Susan Weber

All Publications

• Targeting Repetitive Laboratory Testing with Electronic Health Records-Embedded Predictive Decision Support: A Pre-Implementation Study. Clinical biochemistry Rabbani, N., Ma, S. P., Li, R. C., Winget, M., Weber, S., Boosi, S., Pham, T. D., Svec, D., Shieh, L., Chen, J. H. 2023

  Abstract

  INTRODUCTION: Unnecessary laboratory testing contributes to patient morbidity and healthcare waste. Despite prior attempts at curbing such overutilization, there remains opportunity for improvement using novel data-driven approaches. This study presents the development and early evaluation of a clinical decision support tool that uses a predictive model to help providers reduce low-yield, repetitive laboratory testing in hospitalized patients.METHODS: We developed an EHR-embedded SMART on FHIR application that utilizes a laboratory test result prediction model based on historical laboratory data. A combination of semi-structured physician interviews, usability testing, and quantitative analysis on retrospective laboratory data were used to inform the tool's development and evaluate its acceptability and potential clinical impact.KEY RESULTS: Physicians identified culture and lack of awareness of repeat orders as key drivers for overuse of inpatient blood testing. Users expressed an openness to a lab prediction model and 13/15 physicians believed the tool would alter their ordering practices. The application received a median System Usability Scale score of 75, corresponding to the 75th percentile of software tools. On average, physicians desired a prediction certainty of 85% before discontinuing a routine recurring laboratory order and a higher certainty of 90% before being alerted. Simulation on historical lab data indicates that filtering based on accepted thresholds could have reduced 22% of repeat chemistry panels.CONCLUSIONS: The use of a predictive algorithm as a means to calculate the utility of a diagnostic test is a promising paradigm for curbing laboratory test overutilization. An EHR-embedded clinical decision support tool employing such a model is a novel and acceptable intervention with the potential to reduce low-yield, repetitive laboratory testing.

  View details for DOI 10.1016/j.clinbiochem.2023.01.002

  View details for PubMedID 36623759

• Autoantibodies in chronic hepatitis C virus infection: impact on clinical outcomes and extrahepatic manifestations. BMJ open gastroenterology Gilman, A. J., Le, A. K., Zhao, C. n., Hoang, J. n., Yasukawa, L. A., Weber, S. C., Vierling, J. M., Nguyen, M. H. 2018; 5 (1): e000203

  Abstract

  To examine the role that autoantibodies (auto-abs) play in chronic hepatitis C virus (HCV) regarding demographics, presence of extrahepatic manifestations and long-term outcomes in a large US cohort.Auto-abs have been reported to be prevalent in patients with chronic HCV infection, but data on the natural history of these patients are limited.The study included 1556 consecutive patients with HCV without concurrent HIV and/or HBV who had testing for antinuclear antibody (ANA), antimitochondrial antibody (AMA), antismooth muscle antibody (ASMA) and/or antiliver kidney microsomal antibody (LKM). Primary outcomes included development of cirrhosis, hepatic decompensations, hepatocellular carcinoma (HCC), mortality and/or sustained virological response (SVR) to antiviral therapy.A total of 388 patients tested positive for any auto-ab (ANA 21.8%, ASMA 13.3%, AMA 2.2% and LKM 1.2%). Patients who tested positive versus negative were more likely to be women (29.3% vs 20.9%, p<0.001) and less likely to achieve SVR with most treated patients receiving interferon-based therapies (37.2% vs 47.1%, p=0.031). There was no difference between groups for baseline laboratory data, disease state or rate of extrahepatic manifestations (42.8% vs 45.0%, p=0.44). Kaplan-Meier analysis revealed no statistically significant difference between groups for the 10-year development of cirrhosis, hepatic decompensations, HCC nor survival. Furthermore, auto-ab positivity was only found to be a predictor for a lower rate of SVR on multivariate analysis (adjusted OR=1.61, 95 %  CI 1.00 to 2.58, p=0.048).In our cohort, auto-ab positivity was common, especially in women, and predicted a lower rate of SVR but otherwise had no impact on the natural history of chronic HCV or presence of extrahepatic manifestations.

