Sequential Data-Mining for Adverse Events After Recombinant Herpes Zoster Vaccination Using the Tree-Based Scan Statistic.

Tree-based scan statistics have been successfully used to study the safety of several vaccines without prespecifying health outcomes of concern. In this study, the binomial tree-based scan statistic was applied sequentially to detect adverse events in days 1-28 compared with days 29-56 after recombinant herpes zoster (RZV) vaccination, with 5 looks at the data and formal adjustment for the repeated analyses over time. IBM MarketScan data on commercially insured persons ≥50 years of age receiving RZV during January 1, 2018, to May 5, 2020, were used. With 999,876 doses of RZV included, statistically significant signals were detected only for unspecified adverse effects/complications following immunization, with attributable risks as low as 2 excess cases per 100,000 vaccinations. Ninety percent of cases in the signals occurred in the week after vaccination and, based on previous studies, likely represent nonserious events like fever, fatigue, and headache. Strengths of our study include its untargeted nature, self-controlled design, and formal adjustment for repeated testing. Although the method requires prespecification of the risk window of interest and may miss some true signals detectable using the tree-temporal variant of the method, it allows for early detection of potential safety problems through early initiation of ongoing monitoring.

Understanding the exceptional pre-vaccination Era East Asian COVID-19 outcomes.

During the first year of the pandemic, East Asian countries have reported fewer infections, hospitalizations, and deaths from COVID-19 disease than most countries in Europe and the Americas. Our goal in this paper is to generate and evaluate hypothesis that may explain this striking fact. We consider five possible explanations: (1) population age structure (younger people tend to have less severe COVID-19 disease upon infection than older people); (2) the early adoption of lockdown strategies to control disease spread; (3) genetic differences between East Asian population and European and American populations that confer protection against COVID-19 disease; (4) seasonal and climactic contributors to COVID-19 spread; and (5) immunological differences between East Asian countries and the rest of the world. The evidence suggests that the first four hypotheses are unlikely to be important in explaining East Asian COVID-19 exceptionalism. Lockdowns, in particular, fail as an explanation because East Asian countries experienced similarly good infection outcomes despite vast differences in lockdown policies adopted by different countries to control the COVID-19 epidemic. The evidence to date is consistent with our fifth hypothesis - pre-existing immunity unique to East Asia - but there are still essential parts of this story left for scientists to check.

As Omicron Takes Hold and Other New Variants Arise, COVID-19 Testing Remains the Universally Agreed Tool to Effect Transition From Pandemic to Endemic State.

The COVID-19 pandemic has caused more than 448 million cases and 6 million deaths worldwide to date. Omicron is now the dominant SARS-CoV-2 variant, making up more than 90% of cases in countries reporting sequencing data. As the pandemic continues into its third year, continued testing is a strategic and necessary tool for transitioning to an endemic state of COVID-19. Here, we address three critical topics pertaining to the transition from pandemic to endemic: defining the endemic state for COVID-19, highlighting the role of SARS-CoV-2 testing as endemicity is approached, and recommending parameters for SARS-CoV-2 testing once endemicity is reached. We argue for an approach that capitalizes on the current public health momentum to increase capacity for PCR-based testing and whole genome sequencing to monitor emerging infectious diseases. Strategic development and utilization of testing, including viral panels in addition to vaccination, can keep SARS-CoV-2 in a manageable endemic state and build a framework of preparedness for the next pandemic.

A Broad Safety Assessment of the Recombinant Herpes Zoster Vaccine.

The recombinant herpes zoster vaccine (RZV), approved as a 2-dose series in the United States in October 2017, has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome after vaccination was identified after approval, and questions remain about other possible adverse events. This data-mining study assessed RZV safety in the United States using the self-controlled tree-temporal scan statistic, scanning data on thousands of diagnoses recorded during follow-up to detect any statistically unusual temporal clustering of cases within a large hierarchy of diagnoses. IBM MarketScan data on commercially insured persons at least 50 years of age receiving RZV between January 1, 2018, and May 5, 2020, were used, including 56 days of follow-up; 1,014,329 doses were included. Statistically significant clustering was found within a few days of vaccination for unspecified adverse effects, complications, or reactions to immunization or other medical substances/care; fever; unspecified allergy; syncope/collapse; cellulitis; myalgia; and dizziness/giddiness. These findings are consistent with the known safety profile of this and other injected vaccines. No cluster of Guillain-Barré syndrome was detected, possibly due to insufficient sample size. This signal-detection method has now been applied to 5 vaccines, with consistently plausible results, and seems a promising addition to vaccine-safety evaluation methods.

