Design considerations, architecture, and use of the Mini-Sentinel distributed data system

Technology-Enabled Outreach to Patients Taking High-Risk Medications Reduces a Quality Gap in Completion of Clinical Laboratory Testing

Clinical laboratory quality improvement (QI) efforts can include population test utilization. The authors used a health care organization's Medical Data Warehouse (MDW) to characterize a gap in guideline-concordant laboratory testing recommended for safe use of antirheumatic agents, then tested the effectiveness of laboratory-led, technology-enabled outreach to patients at reducing this gap. Data linkages available through the Kaiser Permanente Colorado MDW and electronic health record were used to identify ambulatory adults taking antirheumatic agents who were due/overdue for alanine aminotransferase (ALT), aspartate aminotransferase (AST), complete blood count (CBC), or serum creatinine (SCr) testing. Outreach was implemented using an interactive voice response system to send patients text or phone call reminders. Interrupted time series analysis was used to estimate reminder effectiveness. Rates of guideline-concordant testing and testing timeliness in baseline vs. intervention periods were determined using generalized linear models for repeated measures. Results revealed a decrease in percentage of 3763 patients taking antirheumatic agents due/overdue for testing at any given time: baseline 24.3% vs. intervention 17.5% (P < 0.001). Among 3205 patients taking conventional antirheumatic agents, concordance for all ALT testing was baseline 52.8% vs. intervention 65.4% (P < 0.001) among patients chronically using these agents and baseline 20.6% vs. intervention 26.1% (P < 0.001) among patients newly starting these agents. The 95^th percentiles for days to ALT testing were baseline 149 vs. intervention 117 among chronic users and baseline 134 vs. intervention 92 among new starts. AST, CBC, and SCr findings were similar. Technology-enabled outreach reminding patients to obtain laboratory testing improves health care system outcomes.

Collaborating on Data, Science, and Infrastructure: The 20-Year Journey of the Cancer Research Network

The Cancer Research Network (CRN) is a consortium of 12 research groups, each affiliated with a nonprofit integrated health care delivery system, that was first funded in 1998. The overall goal of the CRN is to support and facilitate collaborative cancer research within its component delivery systems. This paper describes the CRN's 20-year experience and evolution. The network combined its members' scientific capabilities and data resources to create an infrastructure that has ultimately supported over 275 projects. Insights about the strengths and limitations of electronic health data for research, approaches to optimizing multidisciplinary collaboration, and the role of a health services research infrastructure to complement traditional clinical trials and large observational datasets are described, along with recommendations for other research consortia.

Graphical Depiction of Longitudinal Study Designs in Health Care Databases

Pharmacoepidemiologic and pharmacoeconomic analysis of health care databases has become a vital source of evidence to support health care decision making and efficient management of health care organizations. However, decision makers often consider studies done in nonrandomized health care databases more difficult to review than randomized trials because many design choices need to be considered. This is perceived as an important barrier to decision making about the effectiveness and safety of medical products. Design flaws in longitudinal database studies are avoidable but can be unintentionally obscured in the convoluted prose of methods sections, which often lack specificity. We propose a simple framework of graphical representation that visualizes study design implementations in a comprehensive, unambiguous, and intuitive way; contains a level of detail that enables reproduction of key study design variables; and uses standardized structure and terminology to simplify review and communication to a broad audience of decision makers. Visualization of design details will make database studies more reproducible, quicker to review, and easier to communicate to a broad audience of decision makers.

A new analytic tool developed to assess safe use recommendations

PURPOSE

Develop a flexible analytic tool for the Food and Drug Administration's (FDA's) Sentinel System to assess adherence to safe use recommendations with two capabilities: characterize adherence to patient monitoring recommendations for a drug, and characterize concomitant medication use before, during, and/or after drug therapy.

METHODS

We applied the tool in the Sentinel Distributed Database to assess adherence to the labeled recommendation that patients treated with dronedarone undergo electrocardiogram (ECG) testing no less often than every 3 months. Measures of length of treatment, time to first ECG, number of ECGs, and time between ECGs were assessed. We also assessed concomitant use of contraception among female users of mycophenolate per label recommendations (concomitancy 4 weeks before through 6 weeks after discontinuation of mycophenolate). Unadjusted results were stratified by age, month-year, and sex.

RESULTS

We identified 21 457 new episodes of dronedarone use of greater than or equal to 90 days (July 2009 to September 2015); 86% had greater than or equal to one ECG, and 22% met the recommendation of an ECG no less often than every 3 months. We identified 21 942 new episodes of mycophenolate use among females 12 to 55 years (January 2016 to September 2015); 16% had greater than or equal to 1 day of concomitant contraception dispensed, 12% had concomitant contraception use for greater than or equal to 50% of the 4 weeks before initiation through 6 weeks after mycophenolate; younger females had more concomitancy. These results may be underestimates as the analyses are limited to claims data.

CONCLUSIONS

We developed a tool for use in databases formatted to the Sentinel Common Data Model that can assess adherence to safe use recommendations involving patient monitoring and concomitant drug use over time.

How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias

Several pharmacoepidemiology networks have been developed over the past decade that use a distributed approach, implementing the same analysis at multiple data sites, to preserve privacy and minimize data sharing. Distributed networks are efficient, by interrogating data on very large populations. The structure of these networks can also be leveraged to improve replicability, increase transparency, and reduce bias. We describe some features of distributed networks using, as examples, the Canadian Network for Observational Drug Effect Studies, the Sentinel System in the USA, and the European Research Network of Pharmacovigilance and Pharmacoepidemiology. Common protocols, analysis plans, and data models, with policies on amendments and protocol violations, are key features. These tools ensure that studies can be audited and repeated as necessary. Blinding and strict conflict of interest policies reduce the potential for bias in analyses and interpretation. These developments should improve the timeliness and accuracy of information used to support both clinical and regulatory decisions.

Identifying Preanalytic and Postanalytic Laboratory Quality Gaps Using a Data Warehouse and Structured Multidisciplinary Process

CONTEXT.—

The laboratory total testing process includes preanalytic, analytic, and postanalytic phases, but most laboratory quality improvement efforts address the analytic phase. Expanding quality improvement to preanalytic and postanalytic phases via use of medical data warehouses, repositories that include clinical, utilization, and administrative data, can improve patient care by ensuring appropriate test utilization. Cross-department, multidisciplinary collaboration to address gaps and improve patient and system outcomes is beneficial.

OBJECTIVE.—

To demonstrate medical data warehouse utility for characterizing laboratory-associated quality gaps amenable to preanalytic or postanalytic interventions.

DESIGN.—

A multidisciplinary team identified quality gaps. Medical data warehouse data were queried to characterize gaps. Organizational leaders were interviewed about quality improvement priorities. A decision aid with elements including national guidelines, local and national importance, and measurable outcomes was completed for each gap.

RESULTS.—

Gaps identified included (1) test ordering; (2) diagnosis, detection, and documentation, and (3) high-risk medication monitoring. After examination of medical data warehouse data including enrollment, diagnoses, laboratory, pharmacy, and procedures for baseline performance, high-risk medication monitoring was selected, specifically alanine aminotransferase, aspartate aminotransferase, complete blood count, and creatinine testing among patients receiving disease-modifying antirheumatic drugs. The test utilization gap was in monitoring timeliness (eg, >60% of patients had a monitoring gap exceeding the guideline recommended frequency). Other contributors to selecting this gap were organizational enthusiasm, regulatory labeling, and feasibility of a significant laboratory role in addressing the gap.

