and record their weight and waist and hip circumferences. These data were compared with standardized measurements taken approximately six months apart by technicians who visited participants at their homes. Crude Pearson correlations between self-reported waist circumferences and the average of two technician-measured waist circumferences were 0.95 for men and 0.89 for women. Similar correlations for hip measurements were 0.88 for men and 0.84 for women, and for waist-to-hip ratios, 0.69 for men and 0.70 for women. After adjusting for age and body mass index (kg/m2), correlations for waist-to-hip ratios were 0.55 for men and 0.58 for women. Correlations became stronger after correcting for random within-person variability from daily or seasonal fluctuations. Self-reported and measured weights were highly correlated: 0.97 for men and 0.97 for women. Self-reported waist, hip, and weight measurements appear reasonably valid. The moderate degree of measurement error for the ratio of self-reported waist and hip circumferences, however, implies that previously reported associations based on self-report of these measures may have been appreciably underestimated. (Epidemiology 1990; 1:46&473)

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at http://www.ohnlp.org. The cTAKES builds on existing open-source technologies-the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text.

The prevalence of Prostatism: A Population-Based Survey of Urinary Symptoms

To establish the age-specific prevalence of urinary symptoms among a community-based cohort of men, a randomly selected sample of men were screened and invited to participate in a longitudinal survey of urinary symptoms. The population of Olmsted County, Minnesota, as enumerated by the Rochester Epidemiology Project, formed the sampling base for this study. Men between 40 and 79 years old with no history of prostate or other urological surgery, and who also were free of conditions associated with neurogenic bladder were invited to participate. A previously validated questionnaire was completed by the subject. Urine flow measures, current medications and family histories of urinary disease were also obtained. Nonresponse corrected scores for a composite of obstructive symptoms showed moderate to severe symptomatology among 13% of the men 40 to 49 years old and 28% of those older than 70 years. Prostatism is a highly prevalent symptom complex among unselected men in the community. The specific urinary symptoms of nocturia, weak stream, restarting, urgency and sensation of incomplete emptying are strongly age-related and, therefore, may be predictive of a prostatic disease process.

BioPortal: ontologies and integrated data resources at the click of a mouse

Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing and decision support. BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides access via Web services and Web browsers to ontologies developed in OWL, RDF, OBO format and Protégé frames. BioPortal functionality includes the ability to browse, search and visualize ontologies. The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content by providing features to add notes to ontology terms, mappings between terms and ontology reviews based on criteria such as usability, domain coverage, quality of content, and documentation and support. BioPortal also enables integrated search of biomedical data resources such as the Gene Expression Omnibus (GEO), ClinicalTrials.gov, and ArrayExpress, through the annotation and indexing of these resources with ontologies in BioPortal. Thus, BioPortal not only provides investigators, clinicians, and developers ‘one-stop shopping’ to programmatically access biomedical ontologies, but also provides support to integrate data from a variety of biomedical resources.

An example-based mapping method for text categorization and retrieval

A unified model for text categorization and text retrieval is introduced. We use a training set of manually categorized documents to learn word-category associations, and use these associations to predict the categories of arbitrary documents. Similarly, we use a training set of queries and their related documents to obtain empirical associations between query words and indexing terms of documents, and use these associations to predict the related documents of arbitrary queries. A Linear Least Squares Fit (LLSF) technique is employed to estimate the likelihood of these associations. Document collections from the MEDLINE database and Mayo patient records are used for studies on the effectiveness of our approach, and on how much the effectiveness depends on the choices of training data, indexing language, word-weighting scheme, and morphological canonicalization. Alternative methods are also tested on these data collections for comparison. It is evident that the LLSF approach uses the relevance information effectively within human decisions of categorization and retrieval, and achieves a semantic mapping of free texts to their representations in an indexing language. Such a semantic mapping lead to a significant improvement in categorization and retrieval, compared to alternative approaches.

The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies.

IntroductionThe eMERGE (electronic MEdical Records and GEnomics) Network is an NHGRI-supported consortium of five institutions to explore the utility of DNA repositories coupled to Electronic Medical Record (EMR) systems for advancing discovery in genome science. eMERGE also includes a special emphasis on the ethical, legal and social issues related to these endeavors.OrganizationThe five sites are supported by an Administrative Coordinating Center. Setting of network goals is initiated by working groups: (1) Genomics, (2) Informatics, and (3) Consent & Community Consultation, which also includes active participation by investigators outside the eMERGE funded sites, and (4) Return of Results Oversight Committee. The Steering Committee, comprised of site PIs and representatives and NHGRI staff, meet three times per year, once per year with the External Scientific Panel.Current progressThe primary site-specific phenotypes for which samples have undergone genome-wide association study (GWAS) genotyping are cataract and HDL, dementia, electrocardiographic QRS duration, peripheral arterial disease, and type 2 diabetes. A GWAS is also being undertaken for resistant hypertension in ≈2,000 additional samples identified across the network sites, to be added to data available for samples already genotyped. Funded by ARRA supplements, secondary phenotypes have been added at all sites to leverage the genotyping data, and hypothyroidism is being analyzed as a cross-network phenotype. Results are being posted in dbGaP. Other key eMERGE activities include evaluation of the issues associated with cross-site deployment of common algorithms to identify cases and controls in EMRs, data privacy of genomic and clinically-derived data, developing approaches for large-scale meta-analysis of GWAS data across five sites, and a community consultation and consent initiative at each site.Future activitiesPlans are underway to expand the network in diversity of populations and incorporation of GWAS findings into clinical care.SummaryBy combining advanced clinical informatics, genome science, and community consultation, eMERGE represents a first step in the development of data-driven approaches to incorporate genomic information into routine healthcare delivery.

