Susan C. Weber

STRIDE--An integrated standards-based translational research informatics platform.

STRIDE (Stanford Translational Research Integrated Database Environment) is a research and development project at Stanford University to create a standards-based informatics platform supporting clinical and translational research. STRIDE consists of three integrated components: a clinical data warehouse, based on the HL7 Reference Information Model (RIM), containing clinical information on over 1.3 million pediatric and adult patients cared for at Stanford University Medical Center since 1995; an application development framework for building research data management applications on the STRIDE platform and a biospecimen data management system. STRIDEs semantic model uses standardized terminologies, such as SNOMED, RxNorm, ICD and CPT, to represent important biomedical concepts and their relationships. The system is in daily use at Stanford and is an important component of Stanford Universitys CTSA (Clinical and Translational Science Award) Informatics Program.on behalf of the American Heart Association Statistics Committee and Stroke Statistics Nathan D. Wong, Daniel Woo and Melanie B. Turner Elsayed Z. Soliman, Paul D. Sorlie, Nona Sotoodehnia, Tanya N. Turan, Salim S. Virani, Claudia S. Moy, Dariush Mozaffarian, Michael E. Mussolino, Graham Nichol, Nina P. Paynter, Lynda D. Lisabeth, Diane M. Makuc, Gregory M. Marcus, Ariane Marelli, David B. Matchar, Lichtman, Virginia J. Howard, Brett M. Kissela, Steven J. Kittner, Daniel T. Lackland, Judith H. Caroline S. Fox, Heather J. Fullerton, Cathleen Gillespie, Susan M. Hailpern, John A. Heit, Benjamin, Jarett D. Berry, William B. Borden, Dawn M. Bravata, Shifan Dai, Earl S. Ford, Writing Group Members, Véronique L. Roger, Alan S. Go, Donald M. Lloyd-Jones, Emelia J. Association 2012 Update : A Report From the American Heart −− Heart Disease and Stroke StatisticsHeart Disease, Stroke and other Cardiovascular Diseases • Cardiovascular disease is the leading global cause of death, accounting for 17.3 million deaths per year, a number that is expected to grow to more than 23.6 million by 2030. • In 2008, cardiovascular deaths represented 30 percent of all global deaths, with 80 percent of those deaths taking place in lowand middle-income countries. • Nearly 787,000 people in the U.S. died from heart disease, stroke and other cardiovascular diseases in 2011. That’s about one of every three deaths in America. • About 2,150 Americans die each day from these diseases, one every 40 seconds. • Cardiovascular diseases claim more lives than all forms of cancer combined. • About 85.6 million Americans are living with some form of cardiovascular disease or the after-effects of stroke. • Direct and indirect costs of cardiovascular diseases and stroke total more than

A simple heuristic for blindfolded record linkage.

320.1 billion. That includes health expenditures and lost productivity. • Nearly half of all African-American adults have some form of cardiovascular disease, 48 percent of women and 46 percent of men. • Heart disease is the No. 1 cause of death in the world and the leading cause of death in the United States, killing over 375,000 Americans a year. • Heart disease accounts for 1 in 7 deaths in the U.S. • Someone in the U.S. dies from heart disease about once every 90 seconds.Author(s): Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; de Ferranti, Sarah; Despres, Jean-Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Judd, Suzanne E; Kissela, Brett M; Lackland, Daniel T; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Matchar, David B; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Willey, Joshua Z; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Disease Risk Factors Identified Through Shared Genetic Architecture and Electronic Medical Records

OBJECTIVES To address the challenge of balancing privacy with the need to create cross-site research registry records on individual patients, while matching the data for a given patient as he or she moves between participating sites. To evaluate the strategy of generating anonymous identifiers based on real identifiers in such a way that the chances of a shared patient being accurately identified were maximized, and the chances of incorrectly joining two records belonging to different people were minimized. METHODS Our hypothesis was that most variation in names occurs after the first two letters, and that date of birth is highly reliable, so a single match variable consisting of a hashed string built from the first two letters of the patients first and last names plus their date of birth would have the desired characteristics. We compared and contrasted the match algorithm characteristics (rate of false positive v. rate of false negative) for our chosen variable against both Social Security Numbers and full names. RESULTS In a data set of 19 000 records, a derived match variable consisting of a 2-character prefix from both first and last names combined with date of birth has a 97% sensitivity; by contrast, an anonymized identifier based on the patients full names and date of birth has a sensitivity of only 87% and SSN has sensitivity 86%. CONCLUSION The approach we describe is most useful in situations where privacy policies preclude the full exchange of the identifiers required by more sophisticated and sensitive linkage algorithms. For data sets of sufficiently high quality this effective approach, while producing a lower rate of matching than more complex algorithms, has the merit of being easy to explain to institutional review boards, adheres to the minimum necessary rule of the HIPAA privacy rule, and is faster and less cumbersome to implement than a full probabilistic linkage.