CONCLUSIONS.—

A multidisciplinary process facilitated identification and selection of a laboratory medicine quality gap. Medical data warehouse data were instrumental in characterizing gaps.

Use of tumor necrosis factor-alpha inhibitors during pregnancy among women who delivered live born infants

PURPOSE

To describe the use of tumor necrosis factor-alpha inhibitors (TNFis) among pregnancies ending in a live birth and with a diagnosis of ankylosing spondylitis (AS), Crohn's disease (CD), juvenile idiopathic arthritis (JIA), psoriasis (PsO), psoriatic arthritis (PsA), rheumatoid arthritis (RA), or ulcerative colitis (UC).

METHODS

We identified pregnancies among women aged 15 to 54 years between 01/01/2004 and 09/30/2015 from 16 health plans participating in Sentinel. We inferred indication using ICD-9-CM codes in the 183-day period before conception. We assessed proportion of infliximab, etanercept, adalimumab, certolizumab pegol, and golimumab by calendar year, indication, and maternal age, and compared them to proportions in an age-matched, indication-matched, and date-matched non-pregnant cohort.

RESULTS

Among 19 681 pregnancies with at least one chronic inflammatory condition, 2990 (15.2%) received a TNFi. In both pregnancies and matched non-pregnant cohort, TNFi use was highest (34.4%; 55.8%) for PsA patients and lowest (6.2%; 13.4%) for PsO patients. Etanercept was most frequently used among AS/JIA/PsA/PsO/RA patients, while infliximab was the preferred TNFi for CD/UC patients. Except for infliximab and certolizumab, TNFi use during pregnancy decreased after the first trimester. Pregnancies among older pregnant women (45-54 years) were more likely to be treated compared with the matched non-pregnant cohort.

CONCLUSION

There was a preference for etanercept among pregnancies with AS/JIA/PsA/PsO/RA, despite the availability of other TNFis. Decline in TNFi use after the first trimester may be related to the desire to reduce TNFis transplacental transfer and to minimize infection risk to the fetus or baby associated with live vaccine immunizations after birth.

Association of Risk for Venous Thromboembolism With Use of Low-Dose Extended- and Continuous-Cycle Combined Oral Contraceptives: A Safety Study Using the Sentinel Distributed Database

IMPORTANCE

Continuous/extended cyclic estrogen use (84/7 or 365/0 days cycles) in combined oral contraceptives (COCs) could potentially expose women to an increased cumulative dose of estrogen, compared with traditional cyclic regimens (21/7 days cycle), and may increase the risk for venous thromboembolism (VTE).

OBJECTIVE

To determine, while holding the progestogen type constant, whether the risk for VTE is higher with use of continuous/extended COCs than with cyclic COCs among women who initiated a COC containing ethinyl estradiol and levonorgestrel.

DESIGN, SETTING, AND PARTICIPANTS

Incident user retrospective cohort study of primarily commercially insured US population identified from the Sentinel Distributed Database. Participants were women aged 18 to 50 years at the time of initiating a study COC between May 2007 and September 2015. Using a propensity score approach and Cox proportional hazards regression models, we estimated the hazard ratios of VTE overall and separately by ethinyl estradiol dose and age groups.

EXPOSURES

Initiation of continuous/extended or traditional cyclic COCs containing ethinyl estradiol or levonorgestrel of any dose.

MAIN OUTCOMES AND MEASURES

First VTE hospitalization that occurred during the study follow-up, identified by an inpatient International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis code of 415.1, 415.1x, 453, 453.x, or 453.xx.

RESULTS

We identified 210 691 initiators of continuous/extended COCs (mean [SD] age, 30.4 [8.6] years) and 522 316 initiators of cyclic COCs (mean [SD] age, 28.8 [8.3] years), with a mean of 0.7 person-years at risk among continuous/extended and cyclic users. Baseline cardiovascular and metabolic conditions (7.2% vs 4.7%), gynecological conditions (39.7% vs 32.3%), and health services utilization were slightly higher among continuous/extended cyclic than cyclic COC users. Propensity score matching decreased the hazard ratio estimates from 1.84 (95% CI, 1.53-2.21) to 1.32 (95% CI, 1.07-1.64) for continuous/extended use compared with cyclic COC use. The absolute risk difference (0.27 per 1000 persons) and the incidence rate difference (0.35 cases per 1000 person-years [1.44 vs 1.09 cases per 1000 person-years]) between the 2 propensity score-matched cohorts remained low, which may not translate into a clinically significant risk differences between cyclic and noncyclic estrogen use.

CONCLUSIONS AND RELEVANCE

Holding the progestogen type constant (levonorgestrel), we observed a slightly elevated VTE risk in association with continuous/extended COC use when compared with cyclic COC use. However, due to the small absolute risk difference and potential residual confounding, our findings did not show strong evidence supporting a VTE risk difference between continuous/extended and cyclic COC use.

Lessons from meta-analyses of randomized clinical trials for analysis of distributed networks of observational databases

Networks of constellations of longitudinal observational databases, often electronic medical records or transactional insurance claims or both, are increasingly being used for studying the effects of medicinal products in real-world use. Such databases are frequently configured as distributed networks. That is, patient-level data are kept behind firewalls and not communicated outside of the data vendor other than in aggregate form. Instead, data are standardized across the network, and queries of the network are executed locally by data partners, and summary results provided to a central research partner(s) for amalgamation, aggregation, and summarization. Such networks can be huge covering years of data on upwards of 100 million patients. Examples of such networks include the FDA Sentinel Network, ASPEN, CNODES, and EU-ADR. As this is a new emerging field, we note in this paper the conceptual similarities and differences between the analysis of distributed networks and the now well-established field of meta-analysis of randomized clinical trials (RCTs). We recommend, wherever appropriate, to apply learnings from meta-analysis to help guide the development of distributed network analyses of longitudinal observational databases.

Evaluation of Electronic Healthcare Databases for Post-Marketing Drug Safety Surveillance and Pharmacoepidemiology in China

INTRODUCTION

Electronic healthcare databases (EHDs) are used increasingly for post-marketing drug safety surveillance and pharmacoepidemiology in Europe and North America. However, few studies have examined the potential of these data sources in China.

METHODS

Three major types of EHDs in China (i.e., a regional community-based database, a national claims database, and an electronic medical records [EMR] database) were selected for evaluation. Forty core variables were derived based on the US Mini-Sentinel (MS) Common Data Model (CDM) as well as the data features in China that would be desirable to support drug safety surveillance. An email survey of these core variables and eight general questions as well as follow-up inquiries on additional variables was conducted. These 40 core variables across the three EHDs and all variables in each EHD along with those in the US MS CDM and Observational Medical Outcomes Partnership (OMOP) CDM were compared for availability and labeled based on specific standards.

RESULTS

All of the EHDs' custodians confirmed their willingness to share their databases with academic institutions after appropriate approval was obtained. The regional community-based database contained 1.19 million people in 2015 with 85% of core variables. Resampled annually nationwide, the national claims database included 5.4 million people in 2014 with 55% of core variables, and the EMR database included 3 million inpatients from 60 hospitals in 2015 with 80% of core variables. Compared with MS CDM or OMOP CDM, the proportion of variables across the three EHDs available or able to be transformed/derived from the original sources are 24-83% or 45-73%, respectively.