Mining Clinical Data for Novel Posttraumatic Stress Disorder Medications.

BACKGROUND: Despite the prevalence and negative impact of posttraumatic stress disorder (PTSD), there are few medications approved by the U.S. Food and Drug Administration for treatment, and approved medications do not work well enough. We leveraged large-scale electronic health record data to identify existing medications that may be repurposed as PTSD treatments. METHODS: We constructed a mechanistic tree of all Food and Drug Administration-approved medications and used the tree-based scan statistic to identify medications associated with greater than expected levels of clinically meaningful improvement in PTSD symptoms using electronic health record data from the U.S. Department of Veterans Affairs. Our cohort included patients with a diagnosis of PTSD who had repeated symptom measurements using the PTSD Checklist over a 20-year period (N = 168,941). We calculated observed numbers based on patients taking each drug or mechanistically related class of drugs and the expected numbers based on the tree as a whole. RESULTS: Medications typically used to treat PTSD, such as the Food and Drug Administration-approved agent sertraline, were associated with improvement in PTSD symptoms, but the effects were small. Several, but not all, direct-acting antivirals used in the treatment of hepatitis C virus demonstrated a strong association with PTSD improvement. The finding was robust to a sensitivity analysis excluding patients who received established PTSD treatments, including trauma-focused psychotherapy, concurrent with hepatitis treatment. CONCLUSIONS: Our exploratory approach both demonstrated findings that are consistent with what is known about pharmacotherapy for PTSD and uncovered a novel class of medications that may improve PTSD symptoms.

A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes.

BACKGROUND: Clinical trials are often underpowered to detect serious but rare adverse events of a new medication. We applied a novel data mining tool to detect potential adverse events of canagliflozin, the first sodium glucose co-transporter 2 (SGLT2 inhibitor) in the United States, using real-world data from shortly after its market entry and before public awareness of its potential safety concerns. METHODS: In a U. S. commercial claims dataset (29 March 2013-30 Sept 2015), two pairwise cohorts of patients over 18 years of age with type 2 diabetes (T2D) who were newly dispensed canagliflozin or an active comparator, that is a dipeptidyl peptidase 4 inhibitor (DPP4) or a glucagon-like peptide 1 receptor agonist (GLP1), were identified and propensity score-matched. We used variable ratio matching with up to four people receiving a DPP4 or GLP1 for each person receiving canagliflozin. We identified potential safety signals using a hierarchical tree-based scan statistic data mining method with the hierarchical outcome tree constructed based on international classification of disease coding. We screened for incident adverse events where there were more outcomes observed among canagliflozin vs. comparator initiators than expected by chance, after adjusting for multiple testing. RESULTS: We identified two pairwise propensity score variable ratio matched cohorts of 44,733 canagliflozin vs. 99,458 DPP4 initiators, and 55,974 canagliflozin vs. 74,727 GLP1 initiators. When we screened inpatient and emergency room diagnoses, diabetic ketoacidosis was the only severe adverse event associated with canagliflozin initiation with p < .05 in both cohorts. When outpatient diagnoses were also considered, signals for female and male genital infections emerged in both cohorts (p < .05). CONCLUSIONS AND RELEVANCE: In a large population-based study, we identified known but no other adverse events associated with canagliflozin, providing reassurance on its safety among adult patients with T2D and suggesting the tree-based scan statistic method is a useful post-marketing safety monitoring tool for newly approved medications.

Exact sequential test for clinical trials and post-market drug and vaccine safety surveillance with Poisson and binary data.

In sequential analysis, hypothesis testing is performed repeatedly in a prospective manner as data accrue over time to quickly arrive at an accurate conclusion or decision. In this tutorial paper, detailed explanations are given for both designing and operating sequential testing. We describe the calculation of exact thresholds for stopping or signaling, statistical power, expected time to signal, and expected sample sizes for sequential analysis with Poisson and binary type data. The calculations are run using the package Sequential, constructed in R language. Real data examples are inspired on clinical trials practice, such as the current efforts to develop treatments to face the COVID-19 pandemic, and the comparison of treatments of osteoporosis. In addition, we mimic the monitoring of adverse events following influenza vaccination and Pediarix vaccination.

Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June-July 2020.

A surveillance system that uses census tract resolution and the SaTScan prospective space-time scan statistic detected clusters of increasing severe acute respiratory syndrome coronavirus 2 test percent positivity in New York City, NY, USA. Clusters included one in which patients attended the same social gathering and another that led to targeted testing and outreach.

A General Propensity Score for Signal Identification Using Tree-Based Scan Statistics.

The tree-based scan statistic (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) is a data-mining method that adjusts for multiple testing of correlated hypotheses when screening thousands of potential adverse events for signal identification. Simulation has demonstrated the promise of TreeScan with a propensity score (PS)-matched cohort design. However, it is unclear which variables to include in a PS for applied signal identification studies to simultaneously adjust for confounding across potential outcomes. We selected 4 pairs of medications with well-understood safety profiles. For each pair, we evaluated 5 candidate PSs with different combinations of 1) predefined general covariates (comorbidity, frailty, utilization), 2) empirically selected (data-driven) covariates, and 3) covariates tailored to the drug pair. For each pair, statistical alerting patterns were similar with alternative PSs (≤11 alerts in 7,996 outcomes scanned). Inclusion of covariates tailored to exposure did not appreciably affect screening results. Inclusion of empirically selected covariates can provide better proxy coverage for confounders but can also decrease statistical power. Unlike tailored covariates, empirical and predefined general covariates can be applied "out of the box" for signal identification. The choice of PS depends on the level of concern about residual confounding versus loss of power. Potential signals should be followed by pharmacoepidemiologic assessment where confounding control is tailored to the specific outcome(s) under investigation.

A Broad Safety Assessment of the 9-Valent Human Papillomavirus Vaccine.

Parents indicate that safety is their top concern about human papillomavirus (HPV) vaccination. A data-mining method not requiring prespecification of health outcome(s) or postexposure period(s) of potentially increased risk can be used to identify possible associations between an exposure and any of thousands of medically attended health outcomes; this method was applied to data on the 9-valent HPV vaccine (HPV9) to detect potential safety problems. Data on 9- to 26-year-olds who had received HPV9 vaccine between November 4, 2016, and August 5, 2018, inclusive, were extracted from the MarketScan database and analyzed for statistically significant clustering of incident diagnoses within the hierarchy of diagnoses coded using the International Classification of Diseases and temporally within the 1 year after vaccination, using the self-controlled tree-temporal scan statistic and TreeScan software. Only 56 days of postvaccination enrollment was required; subsequent follow-up was censored at disenrollment. Multiple testing was adjusted for. The analysis included 493,089 doses of HPV9. Almost all signals resulted from temporal confounding, not unexpected with a 1-year follow-up period. The only plausible signals were for nonspecific adverse events (e.g., injection-site reactions, headache) on days 1-2 after vaccination, with attributable risks as low as 1 per 100,000 vaccinees. Considering the broad scope of the evaluation and the high statistical power, the findings of no specific serious adverse events should provide reassurance about this vaccine's safety.

Active Surveillance of the Safety of Medications Used During Pregnancy.

The scientific community relies on postmarketing approaches to define the risk of using medications in pregnancy because information available at the time of drug approval is limited. Most studies carried out in pregnancy focus on a single outcome or selected outcomes. However, women must balance the benefit of treatment against all possible adverse effects. We aimed to apply and evaluate a tree-based scan statistic data-mining method (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) as a safety surveillance approach that allows for simultaneous evaluation of a comprehensive range of adverse pregnancy outcomes, while preserving the overall rate of false-positive alerts. We evaluated TreeScan with a cohort design and adjustment via propensity score techniques, using 2 test cases: 1) opioids and neonatal opioid withdrawal syndrome and 2) valproate and congenital malformations, implemented in pregnancy cohorts nested within the Medicaid Analytic eXtract (January 1, 2000-December 31, 2014) and the IBM MarketScan Research Database (IBM, Armonk, New York) (January 1, 2003-September 30, 2015). In both cases, we identified known safety concerns, with only 1 previously unreported alert at the preset statistical alerting threshold. This evaluation shows the promise of TreeScan-based approaches for systematic drug safety monitoring in pregnancy. A targeted screening approach followed by deeper investigation to refine understanding of potential signals will ensure that pregnant women and their physicians have access to the best available evidence to inform treatment decisions.