Breast cancer treatment across health care systems: Linking electronic medical records and state registry data to enable outcomes research

Twenty-six traits were associated with diseases through similarities in genetic variation and validated from electronic medical records. Medicine by Association As data get bigger, the challenge is to extract human-sized conclusions that we can comprehend and use. Li and colleagues have done exactly this by exploiting VARIMED, a hand-curated database of single-nucleotide polymorphisms (SNP) associated with diseases or clinical parameters such as cholesterol level and smoking status, extracted from the literature. By finding pairs of diseases and these nondisease clinical parameters (which they call traits) that are associated with the same SNP variants, they construct hypotheses that the traits could be prognostic markers or risk factors for the disease. Ninety-four of the 120 pairs they identified were known and published in the literature; 26 pairs were previously undescribed. The known associations tended to fall into groups: solid organ cancer with prostate-specific antigen (PSA) and autoimmune disorders with major histocompatibility complex (MHC)–related molecules, for example. The authors were able to validate several of the newly associated traits and diseases by extracting data from electronic medical records from three clinical centers: They found that patients with abnormal mean corpuscular volume were more than three times more likely to receive a diagnosis of acute lymphoblastic leukemia within a year than those with normal values. Similarly, abnormal magnesium levels predicted a greater risk of developing gastric cancer within a year, and abnormally high PSA levels predicted a doubling in the odds of receiving a lung cancer diagnosis within a year. This all in silico discovery and validation of potential risk factors for disease present an important hypothesis-generating tool for medicine. Prospective clinical trials will test whether these clinical traits can serve as informative diagnostic and prognostic markers. Genome-wide association studies have identified genetic variants for thousands of diseases and traits. We evaluated the relationships between specific risk factors (for example, blood cholesterol level) and diseases on the basis of their shared genetic architecture in a comprehensive human disease–single-nucleotide polymorphism association database (VARIMED), analyzing the findings from 8962 published association studies. Similarity between traits and diseases was statistically evaluated on the basis of their association with shared gene variants. We identified 120 disease-trait pairs that were statistically similar, and of these, we tested and validated five previously unknown disease-trait associations by searching electronic medical records (EMRs) from three independent medical centers for evidence of the trait appearing in patients within 1 year of first diagnosis of the disease. We validated that the mean corpuscular volume is elevated before diagnosis of acute lymphoblastic leukemia; both have associated variants in the gene IKZF1. Platelet count is decreased before diagnosis of alcohol dependence; both are associated with variants in the gene C12orf51. Alkaline phosphatase level is elevated in patients with venous thromboembolism; both share variants in ABO. Similarly, we found that prostate-specific antigen and serum magnesium levels were altered before the diagnosis of lung cancer and gastric cancer, respectively. Disease-trait associations identify traits that could serve as future prognostics, if validated through EMR and subsequent prospective trials.

NOVEL INTEGRATION OF HOSPITAL ELECTRONIC MEDICAL RECORDS AND GENE EXPRESSION MEASUREMENTS TO IDENTIFY GENETIC MARKERS OF MATURATION

Understanding of cancer outcomes is limited by data fragmentation. In the current study, the authors analyzed the information yielded by integrating breast cancer data from 3 sources: electronic medical records (EMRs) from 2 health care systems and the state registry.

Systematic identification of risk factors for Alzheimer's disease through shared genetic architecture and electronic medical records.

Traditionally, the elucidation of genes involved in maturation and aging has been studied in a temporal fashion by examining gene expression at different time points in an organisms life as well as by knocking out, knocking in, and mutating genes thought to be involved. Here, we propose an in silico method to combine clinical electronic medical record (EMR) data and gene expression measurements in the context of disease to identify genes that may be involved in the process of human maturation and aging. First we show that absolute lymphocyte count may serve as a biomarker for maturation by using statistical methods to compare trends among different clinical laboratory tests in response to an increase in age. We then propose using the rate of decay for absolute lymphocyte count across 12 diseases as a proxy for differences in aging. We correlate the differing rates with gene expression across the same diseases to find maturation/aging related genes. Among the 53 genes with strongest correlations between expression profile and change in rate of decay, we found genes previously implicated in the process of aging, including MGMT (DNA repair), TERF2 (telomere stability), POLD1 (DNA replication and repair), and POLG (mtDNA replication).