Trends in Mastectomy Rates at the Mayo Clinic Rochester: Effect of Surgical Year and Preoperative Magnetic Resonance Imaging

PURPOSE Recent changes have occurred in the presurgical planning for breast cancer, including the introduction of preoperative breast magnetic resonance imaging (MRI). We sought to analyze the trends in mastectomy rates and the relationship to preoperative MRI and surgical year at Mayo Clinic, Rochester, MN. PATIENTS AND METHODS We identified 5,405 patients who underwent surgery between 1997 and 2006. Patients undergoing MRI were identified from a prospective database. Trends in mastectomy rate and the association of MRI with surgery type were analyzed. Multiple logistic regression was used to assess the effect of surgery year and MRI on surgery type, while adjusting for potential confounding variables. RESULTS Mastectomy rates differed significantly across time (P < .0001), and decreased from 45% in 1997% to 31% in 2003, followed by increasing rates for 2004 to 2006. The use of MRI increased from 10% in 2003% to 23% in 2006 (P < .0001). Patients with MRI were more likely to undergo mastectomy than those without MRI (54% v 36%; P < .0001). However, mastectomy rates increased from 2004 to 2006 predominantly among patients without MRI (29% in 2003% to 41% in 2006; P < .0001). In a multivariable model, both MRI (odds ratio [OR], 1.7; P < .0001) and surgical year (compared to 2003 OR: 1.4 for 2004, 1.8 for 2005, and 1.7 for 2006; P < .0001) were independent predictors of mastectomy. CONCLUSION After a steady decline, mastectomy rates have increased in recent years with both surgery year and MRI as significant predictors for type of surgery. Further studies are needed to evaluate the role of MRI and other factors influencing surgical planning.

Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data

Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with P < 4.6 × 10−6 (false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (n = 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.

The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future

The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute–funded consortium engaged in the development of methods and best practices for using the electronic medical record as a tool for genomic research. Now in its sixth year and second funding cycle, and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from electronic medical records can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and health-care informatics, particularly for electronic phenotyping, genome-wide association studies, genomic medicine implementation, and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here, we describe the evolution, accomplishments, opportunities, and challenges of the network from its inception as a five-group consortium focused on genotype–phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting toward the implementation of genomic medicine.Genet Med 15 10, 761–771.Genetics in Medicine (2013); 15 10, 761–771. doi:10.1038/gim.2013.72

Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium

Clinical data captured in electronic medical records accurately identify cases and controls for genome-wide association studies. Where Electronic Records and Genomics Meet There has been a surge of interest in using electronic medical records in hospitals and clinics to capture information about patients that is normally buried in doctors’ handwritten notes. Indeed, the U.S. government has made the implementation of electronic medical records a priority area and has instigated standards for the recording and use of these records. The clinical data captured in electronic medical records including diagnoses, medical tests, and medications provide accurate clinical information that will improve patient care. With the ability to sequence the genomes of individuals faster and cheaper than ever before, it may be possible in the future to include the genome sequences of patients in their electronic medical records. A consortium called the Electronic Medical Records and Genomics Network (eMERGE) has set out to investigate whether clinical data captured in electronic medical records could be used to accurately identify patients with particular diseases for inclusion in genome-wide association studies (GWAS). GWAS scrutinize the genomes of individuals with particular diseases to identify tiny genetic variations that are associated with the risk of developing that disease. Here, the eMERGE consortium reports its study of the electronic medical records from five clinical centers and how accurately it identified patients with one of five diseases: dementia, cataracts, peripheral arterial disease, type 2 diabetes, and cardiac conduction defects. The investigators show that even though the electronic medical records were of different types and did not all use natural language processing to extract information from the records, they were able to obtain robust positive and negative values for identifying patients with these diseases with sufficient accuracy for use in GWAS. They conclude that widespread adoption of electronic medical records will provide real-world clinical data that will be valuable for GWAS and other types of genetic research. Clinical data in electronic medical records (EMRs) are a potential source of longitudinal clinical data for research. The Electronic Medical Records and Genomics Network (eMERGE) investigates whether data captured through routine clinical care using EMRs can identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies (GWAS). Using data from five different sets of EMRs, we have identified five disease phenotypes with positive predictive values of 73 to 98% and negative predictive values of 98 to 100%. Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format. We identified natural language processing as an important tool to improve case identification rates. Efforts and incentives to increase the implementation of interoperable EMRs will markedly improve the availability of clinical data for genomics research.