CONCLUSIONS

These EHDs provide potential value to post-marketing drug safety surveillance and pharmacoepidemiology in China. Future research is warranted to assess the quality and completeness of these EHDs or additional data sources in China.

Comparative safety of NSAIDs for gastrointestinal events in Asia-Pacific populations: A multi-database, international cohort study

PURPOSE

The safety of nonsteroidal anti-inflammatory drugs (NSAIDs) commonly used in Asia-Pacific countries has had limited study. We assessed the risk of hospitalization for gastrointestinal events with loxoprofen and mefenamic acid compared with other NSAIDs in Asia-Pacific populations.

METHODS

We conducted a cohort study using a distributed network with a common data model in Australia, Hong Kong, Japan, Korea, and Taiwan. We included patients who initiated diclofenac, loxoprofen, mefenamic acid, or celecoxib and followed them until their first gastrointestinal hospitalization, switch or discontinuation of medication, disenrollment, or end of database coverage. We used Cox proportional hazards models to assess hospitalization risk.

RESULTS

We identified 9879 patients in Japan, 70 492 in Taiwan, 263 741 in Korea, and 246 in Hong Kong who initiated an NSAID, and 44 013 patients in Australia, a predominantly Caucasian population. The incidence of gastrointestinal hospitalization was 25.6 per 1000 person-years in Japan, 32.8 in Taiwan, 11.5 in Korea, 484.5 in Hong Kong, and 35.6 in Australia. Compared with diclofenac, the risk of gastrointestinal events with loxoprofen was significantly lower in Korea (hazards ratio, 0.37; 95% CI, 0.25-0.54) but not in Japan (1.65; 95% CI, 0.47-5.78). The risk of gastrointestinal events with mefenamic acid was significantly lower in Taiwan (0.45; 95% CI, 0.26-0.78) and Korea (0.11; 95% CI, 0.05-0.27) but not Hong Kong (2.16; 95% CI, 0.28-16.87), compared with diclofenac.

CONCLUSIONS

Compared with diclofenac, loxoprofen was associated with a lower risk of gastrointestinal hospitalizations in Korea and mefenamic acid with a lower risk in Taiwan and Korea.

Pharmacovigilance in China: development and challenges

Background Rational drug use and drug safety are becoming increasingly important concerns in China with the increasing public access to drugs and the health-care system, and this has led to the development of pharmacovigilance in China. Aim of the review To provide a brief introduction about pharmacovigilance in China in terms of system development, utilization and challenges. Method Relevant studies on pharmacovigilance related to the study aim was undertaken through literature search to synthesize the extracted data. Results The creation and evolvement of China's pharmacovigilance system spans across 30 years since 1989. The system consists of four progressing administrative layers: county, municipal, provincial and national levels. China has passed over 20 laws and regulations related to pharmacovigilance covering the processes of drug development, manufacture, distribution and use with the aim to guard drug safety. An online spontaneous self-reporting Adverse Drug Reaction (ADR) Monitoring System was established in 2003. ADRs are mainly reported by medical institutions, pharmaceutical manufacturers, and drug distributors. Currently there is no mandatory ADR reporting requirement for pharmaceutical manufacturers, and a proposed regulation under public comment will likely change this. China has started to build active pharmacovigilance surveillance programs in addition to the passive ADR reporting system. The China Food and Drug Administration has established the intensive Safety Monitoring Program and the National Adverse Drug Reaction Monitoring Sentinel Alliance Program based on electronic health records to further the efforts of ADR reporting, monitoring and analysis. Conclusion The practice of ADR monitoring and pharmacovigilance in China have made great progress. More efforts are needed both in system building, and creation of laws and regulations to strengthen the safe use of medicines.

Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects

BACKGROUND

Decision makers in health care increasingly rely on nonrandomized database analyses to assess the effectiveness, safety, and value of medical products. Health care data scientists use data-adaptive approaches that automatically optimize confounding control to study causal treatment effects. This article summarizes relevant experiences and extensions.

METHODS

The literature was reviewed on the uses of high-dimensional propensity score (HDPS) and related approaches for health care database analyses, including methodological articles on their performance and improvement. Articles were grouped into applications, comparative performance studies, and statistical simulation experiments.

RESULTS

The HDPS algorithm has been referenced frequently with a variety of clinical applications and data sources from around the world. The appeal of HDPS for database research rests in 1) its superior performance in situations of unobserved confounding through proxy adjustment, 2) its predictable efficiency in extracting confounding information from a given data source, 3) its ability to automate estimation of causal treatment effects to the extent achievable in a given data source, and 4) its independence of data source and coding system. Extensions of the HDPS approach have focused on improving variable selection when exposure is sparse, using free text information and time-varying confounding adjustment.

CONCLUSION

Semiautomated and optimized confounding adjustment in health care database analyses has proven successful across a wide range of settings. Machine-learning extensions further automate its use in estimating causal treatment effects across a range of data scenarios.

Early impact of the ICD-10-CM transition on selected health outcomes in 13 electronic health care databases in the United States

PURPOSE

To describe the consistency in the frequency of 5 health outcomes across the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM) eras in the US.

METHODS

We examined the incidence of 3 acute conditions (acute myocardial infarction [AMI], angioedema, ischemic stroke) and the prevalence of 2 chronic conditions (diabetes, hypertension) during the final 5 years of the ICD-9-CM era (January 2010-September 2015) and the first 15 months of the ICD-10-CM era (October 2015-December 2016) in 13 electronic health care databases in the Sentinel System. For each health outcome reviewed during the ICD-10-CM era, we evaluated 4 definitions, including published algorithms derived from other countries, as well as simple-forward, simple-backward, and forward-backward mapping using the General Equivalence Mappings. For acute conditions, we also compared the incidence between April to December 2014 and April to December 2016.

RESULTS

The analyses included data from approximately 172 million health plan members. While the incidence or prevalence of AMI and hypertension performed similarly across the 2 eras, the other 3 outcomes did not demonstrate consistent trends for some or all the ICD-10-CM definitions assessed.

CONCLUSIONS

When using data from both the ICD-9-CM and ICD-10-CM eras, or when using results from ICD-10-CM data to compare to results from ICD-9-CM data, researchers should test multiple ICD-10-CM outcome definitions as part of sensitivity analysis. Ongoing assessment of the impact of ICD-10-CM transition on identification of health outcomes in US electronic health care databases should occur as more data accrue.

Sentinel Modular Program for Propensity Score-Matched Cohort Analyses: Application to Glyburide, Glipizide, and Serious Hypoglycemia

Sentinel is a program sponsored by the US Food and Drug Administration to monitor the safety of medical products. We conducted a cohort assessment to evaluate the ability of the Sentinel Propensity Score Matching Tool to reproduce in an expedited fashion the known association between glyburide (vs. glipizide) and serious hypoglycemia. Thirteen data partners who contribute to the Sentinel Distributed Database participated in this analysis. A pretested and customizable analytic program was run at each individual site. De-identified summary results from each data partner were returned and aggregated at the Sentinel Operations Center. We identified a total of 198,550 and 379,507 new users of glyburide and glipizide, respectively. The incidence of emergency department visits and hospital admissions for serious hypoglycemia was 19 per 1000 person-years (95% confidence interval = 17.9, 19.7) for glyburide users and 22 (21.6, 22.7) for glipizide users. In cohorts matched by propensity score based on predefined variables, the hazard ratio (HR) for glyburide was 1.36 (1.24, 1.49) versus glipizide. In cohorts matched on a high-dimensional propensity score based on empirically selected variables, for which the program ran to completion in five data partners, the HR was 1.49 (1.31, 1.70). In cohorts matched on propensity scores based on both predefined and empirically selected variables via the high-dimensional propensity score algorithm (the same five data partners), the HR was 1.51 (1.32, 1.71). These findings are consistent with the literature, and demonstrate the ability of the Sentinel Propensity Score Matching Tool to reproduce this known association in an expedited fashion.See video abstract at, http://links.lww.com/EDE/B275.

