Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study.

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 âˆ¼22% 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.

Fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection in patients with HER2-positive breast cancer: A multidisciplinary approach.

Pertuzumab plus trastuzumab, administered intravenously (IV) with chemotherapy, is standard treatment for HER2-positive metastatic or high-risk early breast cancer. Pertuzumab and trastuzumab are administered over 1-2.5 h traditionally; however, the need for IV infusions places a strain on medical centers with respect to scheduling, preparation, and administration. A novel fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection (PHESGO®, PH FDC SC) can be administered in approximately 5-8 min. PH FDC SC was non-inferior to IV pertuzumab plus trastuzumab in terms of pertuzumab and trastuzumab serum levels in the phase III FeDeriCa study, which enrolled 500 patients with HER2-positive early breast cancer. Total pathologic complete response rates were comparable after PH FDC SC (59.7%) or IV pertuzumab plus trastuzumab (59.5%), as was the incidence of grade ≥3 (48.8% vs 52.8%) and serious adverse events (16.1% vs 17.9%). The results of a phase II clinical trial (PHranceSCa) showed that a majority of patients (85%) preferred PH FDC SC treatment over IV pertuzumab plus trastuzumab. A US multicenter expanded access study (NCT04395508) is evaluating the safety of PH FDC SC administered at home by nurse providers in patients receiving maintenance HER2-targeted therapy every 3 weeks. This product takes much less time to administer than IV pertuzumab-trastuzumab and has the potential to alleviate time constraints for patients and busy clinics. In this review we provide an overview of PH FDC SC, and discuss our experience in preparing and administering this product to patients with HER2-positive breast cancer during clinical trials.

Autoantibodies in chronic hepatitis C virus infection: impact on clinical outcomes and extrahepatic manifestations.

GOALS: 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. BACKGROUND: 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. STUDY: 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. RESULTS: 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). CONCLUSIONS: 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.

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

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.

OBJECTIVE: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.

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

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.

Disease risk factors identified through shared genetic architecture and electronic medical records.

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.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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]). CONCLUSIONS: 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.

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

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.

A simple heuristic for blindfolded record linkage.

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 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. 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 patient's 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.

Oncoshare: lessons learned from building an integrated multi-institutional database for comparative effectiveness research.

Comparative effectiveness research (CER) using observational data requires informatics methods for the extraction, standardization, sharing, and integration of data derived from a variety of electronic sources. In the Oncoshare project, we have developed such methods as part of a collaborative multi-institutional CER study of patterns, predictors, and outcome of breast cancer care. In this paper, we present an evaluation of the approaches we undertook and the lessons we learned in building and validating the Oncoshare data resource. Specifically, we determined that 1) the state or regional cancer registry makes the most efficient starting point for determining inclusion of subjects; 2) the data dictionary should be based on existing registry standards, such as Surveillance, Epidemiology and End Results (SEER), when applicable; 3) the Social Security Administration Death Master File (SSA DMF), rather than clinical resources, provides standardized ascertainment of mortality outcomes; and 4) CER database development efforts, despite the immediate availability of electronic data, may take as long as two years to produce validated, reliable data for research. Through our efforts using these methods, Oncoshare integrates complex, longitudinal data from multiple electronic medical records and registries and provides a rich, validated resource for research on oncology care.

Implementing a Real-time Complex Event Stream Processing System to Help Identify Potential Participants in Clinical and Translational Research Studies.

Event Stream Processing is a computational approach to the problem of how to infer the occurrence of an event from a data stream in real time without reference to a database. This paper describes how we implemented this technology on the STRIDE platform to address the challenge of real time notification of patients presenting in the Emergency Department (ED) who potentially meet eligibility criteria for a clinical study. The system was evaluated against a standalone legacy alerting system and found to perform adequately. While our initial use of this technology was focused on relatively simple alerts, the system is extensible and has the potential to provide enterprise-level research alerting services supporting more complex scenarios.

Managing Medical Vocabulary Updates in a Clinical Data Warehouse: An RxNorm Case Study.

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.

Automated mapping of pharmacy orders from two electronic health record systems to RxNorm within the STRIDE clinical data warehouse.

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.

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. 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.

Novel integration of hospital electronic medical records and gene expression measurements to identify genetic markers of maturation.

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).

Clinical arrays of laboratory measures, or "clinarrays", built from an electronic health record enable disease subtyping by severity.

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.