  View details for PubMedID 29755758

  View details for PubMedCentralID PMC5942460

• Rate of hepatocellular carcinoma surveillance remains low for a large, real-life cohort of patients with hepatitis C cirrhosis. BMJ open gastroenterology Tran, S. A., Le, A. n., Zhao, C. n., Hoang, J. n., Yasukawa, L. A., Weber, S. n., Henry, L. n., Nguyen, M. H. 2018; 5 (1): e000192

  Abstract

  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.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.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.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.

  View details for PubMedID 29607053

• Barriers to care for chronic hepatitis C in the direct-acting antiviral era: a single-centre experience. BMJ open gastroenterology Nguyen, P. n., Vutien, P. n., Hoang, J. n., Trinh, S. n., Le, A. n., Yasukawa, L. A., Weber, S. n., Henry, L. n., Nguyen, M. H. 2017; 4 (1): e000181

  Abstract

  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.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.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).Despite higher rates of medical comorbidities in the DAA era, considerable treatment challenges remain including cost, loss to follow-up and ethnic disparities.

  View details for PubMedID 29333275

• Synergistic drug combinations from electronic health records and gene expression. Journal of the American Medical Informatics Association Low, Y. S., Daugherty, A. C., Schroeder, E. A., Chen, W., Seto, T., Weber, S., Lim, M., Hastie, T., Mathur, M., Desai, M., Farrington, C., Radin, A. A., Sirota, M., Kenkare, P., Thompson, C. A., Yu, P. P., Gomez, S. L., Sledge, G. W., Kurian, A. W., Shah, N. H. 2016

  Abstract

  Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.

  View details for DOI 10.1093/jamia/ocw161

  View details for PubMedID 27940607

• Autoimmune Hemolytic Anemia Confers Risk of Thromboembolism That Is Not Attributable to Usual Thrombosis Risk Factors: A Longitudinal, Retrospective Cohort Study Using the "Stride" Database Chen, E. C., Loftus, P. D., Weber, S. C., Hoang, N., Gilbert, J., Kummar, S. AMER SOC HEMATOLOGY. 2016

  View details for Web of Science ID 000394446807117

• LACTATION AND FUTURE CARDIOVASCULAR DISEASE IN WOMEN: AN UNDER APPRECIATED TARGET FOR PREVENTION Rajaei, S., Tsai, S. A., Rigdon, J., Weber, S., Mathur, M., Tremmel, J. ELSEVIER SCIENCE INC. 2016: 1859

  View details for DOI 10.1016/S0735-1097(16)31860-5

  View details for Web of Science ID 000375188702705

• Differential Progression to Cirrhosis and Hepatic Decompensation in Asian Americans (AA) and Non-Asian (NA) Patients with Chronic Hepatitis C (CHC) Le, A. K., Nguyen, N. H., Zhao, C., Hoang, J. K., Chang, C. Y., Jin, M., Nguyen, P., Nguyen, P. T., Le, R. H., Jin, M. Q., Nguyen, L., Yasukawa, L., Zhang, J. Q., Weber, S. C., Nguyen, M. H. WILEY-BLACKWELL. 2015: 1110A–1111A

  View details for Web of Science ID 000368375404094

• Racial Differences in the Presentation and the Incidence of Hepatocellular Carcinoma (HCC) in a Large Cohort of Patients with Cirrhosis in the United States Nguyen, L. H., Zhao, C., Nguyen, N. H., Hoang, J. K., Nguyen, M. D., Le, R. H., Nguyen, P. T., Lin, D., Vu, V. D., Yasukawa, L., Weber, S. C., Nguyen, M. H. WILEY-BLACKWELL. 2015: 405A–406A