Screening Medications for Association with Progression to Wet Age-Related Macular Degeneration.

PURPOSE: There is an urgent need for treatments that prevent or delay development to advanced age-related macular degeneration (AMD). Drugs already on the market for other conditions could affect progression to neovascular AMD (nAMD). If identified, these drugs could provide insights for drug development targets. The objective of this study was to use a novel data mining method that can simultaneously evaluate thousands of correlated hypotheses, while adjusting for multiple testing, to screen for associations between drugs and delayed progression to nAMD. DESIGN: We applied a nested case-control study to administrative insurance claims data to identify cases with nAMD and risk-set sampled controls that were 1:4 variable ratio matched on age, gender, and recent healthcare use. PARTICIPANTS: The study population included cases with nAMD and risk set matched controls. METHODS: We used a tree-based scanning method to evaluate associations between hierarchical classifications of drugs that patients were exposed to within 6 months, 7 to 24 months, or ever before their index date. The index date was the date of first nAMD diagnosis in cases. Risk-set sampled controls were assigned the same index date as the case to which they were matched. The study was implemented using Medicare data from New Jersey and Pennsylvania, and national data from IBM MarketScan Research Database. We set an a priori threshold for statistical alerting at P ≤ 0.01 and focused on associations with large magnitude (relative risks ≥ 2.0). MAIN OUTCOME MEASURES: Progression to nAMD. RESULTS: Of approximately 4000 generic drugs and drug classes evaluated, the method detected 19 distinct drug exposures with statistically significant, large relative risks indicating that cases were less frequently exposed than controls. These included (1) drugs with prior evidence for a causal relationship (e.g., megestrol); (2) drugs without prior evidence for a causal relationship, but potentially worth further exploration (e.g., donepezil, epoetin alfa); (3) drugs with alternative biologic explanations for the association (e.g., sevelamer); and (4) drugs that may have resulted in statistical alerts due to their correlation with drugs that alerted for other reasons. CONCLUSIONS: This exploratory drug-screening study identified several potential targets for follow-up studies to further evaluate and determine if they may prevent or delay progression to advanced AMD.

Leveraging epidemiological principles to evaluate Sweden's COVID-19 response.

In the response to COVID-19, countries have implemented response strategies along a continuum of population- and venue-level specificity ranging from suppression to mitigation strategies. Suppression strategies generally include population-wide shelter-in-place mandates or lockdowns, closure of nonessential physical venues, travel bans, testing and contact tracing, and quarantines. Sweden followed a mitigation strategy focused on risk-tailored approaches to mitigate specific acquisition risks among the elderly, minimizing the disruption to education and the delivery of other health care services, and recommendations for social distancing to minimize the disease burden. To date, Sweden has reported higher case counts and attributable mortality than other Scandinavian countries and lower than other Northern European countries. However, there are several limitations with comparison given heterogeneity in testing strategies, suspected and confirmed case definitions, and assessment of attributable mortality. The decisions in Sweden also reflect social priorities such as equity being a foundational principle of Swedish social systems. Consistently, in-person education for those aged less than 16 years continued throughout. Notably, the mitigation strategy did not eliminate the inequitable impacts of COVID-19 cases and mortality in Sweden with higher-exposure and generally lower-income occupations being associated with higher risks intersecting with these communities often residing in more dense multigenerational households. From January 1 to November 15, there has been a 1.8% increase in all-cause mortality in 2020 compared with the average of 2015-2019, representing an excess of 14.3 deaths per 100,000 population. However, the final assessment of excess deaths in Sweden in 2020 including stratification by age and integration of secular trends can only be calculated in the coming years. In response to increasing cases in the fall of 2020, Sweden has continued to leverage business-oriented regulations and public-oriented guidelines for social distancing rather than police-enforced mandates. Ultimately, pandemics present no winners. Countries have implemented a range of different COVID-19 prevention and mitigation strategies responsive to their own priorities and legal systems including equity and the balancing of competing health priorities. Given these varied approaches, countries that pursued elimination, suppression, or mitigation strategies can collaboratively learn from both successes and challenges of the different strategies to inform COVID-19 and future pandemic responses.