Assessment of Quadrivalent Human Papillomavirus Vaccine Safety Using the Self-Controlled Tree-Temporal Scan Statistic Signal-Detection Method in the Sentinel System

The self-controlled tree-temporal scan statistic-a new signal-detection method-can evaluate whether any of a wide variety of health outcomes are temporally associated with receipt of a specific vaccine, while adjusting for multiple testing. Neither health outcomes nor postvaccination potential periods of increased risk need be prespecified. Using US medical claims data in the Food and Drug Administration's Sentinel system, we employed the method to evaluate adverse events occurring after receipt of quadrivalent human papillomavirus vaccine (4vHPV). Incident outcomes recorded in emergency department or inpatient settings within 56 days after first doses of 4vHPV received by 9- through 26.9-year-olds in 2006-2014 were identified using International Classification of Diseases, Ninth Revision, diagnosis codes and analyzed by pairing the new method with a standard hierarchical classification of diagnoses. On scanning diagnoses of 1.9 million 4vHPV recipients, 2 statistically significant categories of adverse events were found: cellulitis on days 2-3 after vaccination and "other complications of surgical and medical procedures" on days 1-3 after vaccination. Cellulitis is a known adverse event. Clinically informed investigation of electronic claims records of the patients with "other complications" did not suggest any previously unknown vaccine safety problem. Considering that thousands of potential short-term adverse events and hundreds of potential risk intervals were evaluated, these findings add significantly to the growing safety record of 4vHPV.

A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks

Introduction:

Patient privacy and data security concerns often limit the feasibility of pooling patient-level data from multiple sources for analysis. Distributed data networks (DDNs) that employ privacy-protecting analytical methods, such as distributed regression analysis (DRA), can mitigate these concerns. However, DRA is not routinely implemented in large DDNs.

Objective:

We describe the design and implementation of a process framework and query workflow that allow automatable DRA in real-world DDNs that use PopMedNet™, an open-source distributed networking software platform.

Methods:

We surveyed and catalogued existing hardware and software configurations at all data partners in the Sentinel System, a PopMedNet-driven DDN. Key guiding principles for the design included minimal disruptions to the current PopMedNet query workflow and minimal modifications to data partners’ hardware configurations and software requirements.

Results:

We developed and implemented a three-step process framework and PopMedNet query workflow that enables automatable DRA: 1) assembling a de-identified patient-level dataset at each data partner, 2) distributing a DRA package to data partners for local iterative analysis, and 3) iteratively transferring intermediate files between data partners and analysis center. The DRA query workflow is agnostic to statistical software, accommodates different regression models, and allows different levels of user-specified automation.

Discussion:

The process framework can be generalized to and the query workflow can be adopted by other PopMedNet-based DDNs.

Conclusion:

DRA has great potential to change the paradigm of data analysis in DDNs. Successful implementation of DRA in Sentinel will facilitate adoption of the analytic approach in other DDNs.

Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample

Vaccine safety studies are often electronic health record (EHR)-based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self-controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. However, for common adverse events, limited resources often make it impossible to adjudicate all adverse events observed in electronic data. In this paper, we considered four approaches for analyzing SCCS data with confirmation rates estimated from an internal validation sample: (1) observed cases, (2) confirmed cases only, (3) known confirmation rate, and (4) multiple imputation (MI). We conducted a simulation study to evaluate these four approaches using type I error rates, percent bias, and empirical power. Our simulation results suggest that when misclassification of adverse events is present, approaches such as observed cases, confirmed case only, and known confirmation rate may inflate the type I error, yield biased point estimates, and affect statistical power. The multiple imputation approach considers the uncertainty of estimated confirmation rates from an internal validation sample, yields a proper type I error rate, largely unbiased point estimate, proper variance estimate, and statistical power.

Real-World Data Analytics Fit for Regulatory Decision-Making

Healthcare database analyses (claims, electronic health records) have been identified by various regulatory initiatives, including the 21^st Century Cures Act and Prescription Drug User Fee Act ("PDUFA"), as useful supplements to randomized clinical trials to generate evidence on the effectiveness, harm, and value of medical products in routine care. Specific applications include accelerated drug approval pathways and secondary indications for approved medical products. Such real-world data ("RWD") analyses reflect how medical products impact health outside a highly controlled research environment. A constant stream of data from the routine operation of modern healthcare systems that can be analyzed in rapid cycles enables incremental evidence development for regulatory decision-making. Key evidentiary needs by regulators include 1) monitoring of medication performance in routine care, including the effectiveness, safety and value; 2) identifying new patient strata in which a drug may have added value or unacceptable harms; and 3) monitoring targeted utilization. Four broad requirements have been proposed to enable successful regulatory decision-making based on healthcare database analyses (collectively, "MVET"): Meaningful evidence that provides relevant and context-informed evidence sufficient for interpretation, drawing conclusions, and making decisions; valid evidence that meets scientific and technical quality standards to allow causal interpretations; expedited evidence that provides incremental evidence that is synchronized with the decision-making process; and transparent evidence that is audible, reproducible, robust, and ultimately trusted by decision-makers. Evidence generation systems that satisfy MVET requirements to a high degree will contribute to effective regulatory decision-making. Rapid-cycle analytics of healthcare databases is maturing at a time when regulatory overhaul increasingly demands such evidence. Governance, regulations, and data quality are catching up as the utility of this resource is demonstrated in multiple contexts.

Identifying Unmet Treatment Needs for Patients With Osteoporotic Fracture: Feasibility Study for an Electronic Clinical Surveillance System

BACKGROUND

Traditional clinical surveillance relied on the results from clinical trials and observational studies of administrative databases. However, these studies not only required many valuable resources but also faced a very long time lag.

OBJECTIVE

This study aimed to illustrate a practical application of the National Taiwan University Hospital Clinical Surveillance System (NCSS) in the identification of patients with an osteoporotic fracture and to provide a high reusability infrastructure for longitudinal clinical data.

METHODS

The NCSS integrates electronic medical records in the National Taiwan University Hospital (NTUH) with a data warehouse and is equipped with a user-friendly interface. The NCSS was developed using professional insight from multidisciplinary experts, including clinical practitioners, epidemiologists, and biomedical engineers. The practical example identifying the unmet treatment needs for patients encountering major osteoporotic fractures described herein was mainly achieved by adopting the computerized workflow in the NCSS.

RESULTS

We developed the infrastructure of the NCSS, including an integrated data warehouse and an automatic surveillance workflow. By applying the NCSS, we efficiently identified 2193 patients who were newly diagnosed with a hip or vertebral fracture between 2010 and 2014 at NTUH. By adopting the filter function, we identified 1808 (1808/2193, 82.44%) patients who continued their follow-up at NTUH, and 464 (464/2193, 21.16%) patients who were prescribed anti-osteoporosis medications, within 3 and 12 months post the index date of their fracture, respectively.