  View details for Web of Science ID 000368375401270

• Higher Incidence of Hepatocellular Carcinoma (HCC) in Patients with Hepatitis B Virus (HBV) -related Liver Cirrhosis (LC) Compared to Hepatitis C Virus (HCV)-LC and Non-Viral LC Zhao, C., Nguyen, N. H., Nguyen, M. D., Hoang, J. K., Nguyen, L. H., Le, R. H., Nguyen, P. T., Vu, V. D., Lin, D., Yasukawa, L., Weber, S. C., Nguyen, M. H. WILEY-BLACKWELL. 2015: 440A

  View details for Web of Science ID 000368375401341

• Low HCV Treatment for Chronic Hepatitis C (CHC) Patients in Pre-Protease Inhibitor (PI) and Post-PI/Pre-Direct Acting Antiviral (DAA) Eras Compared to Post-DAA Era, Especially in African Americans: Analysis of A Large Real-World Cohort of 7105 Patients Nguyen, P. T., Nguyen, N. H., Hoang, J. K., Zhao, C., Le, A. K., Chang, C. Y., Le, R. H., Nguyen, M. D., Jin, M., Weber, S. C., Yasukawa, L., Nguyen, M. H. WILEY-BLACKWELL. 2015: 1094A

  View details for Web of Science ID 000368375404063

• Risk Differences of Hepatocellular Carcinoma (HCC) Among Patients with Chronic Hepatitis C (CHC) of Different Viral Genotypes: a Historical Cohort Study in the United States Jin, M., Zhao, C., Hoang, J. K., Nguyen, N. H., Le, A. K., Chang, C. Y., Le, R. H., Kutsenko, A., Yasukawa, L., Zhang, J. Q., Weber, S. C., Nguyen, M. H. WILEY-BLACKWELL. 2015: 392A

  View details for Web of Science ID 000368375401243

• Progression to liver cirrhosis in patients with chronic hepatitis C (CHC) in a large United States cohort: a natural history study Nguyen, N. H., Hoang, J. K., Le, A. K., Zhao, C., Chang, C. Y., Le, R. H., Jin, M., Kutsenko, A., Yasukawa, L., Zhang, J. Q., Weber, S. C., Nguyen, M. H. WILEY-BLACKWELL. 2015: 1089A

  View details for Web of Science ID 000368375404053

• Higher Risk for Hepatocellular Carcinoma (HCC) in Asian Americans (AA) with Chronic Hepatitis C (CHC) compared to Non-Asians (NA), Regardless of Cirrhosis Status Le, R. H., Nguyen, N. H., Hoang, J. K., Le, A. K., Nguyen, P. T., Zhao, C., Chang, C. Y., Jin, M., Jin, M. Q., Wong, K. N., Yasukawa, L., Zhang, J. Q., Weber, S. C., Nguyen, M. H. WILEY-BLACKWELL. 2015: 406A–407A

  View details for Web of Science ID 000368375401272

• Disease risk factors identified through shared genetic architecture and electronic medical records. Science translational medicine Li, L., Ruau, D. J., Patel, C. J., Weber, S. C., Chen, R., Tatonetti, N. P., Dudley, J. T., Butte, A. J. 2014; 6 (234): 234ra57-?

  Abstract

  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.

  View details for DOI 10.1126/scitranslmed.3007191

  View details for PubMedID 24786325

• Disease Risk Factors Identified Through Shared Genetic Architecture and Electronic Medical Records SCIENCE TRANSLATIONAL MEDICINE Li, L., Ruau, D. J., Patel, C. J., Weber, S. C., Chen, R., Tatonetti, N. P., Dudley, J. T., Butte, A. J. 2014; 6 (234)

  Abstract

  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.