An Overview Of Vaccine Development, Approval, And Regulation, With Implications For COVID-19.

The Food and Drug Administration (FDA) approves vaccines when their benefits outweigh the risks for their intended use. In this article we review the standard FDA approach to vaccine evaluation, which underpins its current approaches to assessment of vaccines to prevent coronavirus disease 2019 (COVID-19). The FDA has established pathways to accelerate vaccine availability before approval, such as Emergency Use Authorization, and to channel resources to high-priority products and allow more flexibility in the evidence required for approval, including accelerated approval based on surrogate markers of effectiveness. Among the thirty-five new vaccines approved in the US from 2006 through October 2020, about two-thirds of their pivotal trials used the surrogate outcome of immune system response, and only one-third evaluated actual disease incidence. Postapproval safety surveillance of new vaccines, and particularly vaccines receiving expedited approval, is crucial. This has generally been accomplished through such mechanisms as the Centers for Disease Control and Prevention (CDC) and FDA Vaccine Adverse Event Reporting System, the CDC Vaccine Safety Datalink, and the CDC Clinical Immunization Safety Assessment Project. Adverse events detected in this way may lead to changes in a vaccine's recommended use or withdrawal from the market. Regulatory oversight of new vaccines must balance speed with rigor to effectively address the pandemic.

Data Mining for Adverse Events of Tumor Necrosis Factor-Alpha Inhibitors in Pediatric Patients: Tree-Based Scan Statistic Analyses of Danish Nationwide Health Data.

BACKGROUND AND OBJECTIVES: Tumor necrosis factor-alpha (TNF-α) inhibitors are efficacious and considered generally safe in adults. However, pediatric-specific safety evidence is scarce. The aim of this study was to screen for signals of previously unknown adverse events of TNF-α inhibitors in pediatric patients. METHODS: We conducted a data-mining study based on routinely collected, nationwide Danish healthcare data for 2004-2016. Using tree-based scan statistics to identify events with unexpectedly high incidence during TNF-α inhibitor use among patients with inflammatory bowel disease or juvenile idiopathic arthritis, two analyses were performed: comparison with episodes of no use and with other time periods from the same patient. Based on incident physician-assigned diagnosis codes from outpatient and inpatient visits in specialist care, we screened thousands of potential adverse events while adjusting for multiple testing. RESULTS: We identified 1310 episodes of new TNF-α inhibitor use that met the eligibility criteria. Two signals of adverse events of TNF-α inhibitors, as compared with no use, were detected. First, there were excess events of dermatologic complications (ICD-10: L00-L99, 87 vs. 44 events, risk difference [RD] 3.3%), which have been described previously in adults and children. Second, there were excess events of psychiatric diagnosis adjustment disorders (ICD-10: F432, 33 vs. 7 events, RD 2.0%), which was likely associated with the underlying disease and its severity, rather than with the treatment. The self-controlled analysis generated no signal. CONCLUSIONS: No signals of previously unknown adverse events of TNF-α inhibitors in pediatric patients were detected. The study showed that real-world data and newly developed methods for adverse events data mining can play a particularly important role in pediatrics where pre-approval drug safety data are scarce.

Individual and Neighborhood Factors Associated With Failure to Vaccinate Against Influenza During Pregnancy.

Uptake of influenza vaccine among pregnant women remains low. We investigated whether unvaccinated pregnant women were clustered geographically and determined factors associated with failure to vaccinate using spatial and multivariate logistic regression analyses. Pregnant women who were members of Kaiser Permanente Northern California in 2015 or 2016 were included in the study. More than half (53%) of the 77,607 included pregnant women were unvaccinated. Spatial analysis identified 5 clusters with a high prevalence of unvaccinated pregnant women. The proportion of unvaccinated women ranged from 57% to 75% within clusters as compared with 51% outside clusters. In covariate-adjusted analyses, residence in a cluster was associated with a 41% increase in the odds of being unvaccinated (odds ratio (OR) = 1.41, 95% confidence interval (CI): 1.36, 1.46). The odds of being unvaccinated were greater for Black women (OR = 1.58, 95% CI: 1.49, 1.69), Hispanic women (OR = 1.15, 95% CI: 1.05, 1.25), women with subsidized health insurance (OR = 1.18, 95% CI: 1.11, 1.24), women with fewer than 5 prenatal-care visits (OR = 1.85, 95% CI: 1.60, 2.16), and neighborhoods with a high deprivation index (fourth quartile vs. first: OR = 1.14, 95% CI: 1.07, 1.21). In conclusion, unvaccinated pregnant women were clustered geographically and by key sociodemographic factors. These findings suggest that interventions to increase influenza vaccine coverage among pregnant women are needed, particularly in vulnerable populations.