CONCLUSIONS

The NCSS systems can integrate the workflow of cohort identification to accelerate the survey process of clinically relevant problems and provide decision support in the daily practice of clinical physicians, thereby making the benefit of evidence-based medicine a reality.

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®)

Introduction

Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data.

Methods

PCORnet's Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals.

Results

The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners.

Discussion

The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.

Safety and Effectiveness of Direct Oral Anticoagulants Versus Vitamin K Antagonists: Pilot Implementation of a Near-Real-Time Monitoring Program in Italy

Background

Real‐time monitoring is used to the ends of postmarketing observational research on newly marketed drugs. We implemented a pilot near‐real‐time monitoring program on the test case of oral anticoagulants. Specifically, we evaluated the safety and effectiveness of direct oral anticoagulants compared to vitamin K antagonists in nonvalvular atrial fibrillation secondary prevention during 2013‐2015 in the Lazio Region, Italy.

Methods and Results

A cohort study was conducted using a sequential propensity‐score–matched new user parallel‐cohort design. Sequential analyses were performed using Cox models. Overall, 10 742 patients contributed to the analyses. Compared with vitamin K antagonists, direct oral anticoagulant use was associated with a reduction of all‐cause mortality (0.81; 95% confidence interval [CI] 0.66‐0.99), cardiovascular mortality (0.71; 95% CI 0.54‐0.93), myocardial infarction (0.67; 95% CI 0.43‐1.04), ischemic stroke (0.87; 95% CI 0.52‐1.45), hemorrhagic stroke (0.25; 95% CI 0.07‐0.88), and with a nonsignificant increase of gastrointestinal bleeding (1.26; 95% CI 0.69‐2.30).

Conclusions

The present pilot study is a cornerstone to develop real‐time monitoring for new drugs in our region.

A Survey on Pharmacovigilance Activities in ASEAN and Selected Non-ASEAN Countries, and the Use of Quantitative Signal Detection Algorithms

INTRODUCTION

Most Countries have pharmacovigilance (PV) systems in place to monitor the safe use of health products. The process involves the detection and assessment of safety issues from various sources of information, communicating the risk to stakeholders and taking other relevant risk minimization measures.

OBJECTIVES

This study aimed to assess the PV status in Association of Southeast Asian Nation (ASEAN) countries, sources for postmarket safety monitoring, methods used for signal detection and the need for a quantitative signal detection algorithm (QSDA). Comparisons were conducted with centres outside ASEAN.

METHODS

A questionnaire was sent to all PV centres in ASEAN countries, as well as seven other countries, from November 2015 to June 2016. The questionnaire was designed to collect information on the status of PV, with a focus on the use of a QSDA.

RESULTS

Data were collected from nine ASEAN countries and seven other countries. PV activities were conducted in all these countries, which were at different stages of development. In terms of adverse drug reaction (ADR) reports, the average number received per year ranged from 3 to 50,000 reports for ASEAN countries and from 7000 to 1,103,200 for non-ASEAN countries. Thirty-three percent of ASEAN countries utilized statistical methods to help detect signals from ADR reports compared with 100% in the other non-ASEAN countries. Eighty percent agreed that the development of a QSDA would help in drug signal detection. The main limitation identified was the lack of knowledge and/or lack of resources.

CONCLUSION

Spontaneous ADR reports from healthcare professionals remains the most frequently used source for safety monitoring. The traditional method of case-by-case review of ADR reports prevailed for signal detection in ASEAN countries. As the reports continue to grow, the development of a QSDA would be useful in helping detect safety signals.

Do FDA label changes work? Assessment of the 2010 class label change for proton pump inhibitors using the Sentinel System's analytic tools

PURPOSE

To pilot use of the US Food and Drug Administration's (FDA's) Sentinel System data and analytic tools by a non-FDA stakeholder through the Innovation in Medical Evidence Development and Surveillance system of the Reagan Udall Foundation. We evaluated the US FDA 2010 proton pump inhibitor (PPI) class label change that warned of increased risk of bone fracture, to use PPIs for the lowest dose and shortest duration, and to manage bone status for those at risk for osteoporosis.

METHODS

The cohort consisted of adults aged 18 years or older prescribed PPIs without fracture risk factors. We evaluated incident and prevalent uses of the 8 PPIs noted in the label change. Outcomes evaluated before and after label change were PPI dispensing patterns, incident fractures, and osteoporosis screening or interventions. Consistent with FDA use of descriptive tools, we did not include direct comparisons or statistical testing.

RESULTS

There were 1 488 869 and 2 224 420 incident PPI users in the before [PRE] and after [POST] periods, respectively. Users with 1 year or more of exposure decreased (8.4% vs 7.5%), as did mean days supplied/user (130.4 to 113.7 d among all users and 830.8 to 645.4 d among users with 1 y or more of exposure). Osteoporosis screening and interventions did not appear to increase, but the proportion of patients with fractures decreased (4.4% vs 3.1%). Prevalent user results were similar.

CONCLUSIONS

This analysis demonstrated the ability to use Sentinel tools to assess the effectiveness of a label change and accompanying communication at the population level and suggests an influence on subsequent dispensing behavior.

Chart validation of inpatient International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) administrative diagnosis codes for venous thromboembolism (VTE) among intravenous immune globulin (IGIV) users in the Sentinel Distributed Database

The Sentinel Distributed Database (SDD) is a database of patient administrative healthcare records, derived from insurance claims and electronic health records, sponsored by the US Food and Drug Administration for evaluation of medical product outcomes. There is limited information on the validity of diagnosis codes for acute venous thromboembolism (VTE) in the SDD and administrative healthcare data more generally.In this chart validation study, we report on the positive predictive value (PPV) of inpatient administrative diagnosis codes for acute VTE-pulmonary embolism (PE) or lower-extremity or site-unspecified deep vein thrombosis (DVT)-within the SDD. As part of an assessment of thromboembolic adverse event risk following treatment with intravenous immune globulin (IGIV), charts were obtained for 75 potential VTE cases, abstracted, and physician-adjudicated.VTE status was determined for 62 potential cases. PPVs for lower-extremity DVT and/or PE were 90% (95% CI: 73-98%) for principal-position diagnoses, 80% (95% CI: 28-99%) for secondary diagnoses, and 26% (95% CI: 11-46%) for position-unspecified diagnoses (originating from physician claims associated with an inpatient stay). Average symptom onset was 1.5 days prior to hospital admission (range: 19 days prior to 4 days after admission).PPVs for principal and secondary VTE discharge diagnoses were similar to prior study estimates. Position-unspecified diagnoses were less likely to represent true acute VTE cases.

Prospective Postmarketing Surveillance of Acute Myocardial Infarction in New Users of Saxagliptin: A Population-Based Study

OBJECTIVE

The cardiovascular safety of saxagliptin, a dipeptidyl-peptidase 4 inhibitor, compared with other antihyperglycemic treatments is not well understood. We prospectively examined the association between saxagliptin use and acute myocardial infarction (AMI).

RESEARCH DESIGN AND METHODS

We identified patients aged ≥18 years, starting from the approval date of saxagliptin in 2009 and continuing through August 2014, using data from 18 Mini-Sentinel data partners. We conducted seven sequential assessments comparing saxagliptin separately with sitagliptin, pioglitazone, second-generation sulfonylureas, and long-acting insulin, using disease risk score (DRS) stratification and propensity score (PS) matching to adjust for potential confounders. Sequential testing kept the overall chance of a false-positive signal below 0.05 (one-sided) for each pairwise comparison.