  View details for DOI 10.1126/scitranslmed.3007191

  View details for Web of Science ID 000335516100010

  View details for PubMedID 24786325

• Stanford-NIH Pain Registry: Open source platform for large-scale longitudinal assessment of clinical data and patient-reported outcomes American Academy of Pain Medicine’s 30th Annual Meeting Kao, M., Cook, K., Olson, G., Pacht, T., Darnall, B., Weber, S., Mackey, S. 2014
• Breast cancer treatment across health care systems: linking electronic medical records and state registry data to enable outcomes research. Cancer Kurian, A. W., Mitani, A., Desai, M., Yu, P. P., Seto, T., Weber, S. C., Olson, C., Kenkare, P., Gomez, S. L., de Bruin, M. A., Horst, K., Belkora, J., May, S. G., Frosch, D. L., Blayney, D. W., Luft, H. S., Das, A. K. 2014; 120 (1): 103-111

  Abstract

  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.Diagnostic test and treatment data were extracted from the EMRs of all patients with breast cancer treated between 2000 and 2010 in 2 independent California institutions: a community-based practice (Palo Alto Medical Foundation; "Community") and an academic medical center (Stanford University; "University"). The authors incorporated records from the population-based California Cancer Registry and then linked EMR-California Cancer Registry data sets of Community and University patients.The authors initially identified 8210 University patients and 5770 Community patients; linked data sets revealed a 16% patient overlap, yielding 12,109 unique patients. The percentage of all Community patients, but not University patients, treated at both institutions increased with worsening cancer prognostic factors. Before linking the data sets, Community patients appeared to receive less intervention than University patients (mastectomy: 37.6% vs 43.2%; chemotherapy: 35% vs 41.7%; magnetic resonance imaging: 10% vs 29.3%; and genetic testing: 2.5% vs 9.2%). Linked Community and University data sets revealed that patients treated at both institutions received substantially more interventions (mastectomy: 55.8%; chemotherapy: 47.2%; magnetic resonance imaging: 38.9%; and genetic testing: 10.9% [P < .001 for each 3-way institutional comparison]).Data linkage identified 16% of patients who were treated in 2 health care systems and who, despite comparable prognostic factors, received far more intensive treatment than others. By integrating complementary data from EMRs and population-based registries, a more comprehensive understanding of breast cancer care and factors that drive treatment use was obtained.

  View details for DOI 10.1002/cncr.28395

  View details for PubMedID 24101577

  View details for PubMedCentralID PMC3867595

• Breast Cancer Treatment Across Health Care Systems CANCER Kurian, A. W., Mitani, A., Desai, M., Yu, P. P., Seto, T., Weber, S. C., Olson, C., Kenkare, P., Gomez, S. L., de Bruin, M. A., Horst, K., Belkora, J., May, S. G., Frosch, D. L., Blayney, D. W., Luft, H. S., Das, A. K. 2014; 120 (1): 103-111

  Abstract

  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.Diagnostic test and treatment data were extracted from the EMRs of all patients with breast cancer treated between 2000 and 2010 in 2 independent California institutions: a community-based practice (Palo Alto Medical Foundation; "Community") and an academic medical center (Stanford University; "University"). The authors incorporated records from the population-based California Cancer Registry and then linked EMR-California Cancer Registry data sets of Community and University patients.The authors initially identified 8210 University patients and 5770 Community patients; linked data sets revealed a 16% patient overlap, yielding 12,109 unique patients. The percentage of all Community patients, but not University patients, treated at both institutions increased with worsening cancer prognostic factors. Before linking the data sets, Community patients appeared to receive less intervention than University patients (mastectomy: 37.6% vs 43.2%; chemotherapy: 35% vs 41.7%; magnetic resonance imaging: 10% vs 29.3%; and genetic testing: 2.5% vs 9.2%). Linked Community and University data sets revealed that patients treated at both institutions received substantially more interventions (mastectomy: 55.8%; chemotherapy: 47.2%; magnetic resonance imaging: 38.9%; and genetic testing: 10.9% [P < .001 for each 3-way institutional comparison]).Data linkage identified 16% of patients who were treated in 2 health care systems and who, despite comparable prognostic factors, received far more intensive treatment than others. By integrating complementary data from EMRs and population-based registries, a more comprehensive understanding of breast cancer care and factors that drive treatment use was obtained.