Determining Which of Several Simultaneously Administered Vaccines Increase Risk of an Adverse Event.

INTRODUCTION: Childhood immunization schedules often involve multiple vaccinations per visit. When increased risk of an adverse event is observed after simultaneous (same-day) vaccinations, it can be difficult to ascertain which triggered the adverse event. This methods paper discusses a systematic process to determine which of the simultaneously administered vaccine(s) are most likely to have caused an observed increase in risk of an adverse event. METHODS: We use an example from the literature where excess risk of seizure was observed 1 day after vaccination, but same-day vaccination patterns made it difficult to discern which vaccine(s) may trigger the adverse event. We illustrate the systematic identification process using a simulation that retained the observed pattern of simultaneous vaccination in an empirical cohort of vaccinated children. We simulated "true" effects for diphtheria-tetanus-acellular pertussis (DTaP) and pneumococcal conjugate (PCV) on risk of seizure the day after vaccination. We varied the independent and interactive effects of vaccines (on the multiplicative scale). After applying the process to simulated data, we evaluated risk of seizure 1 day after vaccination in the empirical cohort. RESULTS: In all simulations, we were able to determine which vaccines contributed to excess risk. In the empirical data, we narrowed the association with seizure from all vaccines in the schedule to three likely candidates, DTaP, PCV, and/or Haemophilus influenzae type B (HiB) (p < 0.01, attributable risk when all three were administered together: five per 100,000). Disentangling their associations with seizure would require a larger sample or more variation in the combinations administered. When none of these three were administered, no excess risk was observed. CONCLUSION: The process outlined could provide valuable information on the magnitude of potential risk from individual and simultaneousvaccinations. Associations should be further investigated with independent data as well as biologically based, statistically independent hypotheses.

Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs.

OBJECTIVE: To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms. DESIGN: Multicenter retrospective cohort study. SETTING: The study included 43 hospitals using a common infection prevention surveillance system. METHODS: A space-time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods. RESULTS: We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals' surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work. CONCLUSIONS: Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

The EMPagliflozin compaRative effectIveness and SafEty (EMPRISE) study programme: Design and exposure accrual for an evaluation of empagliflozin in routine clinical care.

BACKGROUND: The EMPA-REG OUTCOME trial showed that empagliflozin reduced the risk of cardiovascular death and hospitalization for heart failure (HHF) in diabetic patients with cardiovascular disease. EMPRISE is a study programme on the effectiveness, safety and healthcare utilization of empagliflozin in routine care, leveraging real-world data from two commercial and one federal US data sources from 2014 to 2019. OBJECTIVES: To describe rationale and design of EMPRISE, assess ability to minimize confounding and evaluate the time to reach sufficient statistical power for a key study outcome, HHF, using baseline information from the first year of EMPRISE. METHODS: In 3 claims data sets, we identified a 1:1 propensity score (PS)-matched cohort of diabetic patients ≥18 years initiating empagliflozin or a dipeptidyl peptidase-4 inhibitor (DPP4i), resulting in 6643 total pairs. The PS model included >140 baseline covariates. We measured covariate balance via standardized differences (SD) and postmatching c-statistic. We computed the incidence rate (IR) of HHF, predicted exposure accrual over time and calculated expected power. RESULTS: After PS matching, patient characteristics were balanced with SD <0.1 and c-statistic between 0.54 and 0.59. The population IR of HHF was 4.4 per 1000 person-years using a specific HHF definition and 14.8 using a broader HHF definition. In our projection, 80%-powered analyses would require a minimum of 169 HHF events, expected to accumulate by year 3 (specific definition) or year 2 (broader definition). CONCLUSION: Baseline information from EMPRISE provided evidence of solid confounding control and adequate exposure accrual with expected powered analyses for the primary outcomes.