RESULTS

We identified 82,264 saxagliptin users and more than 1.5 times as many users of each comparator. At the end of surveillance, the DRS-stratified hazard ratios (HRs) (95% CI) were 1.08 (0.90-1.28) in the comparison with sitagliptin, 1.11 (0.87-1.42) with pioglitazone, 0.79 (0.64-0.98) with sulfonylureas, and 0.57 (0.46-0.70) with long-acting insulin. The corresponding PS-matched HRs were similar. Only one interim analysis of 168 analyses met criteria for a safety signal: the PS-matched saxagliptin-pioglitazone comparison from the fifth sequential analysis, which yielded an HR of 1.63 (1.12-2.37). This association diminished in subsequent analyses.

CONCLUSIONS

We did not find a higher AMI risk in saxagliptin users compared with users of other selected antihyperglycemic agents during the first 5 years after U.S. Food and Drug Administration approval of the drug.

Outcomes of Dabigatran and Warfarin for Atrial Fibrillation in Contemporary Practice: A Retrospective Cohort Study

BACKGROUND

Dabigatran (150 mg twice daily) has been associated with lower rates of stroke than warfarin in trials of atrial fibrillation, but large-scale evaluations in clinical practice are limited.

OBJECTIVE

To compare incidence of stroke, bleeding, and myocardial infarction in patients receiving dabigatran versus warfarin in practice.

DESIGN

Retrospective cohort.

SETTING

National U.S. Food and Drug Administration Sentinel network.

PATIENTS

Adults with atrial fibrillation initiating dabigatran or warfarin therapy between November 2010 and May 2014.

MEASUREMENTS

Ischemic stroke, intracranial hemorrhage, extracranial bleeding, and myocardial infarction identified from hospital claims among propensity score-matched patients starting treatment with dabigatran or warfarin.

RESULTS

Among 25 289 patients starting dabigatran therapy and 25 289 propensity score-matched patients starting warfarin therapy, those receiving dabigatran did not have significantly different rates of ischemic stroke (0.80 vs. 0.94 events per 100 person-years; hazard ratio [HR], 0.92 [95% CI, 0.65 to 1.28]) or extracranial hemorrhage (2.12 vs. 2.63 events per 100 person-years; HR, 0.89 [CI, 0.72 to 1.09]) but were less likely to have intracranial bleeding (0.39 vs. 0.77 events per 100 person-years; HR, 0.51 [CI, 0.33 to 0.79]) and more likely to have myocardial infarction (0.77 vs. 0.43 events per 100 person-years; HR, 1.88 [CI, 1.22 to 2.90]). However, the strength and significance of the association between dabigatran use and myocardial infarction varied in sensitivity analyses and by exposure definition (HR range, 1.13 [CI, 0.78 to 1.64] to 1.43 [CI, 0.99 to 2.08]). Older patients and those with kidney disease had higher gastrointestinal bleeding rates with dabigatran.

LIMITATION

Inability to examine outcomes by dabigatran dose (unacceptable covariate balance between matched patients) or quality of warfarin anticoagulation (few patients receiving warfarin had available international normalized ratio values).

CONCLUSION

In matched adults with atrial fibrillation treated in practice, the incidences of stroke and bleeding with dabigatran versus warfarin were consistent with those seen in trials. The possible relationship between dabigatran and myocardial infarction warrants further investigation.

PRIMARY FUNDING SOURCE

U.S. Food and Drug Administration.

Development and application of two semi-automated tools for targeted medical product surveillance in a distributed data network

PURPOSE OF REVIEW

An important component of the Food and Drug Administration's Sentinel Initiative is the active post-market risk identification and analysis (ARIA) system, which utilizes semi-automated, parameterized computer programs to implement propensity-score adjusted and self-controlled risk interval designs to conduct targeted surveillance of medical products in the Sentinel Distributed Database. In this manuscript, we review literature relevant to the development of these programs and describe their application within the Sentinel Initiative.

RECENT FINDINGS

These quality-checked and publicly available tools have been successfully used to conduct rapid, replicable, and targeted safety analyses of several medical products. In addition to speed and reproducibility, use of semi-automated tools allows investigators to focus on decisions regarding key methodological parameters. We also identified challenges associated with the use of these methods in distributed and prospective datasets like the Sentinel Distributed Database, namely uncertainty regarding the optimal approach to estimating propensity scores in dynamic data among data partners of heterogeneous size.

SUMMARY

Future research should focus on the methodological challenges raised by these applications as well as developing new modular programs for targeted surveillance of medical products.

Application and optimisation of the Comparison on Extreme Laboratory Tests (CERT) algorithm for detection of adverse drug reactions: Transferability across national boundaries

PURPOSE

The Singapore regulatory agency for health products (Health Sciences Authority), in performing active surveillance of medicines and their potential harms, is open to new methods to achieve this goal. Laboratory tests are a potential source of data for this purpose. We have examined the performance of the Comparison on Extreme Laboratory Tests (CERT) algorithm, developed by Ajou University, Korea, as a potential tool for adverse drug reaction detection based on the electronic medical records of the Singapore health care system.

METHODS

We implemented the original CERT algorithm, comparing extreme laboratory results pre- and post-drug exposure, and 5 variations thereof using 4.5 years of National University Hospital (NUH) electronic medical record data (31 869 588 laboratory tests, 6 699 591 drug dispensings from 272 328 hospitalizations). We investigated 6 drugs from the original CERT paper and an additional 47 drugs. We benchmarked results against a reference standard that we created from UpToDate 2015.

RESULTS

The original CERT algorithm applied to all 53 drugs and 44 laboratory abnormalities yielded a positive predictive value (PPV) and sensitivity of 50.3% and 54.1%, respectively. By raising the minimum number of cases for each drug-laboratory abnormality pair from 2 to 400, the PPV and sensitivity increased to 53.9% and 67.2%, respectively. This post hoc variation, named CERT400, performed particularly well for drug-induced hepatic and renal toxicities.

DISCUSSION

We have demonstrated that the CERT algorithm can be applied across national boundaries. One modification (CERT400) was able to identify adverse drug reaction signals from laboratory data with reasonable PPV and sensitivity, which indicates potential utility as a supplementary pharmacovigilance tool.

Representing Knowledge Consistently Across Health Systems

Objectives: Electronic health records (EHRs) have increasingly emerged as a powerful source of clinical data that can be leveraged for reuse in research and in modular health apps that integrate into diverse health information technologies. A key challenge to these use cases is representing the knowledge contained within data from different EHR systems in a uniform fashion. Method: We reviewed several recent studies covering the knowledge representation in the common data models for the Observational Medical Outcomes Partnership (OMOP) and its Observational Health Data Sciences and Informatics program, and the United States Patient Centered Outcomes Research Network (PCORNet). We also reviewed the Health Level 7 Fast Healthcare Interoperability Resource standard supporting app-like programs that can be used across multiple EHR and research systems. Results: There has been a recent growth in high-impact efforts to support quality-assured and standardized clinical data sharing across different institutions and EHR systems. We focused on three major efforts as part of a larger landscape moving towards shareable, transportable, and computable clinical data. Conclusion: The growth in approaches to developing common data models to support interoperable knowledge representation portends an increasing availability of high-quality clinical data in support of research. Building on these efforts will allow a future whereby significant portions of the populations in the world may be able to share their data for research.