  View details for DOI 10.1002/cncr.28395

  View details for Web of Science ID 000328443000017

  View details for PubMedCentralID PMC3867595

• Stanford-NIH Pain Registry: Catalyzing the rate-limiting step of big data psychometrics with item-response theory and advanced computerized adaptive testing American Academy of Pain Medicine’s 30th Annual Meeting Kao, M., Cook, K., Olson, G., Pacht, T., Darnall, B., Weber, S. C., Mackey, S. 2014
• Transdisciplinary translational science and the case of preterm birth JOURNAL OF PERINATOLOGY Stevenson, D. K., Shaw, G. M., Wise, P. H., Norton, M. E., Druzin, M. L., Valantine, H. A., McFarland, D. A. 2013; 33 (4): 251-258

  Abstract

  Medical researchers have called for new forms of translational science that can solve complex medical problems. Mainstream science has made complementary calls for heterogeneous teams of collaborators who conduct transdisciplinary research so as to solve complex social problems. Is transdisciplinary translational science what the medical community needs? What challenges must the medical community overcome to successfully implement this new form of translational science? This article makes several contributions. First, it clarifies the concept of transdisciplinary research and distinguishes it from other forms of collaboration. Second, it presents an example of a complex medical problem and a concrete effort to solve it through transdisciplinary collaboration: for example, the problem of preterm birth and the March of Dimes effort to form a transdisciplinary research center that synthesizes knowledge on it. The presentation of this example grounds discussion on new medical research models and reveals potential means by which they can be judged and evaluated. Third, this article identifies the challenges to forming transdisciplines and the practices that overcome them. Departments, universities and disciplines tend to form intellectual silos and adopt reductionist approaches. Forming a more integrated (or 'constructionist'), problem-based science reflective of transdisciplinary research requires the adoption of novel practices to overcome these obstacles.

  View details for DOI 10.1038/jp.2012.133

  View details for PubMedID 23079774

• Systematic identification of risk factors for Alzheimer's disease through shared genetic architecture and electronic medical records. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Li, L., Ruau, D., Chen, R., Weber, S., Butte, A. J. 2013: 224-235

  Abstract

  Alzheimer's 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.

  View details for PubMedID 23424127

• Row-Filtering with Dynamic SQL in Support of Compliant EHR Data Marts AMIA CRI Summit on Clinical Research Informatics Weber, S., Srinivas, R., Zhou, V., Ferris, T., Lowe, H. 2013: 278
• A simple heuristic for blindfolded record linkage JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Weber, S. C., Lowe, H., Das, A., Ferris, T. 2012; 19 (E1): E157-E161

  Abstract

  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.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 patient's 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.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 patient's full names and date of birth has a sensitivity of only 87% and SSN has sensitivity 86%.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.

  View details for DOI 10.1136/amiajnl-2011-000329

  View details for PubMedID 22298567

• Clinical Research Alerting for Early Septic Shock Detection 2012 Summit on Clinical Research Informatics S. Weber, H. Lowe, S. Malunjkar, V. Ojha, R. Pearl, J. Quinn 2012
• Oncoshare: Lessons learned from building an integrated multi-institutional database for comparative effectiveness research Proceedings of the AMIA 2012 Annual Symposium SC Weber, T Seto, C Olson, P Kenkare, A Kurian, A Das 2012
• Use of RxNorm and SNOMET-CT® to Support the Use of Medication Information in Research Patient Cohort Searching AMIA Annu Symp Proc Hernandez P, Podchiyska T, Ferris TA, Weber S, Lowe HJ 2011: 106
• Hash-Based Algorithmic Linkage of Patient Records in De-identified Multi-site Patient Research Registries AMIA Annu Symp Proc Weber S, Lowe HJ, Olson G, Seto T, Ferris TA, Das A, Kurian A, Olson C, Kenkare P 2011; CRI: 81
• A Model for Efficient Review of Clinical Data by Researchers within the STRIDE Clinical Data Warehouse AMIA Annu Symp Proc Lowe HJ, Weber S, Ramamoorthy N, Ferris TA, Hernandez P 2011; CRI: 34
• Implementing a Real-time Complex Event Stream Processing System to Help Identify Potential Participants in Clinical and Translational Research Studies AMIA Annu Symp Proc Weber S, Lowe HJ, Malunjkar S, Quinn J 2010: 472
• Managing Medical Vocabulary Updates in a Clinical Data Warehouse: An RxNorm Case Study. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Podchiyska, T., Hernandez, P., Ferris, T., Weber, S., Lowe, H. J. 2010; 2010: 477-481