Ethnic disparities in progression to advanced liver disease and overall survival in patients with chronic hepatitis C: impact of a sustained virological response

Alzheimers disease (AD) is one of the leading causes of death for older people in US with rapidly increasing incidence. AD irreversibly and progressively damages the brain, but there are treatments in clinical trials to potentially slow the development of AD. We hypothesize that the presence of clinical traits, sharing common genetic variants with AD, could be used as a non-invasive means to predict AD or trigger for administration of preventative therapeutics. We developed a method to compare the genetic architecture between AD and traits from prior GWAS studies. Six clinical traits were significantly associated with AD, capturing 5 known risk factors and 1 novel association: erythrocyte sedimentation rate (ESR). The association of ESR with AD was then validated using Electronic Medical Records (EMR) collected from Stanford Hospital and Clinics. We found that female patients and with abnormally elevated ESR were significantly associated with higher risk of AD diagnosis (OR: 1.85 [1.32-2.61], p=0.003), within 1 year prior to AD diagnosis (OR: 2.31 [1.06-5.01], p=0.032), and within 1 year after AD diagnosis (OR: 3.49 [1.93-6.31], p<0.0001). Additionally, significantly higher ESR values persist for all time courses analyzed. Our results suggest that ESR should be tested in a specific longitudinal study for association with AD diagnosis, and if positive, could be used as a prognostic marker.

Rate of hepatocellular carcinoma surveillance remains low for a large, real-life cohort of patients with hepatitis C cirrhosis

Chronic hepatitis C (CHC) can lead to cirrhosis and hepatocellular carcinoma (HCC). A sustained virological response (SVR) is associated with improved outcomes, however, its impact on different ethnic groups is unknown.

Barriers to care for chronic hepatitis C in the direct-acting antiviral era: a single-centre experience

Background In patients with chronic hepatitis C (CHC) cirrhosis, imaging for hepatocellular carcinoma (HCC) is recommended every 6 months to maximise eligibility for curative treatment. The aim was to determine the adherence rate and outcomes among patients with CHC cirrhosis and whether the adherence rate has improved over time. Methods Retrospective cohort study of patients with CHC cirrhosis (n=2366) monitored for ≥1 year at Stanford University Medical Center between January 2001 and August 2015. Results Overall demographics: mean age 54; 62.3% men; 48.3% Caucasian. 24.4% adherent to imaging every 6 months per European Association for the Study of the Liver 2000 and American Association for the Study of Liver Diseases (AASLD) 2011 criteria and 44% at least every 12 months per AASLD 2005 criteria. No significant change in adherence before and after 2011. Predictors of multivariable analysis of adherence were age >54 (OR 1.74, p<0.0001), Asian ethnicity (OR 2.23, p<0.0001), liver decompensation (OR 2.40, p<0.0001) and having ≥2 clinical visits per year (OR 1.33, p=0.01). During follow-up, 9.6% were diagnosed with HCC. Adherent patients were more likely to have smaller tumours (2.3 vs 3.3 cm, p=0.0020), be within the Milan criteria for liver transplants (73.2% vs 54.8%, p=0.006) and receive curative HCC treatment (43.6% vs 24.0%, p=0.005). On multivariable analysis, curative treatment (HR 0.32, p=0.001) and every 6-month imaging (HR 0.34, p=0.005), but not every 6–12 month imaging, were associated with reduced risk of mortality. Conclusions Adherence to HCC surveillance continues to be poor. Adherent patients with HCC were more likely to undergo curative treatment and have better survival. Research understanding barriers to surveillance is needed.

Synergistic drug combinations from electronic health records and gene expression

Background Cure rates for chronic hepatitis C have improved dramatically with direct-acting antivirals (DAAs), but treatment barriers remain. We aimed to compare treatment initiation rates and barriers across both interferon-based and DAA-based eras. Methods We conducted a retrospective cohort study of all patients with chronic hepatitis C seen at an academic hepatology clinic from 1999 to 2016. Patients were identified to have chronic hepatitis C by the International Classification of Diseases, Ninth Revision codes, and the diagnosis was validated by chart review. Patients were excluded if they did not have at least one visit in hepatology clinic, were under 18 years old or had prior treatment with DAA therapy. Patients were placed in the DAA group if they were seen after 1 January 2014 and had not yet achieved virological cure with prior treatment. All others were considered in the interferon group. Results 3202 patients were included (interferon era: n=2688; DAA era: n=514). Despite higher rates of decompensated cirrhosis and medical comorbidities in the DAA era, treatment and sustained virological response rates increased significantly when compared with the interferon era (76.7% vs 22.3%, P<0.001; 88.8% vs 55%, P<0.001, respectively). Lack of follow-up remained a significant reason for non-treatment in both groups (DAA era=24% and interferon era=45%). An additional 8% of patients in the DAA era were not treated due to insurance or issues with cost. In the DAA era, African-Americans, compared with Caucasians, had significantly lower odds of being treated (OR=0.37, P=0.02). Conclusions Despite higher rates of medical comorbidities in the DAA era, considerable treatment challenges remain including cost, loss to follow-up and ethnic disparities.