Combining evidence from multiple electronic health care databases: performances of one-stage and two-stage meta-analysis in matched case-control studies

PURPOSE

Clustering of patients in databases is usually ignored in one-stage meta-analysis of multi-database studies using matched case-control data. The aim of this study was to compare bias and efficiency of such a one-stage meta-analysis with a two-stage meta-analysis.

METHODS

First, we compared the approaches by generating matched case-control data under 5 simulated scenarios, built by varying: (1) the exposure-outcome association; (2) its variability among databases; (3) the confounding strength of one covariate on this association; (4) its variability; and (5) the (heterogeneous) confounding strength of two covariates. Second, we made the same comparison using empirical data from the ARITMO project, a multiple database study investigating the risk of ventricular arrhythmia following the use of medications with arrhythmogenic potential. In our study, we specifically investigated the effect of current use of promethazine.

RESULTS

Bias increased for one-stage meta-analysis with increasing (1) between-database variance of exposure effect and (2) heterogeneous confounding generated by two covariates. The efficiency of one-stage meta-analysis was slightly lower than that of two-stage meta-analysis for the majority of investigated scenarios. Based on ARITMO data, there were no evident differences between one-stage (OR = 1.50, CI = [1.08; 2.08]) and two-stage (OR = 1.55, CI = [1.12; 2.16]) approaches.

CONCLUSIONS

When the effect of interest is heterogeneous, a one-stage meta-analysis ignoring clustering gives biased estimates. Two-stage meta-analysis generates estimates at least as accurate and precise as one-stage meta-analysis. However, in a study using small databases and rare exposures and/or outcomes, a correct one-stage meta-analysis becomes essential.

Secondary Use and Analysis of Big Data Collected for Patient Care

Objectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the timeframe since the working group's last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.

CodeMapper: semiautomatic coding of case definitions. A contribution from the ADVANCE project

BACKGROUND

Assessment of drug and vaccine effects by combining information from different healthcare databases in the European Union requires extensive efforts in the harmonization of codes as different vocabularies are being used across countries. In this paper, we present a web application called CodeMapper, which assists in the mapping of case definitions to codes from different vocabularies, while keeping a transparent record of the complete mapping process.

METHODS

CodeMapper builds upon coding vocabularies contained in the Metathesaurus of the Unified Medical Language System. The mapping approach consists of three phases. First, medical concepts are automatically identified in a free-text case definition. Second, the user revises the set of medical concepts by adding or removing concepts, or expanding them to related concepts that are more general or more specific. Finally, the selected concepts are projected to codes from the targeted coding vocabularies. We evaluated the application by comparing codes that were automatically generated from case definitions by applying CodeMapper's concept identification and successive concept expansion, with reference codes that were manually created in a previous epidemiological study.

RESULTS

Automated concept identification alone had a sensitivity of 0.246 and positive predictive value (PPV) of 0.420 for reproducing the reference codes. Three successive steps of concept expansion increased sensitivity to 0.953 and PPV to 0.616.

CONCLUSIONS

Automatic concept identification in the case definition alone was insufficient to reproduce the reference codes, but CodeMapper's operations for concept expansion provide an effective, efficient, and transparent way for reproducing the reference codes.

Can data repositories help find effective treatments for complex diseases?

There are many challenges to developing treatments for complex diseases. This review explores the question of whether it is possible to imagine a data repository that would increase the pace of understanding complex diseases sufficiently well to facilitate the development of effective treatments. First, consideration is given to the amount of data that might be needed for such a data repository and whether the existing data storage infrastructure is enough. Several successful data repositories are then examined to see if they have common characteristics. An area of science where unsuccessful attempts to develop a data infrastructure is then described to see what lessons could be learned for a data repository devoted to complex disease. Then, a variety of issues related to sharing data are discussed. In some of these areas, it is reasonably clear how to move forward. In other areas, there are significant open questions that need to be addressed by all data repositories. Using that baseline information, the question of whether data archives can be effective in understanding a complex disease is explored. The major goal of such a data archive is likely to be identifying biomarkers that define sub-populations of the disease.

Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations

Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemiologic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed health-care databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases.

Development and validation of algorithms for the detection of statin myopathy signals from electronic medical records

The purpose of this study was to develop and validate sensitive algorithms to detect hospitalized statin-induced myopathy (SIM) cases from electronic medical records (EMRs). We developed four algorithms on a training set of 31,211 patient records from a large tertiary hospital. We determined the performance of these algorithms against manually curated records. The best algorithm used a combination of elevated creatine kinase (>4× the upper limit of normal (ULN)), discharge summary, diagnosis, and absence of statin in discharge medications. This algorithm achieved a positive predictive value of 52-71% and a sensitivity of 72-78% on two validation sets of >30,000 records each. Using this algorithm, the incidence of SIM was estimated at 0.18%. This algorithm captured three times more rhabdomyolysis cases than spontaneous reports (95% vs. 30% of manually curated gold standard cases). Our results show the potential power of utilizing data and text mining of EMRs to enhance pharmacovigilance activities.

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Background

Techniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step.

Methods

We designed a secure protocol for the deduplication of horizontally partitioned datasets with deterministic record linkage algorithms. We provided a formal security analysis of the protocol in the presence of semi-honest adversaries. The protocol was implemented and deployed across three microbiology laboratories located in Norway, and we ran experiments on the datasets in which the number of records for each laboratory varied. Experiments were also performed on simulated microbiology datasets and data custodians connected through a local area network.

Results

The security analysis demonstrated that the protocol protects the privacy of individuals and data custodians under a semi-honest adversarial model. More precisely, the protocol remains secure with the collusion of up to N − 2 corrupt data custodians. The total runtime for the protocol scales linearly with the addition of data custodians and records. One million simulated records distributed across 20 data custodians were deduplicated within 45 s. The experimental results showed that the protocol is more efficient and scalable than previous protocols for the same problem.

Conclusions

The proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0389-x) contains supplementary material, which is available to authorized users.

Surveillance of Medication Use During Pregnancy in the Mini-Sentinel Program

OBJECTIVES

Mini-Sentinel is a pilot project sponsored by the U.S. Food and Drug Administration to create an active surveillance system to monitor the safety of FDA-regulated medical products. We assessed the capability of the Mini-Sentinel pilot to provide prevalence rates of medication use among pregnant women delivering a liveborn infant.

METHODS

An algorithm was developed to identify pregnancies for a reusable analytic tool to be executed against the Mini-Sentinel Distributed Database. Diagnosis and procedure codes were used to identify women ages 10-54 years delivering a liveborn infant between April 2001 and December 2012. A comparison group of age- and date-matched nonpregnant women was identified. The analytic code was distributed to all 18 Mini-Sentinel data partners. The use of specific medications, selected because of concerns about their safe use during pregnancy, was identified from outpatient dispensing data. We determined the frequency of pregnancy episodes and nonpregnant episodes exposed to medications of interest, any time during the pregnant/matched nonpregnant period, and during each trimester.

RESULTS

The analytic tool successfully identified 1,678,410 live birth deliveries meeting the eligibility criteria. The prevalence of use at any time during pregnancy was 0.38 % for angiotensin-converting enzyme inhibitors and 0.22 % for statins. For ≤0.05 % of pregnancy episodes, the woman was dispensed warfarin, methotrexate, ribavirin, or mycophenolate.

CONCLUSIONS

The analytic tool developed for this study can be used to assess the use of medications during pregnancy as safety issues arise, and is adaptable to include different medications, observation periods, pre-existing conditions, and enrollment criteria.