  Abstract

  Use of terminology standards facilitates aggregating data from multiple sources for information retrieval, exchange and analysis. However, medical vocabularies are continuously updated and incorporating those changes consistently into clinical data warehouses requires rigorous methodology. To integrate pharmacy data from two hospital pharmacy information systems the Stanford Translational Research Integrated Database Environment (STRIDE) project mapped medication orders to RxNorm content using the RxNorm drug model. In order to keep the data relevant and up-to-date, we developed a strategy for updating to RxNorm, while preserving the original meaning and mapping of the legacy data. This case study discusses managing the vocabulary update by following the RxNorm content maintenance strategy and supplementing it with operations to retain access to its drug model information.

  View details for PubMedID 21347024

• Self-Service Support for Research Patient Cohort Identification and Review of Clinical Data in the STRIDE Clinical Data Warehouse AMIA Annu Symp Proc Lowe HJ, Weber S, Ferris T, Hernandez P 2010; CRI
• Automated mapping of pharmacy orders from two electronic health record systems to RxNorm within the STRIDE clinical data warehouse. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Hernandez, P., Podchiyska, T., Weber, S., Ferris, T., Lowe, H. 2009; 2009: 244-248

  Abstract

  The Stanford Translational Research Integrated Database Environment (STRIDE) clinical data warehouse integrates medication information from two Stanford hospitals that use different drug representation systems. To merge this pharmacy data into a single, standards-based model supporting research we developed an algorithm to map HL7 pharmacy orders to RxNorm concepts. A formal evaluation of this algorithm on 1.5 million pharmacy orders showed that the system could accurately assign pharmacy orders in over 96% of cases. This paper describes the algorithm and discusses some of the causes of failures in mapping to RxNorm.

  View details for PubMedID 20351858

• STRIDE--An integrated standards-based translational research informatics platform. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Lowe, H. J., Ferris, T. A., Hernandez, P. M., Weber, S. C. 2009; 2009: 391-395

  Abstract

  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. STRIDE's 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 University's CTSA (Clinical and Translational Science Award) Informatics Program.

  View details for PubMedID 20351886

• Novel integration of hospital electronic medical records and gene expression measurements to identify genetic markers of maturation. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Chen, D. P., Weber, S. C., Constantinou, P. S., Ferris, T. A., Lowe, H. J., Butte, A. J. 2008: 243-254

  Abstract

  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 organism's 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).

  View details for PubMedID 18229690

• Clinical arrays of laboratory measures, or "clinarrays", built from an electronic health record enable disease subtyping by severity. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Chen, D. P., Weber, S. C., Constantinou, P. S., Ferris, T. A., Lowe, H. J., Butte, A. J. 2007: 115-119

  Abstract

  The severity of diseases has often been assigned by direct observation of a patient and by pathological examination after symptoms have appeared. As we move into the genomic era, the ability to predict disease severity prior to manifestation has improved dramatically due to genomic sequencing and analysis of gene expression microarrays. However, as the severity of diseases can be exacerbated by non genetic factors, the ability to predict disease severity by examining gene expression alone may be inadequate. We propose the creation of a "clinarray" to examine phenotypic expression in the form of clinical laboratory measurements. We demonstrate that the clinarray can be used to distinguish between the severities of patients with cystic fibrosis and those with Crohn's disease by applying unsupervised clustering methods that have been previously applied to microarrays.

  View details for PubMedID 18693809