Cross border semantic interoperability for learning health systems: The EHR4CR semantic resources and services

With the development of platforms enabling the integration and use of phenome, genome, and exposome data in the context of international research, data management challenges are increasing, and scalable solutions for cross border and cross domain semantic interoperability need to be developed. Reusing routinely collected clinical data, especially, requires computable portable phenotype algorithms running across different electronic health record (EHR) products and healthcare systems. We propose a framework for describing and comparing mediation platforms enabling cross border phenotype identification within federated EHRs. This framework was used to describe the experience gained during the EHR4CR project and the evaluation of the platform developed for accessing semantically equivalent data elements across 11 European participating EHR systems from 5 countries. Developers of semantic interoperability platforms are beginning to address a core set of requirements in order to reach the goal of developing cross border semantic integration of data.

The feasibility of using multiple databases to study rare outcomes: the potential effect of long-acting beta agonists with inhaled corticosteroid therapy on asthma mortality

PURPOSE

Long-acting beta agonists (LABAs) when used without concomitant inhaled corticosteroids (ICS) increase the risk of asthma-related deaths, but the effect on asthma-related death of LABA used in combination with ICS therapy is unknown. To address this question, we explored the feasibility of conducting an observational study using multiple US health care data sources.

METHODS

Retrospective cohort study to evaluate the likelihood of getting an upper 95% confidence limit ≤1.4 for the asthma mortality rate ratio and ≤0.40 per 10 000 person-years for the mortality rate difference, assuming no effect of new use of combined LABA + ICS (versus non-LABA maintenance therapy) on asthma mortality. Ten research institutions executed centrally distributed analytic code based on a standard protocol using an extracted (2000-2010) persistent asthma cohort (asthma diagnosis and ≥4 asthma medications in 12 months). Pooled results were analyzed by the coordinating center. Asthma deaths were ascertained by linkage with the National Death Index.

RESULTS

In a cohort of 994 627 persistent asthma patients (2.4 million person-years; 278 asthma deaths), probabilities of the upper 95% confidence limit for effect estimates being less than targeted values, assuming a null relation, were about 0.05. Modifications in cohort and exposure definitions increased exposed person-time and outcome events, but study size remained insufficient to attain study goals.

CONCLUSIONS

Even with 10 data sources and a 10-year study period, the rarity of asthma deaths among patients using certain medications made it infeasible to study the association between combined LABA + ICS and asthma mortality with our targeted level of study precision. Copyright © 2016 John Wiley & Sons, Ltd.

PIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms

Adaptive, seamless, multisponsor, multitherapy clinical trial designs executed as large scale platforms, could create superior evidence more efficiently than single-sponsor, single-drug trials. These trial PIPELINEs also could diminish barriers to trial participation, increase the representation of real-world populations, and create systematic evidence development for learning throughout a therapeutic life cycle, to continually refine its use. Comparable evidence could arise from multiarm design, shared comparator arms, and standardized endpoints-aiding sponsors in demonstrating the distinct value of their innovative medicines; facilitating providers and patients in selecting the most appropriate treatments; assisting regulators in efficacy and safety determinations; helping payers make coverage and reimbursement decisions; and spurring scientists with translational insights. Reduced trial times and costs could enable more indications, reduced development cycle times, and improved system financial sustainability. Challenges to overcome range from statistical to operational to collaborative governance and data exchange.

Use of selective serotonin reuptake inhibitors (SSRIs) in women delivering liveborn infants and other women of child-bearing age within the U.S. Food and Drug Administration's Mini-Sentinel program

This study was conducted in order to assess the prevalence of use of selective serotonin reuptake inhibitors (SSRIs) among pregnant women delivering a liveborn infant in the USA. A retrospective study was conducted using the automated databases of 15 health-care systems participating in the Mini-Sentinel program. Diagnosis and procedure codes were used to identify women ages 10 to 54 years delivering a liveborn infant between April 2001 and December 2013. A comparison group of age- and date-matched women without live births was identified. The frequency of use of SSRIs was identified from outpatient dispensing data. Among the 1,895,519 liveborn deliveries, 113,689 women (6.0 %) were exposed to an SSRI during pregnancy during the period 2001-2013; 5.4 % were exposed to an SSRI during 2013. During the corresponding time period, 10.5 % of the age- and date-matched cohort of women without live births was exposed to an SSRI, with 10.1 % exposed to an SSRI during 2013. The most common agents dispensed during pregnancy were sertraline (n = 48,678), fluoxetine (n = 28,983), and citalopram (n = 20,591). Among those women exposed to an SSRI during pregnancy, 53.8 % had a diagnosis of depression and 37.3 % had a diagnosis of an anxiety disorder during pregnancy or within 180 days prior to pregnancy. Our finding that 6 % of women with live births were prescribed SSRIs during pregnancy highlights the importance of understanding the differential effects of these medications and other therapeutic options on the developing fetus and on the pregnant women.

The Role of Hemoglobin Laboratory Test Results for the Detection of Upper Gastrointestinal Bleeding Outcomes Resulting from the Use of Medications in Observational Studies

INTRODUCTION

The identification of upper gastrointestinal (UGI) bleeding and perforated ulcers in claims data typically relies on inpatient diagnoses. The use of hemoglobin laboratory results might increase the detection of UGI events that do not lead to hospitalization.

OBJECTIVES

Our objective was to evaluate whether hemoglobin results increase UGI outcome identification in electronic databases, using non-steroidal anti-inflammatory drugs (NSAIDs) as a test case.

METHODS

From three data partner sites within the Mini-Sentinel Distributed Database, we identified NSAID initiators aged ≥18 years between 2008 and 2013. Numbers of events and risks within 30 days after NSAID initiation were calculated for four mutually exclusive outcomes: (1) inpatient UGI diagnosis of bleeding or gastric ulcer (standard claims-based definition without laboratory results); (2) non-inpatient UGI diagnosis AND ≥3 g/dl hemoglobin decrease; (3) ≥3 g/dl hemoglobin decrease without UGI diagnosis in any clinical setting; (4) non-inpatient UGI diagnosis, without ≥3 g/dl hemoglobin decrease.

RESULTS

We identified 2,289,772 NSAID initiators across three sites. Overall, 45.3% had one or more hemoglobin result available within 365 days before or 30 days after NSAID initiation; only 6.8% had results before and after. Of 7637 potential outcomes identified, outcome 1 accounted for 21.7%, outcome 2 for 0.8%, outcome 3 for 34.3%, and outcome 4 for 43.3%. Potential cases identified by outcome 3 were largely not suggestive of UGI events. Outcomes 1, 2, and 4 had similar distributions of specific UGI diagnoses.

CONCLUSIONS

Using available hemoglobin result values combined with non-inpatient UGI diagnoses identified few additional UGI cases. Non-inpatient UGI diagnostic codes may increase outcome detection but would require validation.

Report of the National Heart, Lung, and Blood Institute Working Group: An Integrated Network for Congenital Heart Disease Research

The National Heart, Lung, and Blood Institute convened a working group in January 2015 to explore issues related to an integrated data network for congenital heart disease research. The overall goal was to develop a common vision for how the rapidly increasing volumes of data captured across numerous sources can be managed, integrated, and analyzed to improve care and outcomes. This report summarizes the current landscape of congenital heart disease data, data integration methodologies used across other fields, key considerations for data integration models in congenital heart disease, and the short- and long-term vision and recommendations made by the working group.