Empagliflozin and Risk of Incident Gout: Analysis from the EMPagliflozin Comparative Effectiveness and SafEty (EMPRISE) Cohort Study.

BACKGROUND: Hyperuricemia is frequently observed in patients with type 2 diabetes (T2D) and is associated with increased risk of gout and cardiovascular disease (CVD). Empagliflozin lowers serum urate levels by enhancing its urinary excretion. OBJECTIVE: To compare initiators of empagliflozin vs dipeptidyl peptidase-4 inhibitor (DPP4i) and initiators of empagliflozin vs glucagon-like peptide-1 receptor agonist (GLP-1RA) with respect to the risk of incident gout events. DESIGN AND PARTICIPANTS: Using three claims-based datasets from 08/2014 to 09/2019, we generated two cohorts (cohort 1: empagliflozin vs DPP4i; cohort 2: empagliflozin vs GLP-1RA) of adult patients with T2D and without prior history of gout or gout-specific medication dispensing separately in each dataset. To assess the risk of incident gout, we estimated hazard ratios (HR) and rate differences (RD) per 1000 person-years (PY) with their 95% confidence intervals (CI) before and after 1:1 propensity score (PS) matching adjusting for 141 baseline covariates. KEY RESULTS: We identified 102,262 pairs of 1:1 propensity score-matched adults in cohort 1 and 131,216 pairs in cohort 2. Over a mean follow-up period of 8 months on treatment, the risk of gout was lower in patients initiating empagliflozin compared to DPP4i (HR = 0.69: 95% CI (0.60-0.79); RD = - 2.27: 95% CI (- 3.08, 1.46)) or GLP-1RA (HR = 0.83: 95% CI (0.73-0.94); RD = - 0.99: 95% CI (- 1.66, - 0.32)). Results were consistent across subgroups (sex, age, body mass index, chronic kidney disease, heart failure, cardiovascular disease, and concurrent diuretic use) and sensitivity analyses. CONCLUSIONS: Among adults with T2D, the initiation of empagliflozin vs a DPP4i or GLP-1RA was associated with lower risk of incident gout, complementing results from a post hoc analysis of the EMPA-REG OUTCOME trial and previously published observational research focusing on the sodium-glucose co-transporter-2 inhibitor class in more narrowly defined study populations.

Improving the accuracy of automated gout flare ascertainment using natural language processing of electronic health records and linked Medicare claims data.

BACKGROUND: We aimed to determine whether integrating concepts from the notes from the electronic health record (EHR) data using natural language processing (NLP) could improve the identification of gout flares. METHODS: Using Medicare claims linked with EHR, we selected gout patients who initiated the urate-lowering therapy (ULT). Patients' 12-month baseline period and on-treatment follow-up were segmented into 1-month units. We retrieved EHR notes for months with gout diagnosis codes and processed notes for NLP concepts. We selected a random sample of 500 patients and reviewed each of their notes for the presence of a physician-documented gout flare. Months containing at least 1 note mentioning gout flares were considered months with events. We used 60% of patients to train predictive models with LASSO. We evaluated the models by the area under the curve (AUC) in the validation data and examined positive/negative predictive values (P/NPV). RESULTS: We extracted and labeled 839 months of follow-up (280 with gout flares). The claims-only model selected 20 variables (AUC = 0.69). The NLP concept-only model selected 15 (AUC = 0.69). The combined model selected 32 claims variables and 13 NLP concepts (AUC = 0.73). The claims-only model had a PPV of 0.64 [0.50, 0.77] and an NPV of 0.71 [0.65, 0.76], whereas the combined model had a PPV of 0.76 [0.61, 0.88] and an NPV of 0.71 [0.65, 0.76]. CONCLUSION: Adding NLP concept variables to claims variables resulted in a small improvement in the identification of gout flares. Our data-driven claims-only model and our combined claims/NLP-concept model outperformed existing rule-based claims algorithms reliant on medication use, diagnosis, and procedure codes.

Prognostic Factors of COVID-19: An Umbrella Review Endorsed by the International Society for Pharmacoepidemiology.

During the coronavirus disease 2019 (COVID-19) pandemic, the urgency for updated evidence to inform public health and clinical care placed systematic literature reviews (SLRs) at the cornerstone of research. We aimed to summarize evidence on prognostic factors for COVID-19 outcomes through published SLRs and to critically assess quality elements in the findings' interpretation. An umbrella review was conducted via electronic databases from January 2020 to April 2022. All SLRs (and meta-analyses) in English were considered. Data screening and extraction were conducted by two independent reviewers. AMSTAR 2 tool was used to assess SLR quality. The study was registered with PROSPERO (CRD4202232576). Out of 4,564 publications, 171 SLRs were included of which 3 were umbrella reviews. Our primary analysis included 35 SLRs published in 2022, which incorporated studies since the beginning of the pandemic. Consistent findings showed that, for adults, older age, obesity, heart disease, diabetes, and cancer were more strongly predictive of risk of hospitalization, intensive care unit admission, and mortality due to COVID-19. Male sex was associated with higher risk of short-term adverse outcomes, but female sex was associated with higher risk of long COVID. For children, socioeconomic determinants that may unravel COVID-19 disparities were rarely reported. This review highlights key prognostic factors of COVID-19, which can help clinicians and health officers identify high-risk groups for optimal care. Findings can also help optimize confounding adjustment and patient phenotyping in comparative effectiveness research. A living SLR approach may facilitate dissemination of new findings. This paper is endorsed by the International Society for Pharmacoepidemiology.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.

INTRODUCTION: Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance. METHODS: To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices. RESULTS: We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations. CONCLUSION: Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses: Results of 32 Clinical Trials.

Importance: Nonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates. Objective: To emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time [PICOT]) and to quantify agreement in RCT-database study pairs. Design, Setting, and Participants: New-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022. Exposures: Therapies for multiple clinical conditions were included. Main Outcomes and Measures: Database study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference. Results: In these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement). Conclusions and Relevance: Real-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.

Real-World Evidence Prediction of a Phase IV Oncology Trial: Comparative Degarelix vs Leuprolide Safety.

BACKGROUND: Medical and regulatory communities are increasingly interested in the utility of real-world evidence (RWE) for answering questions pertaining to drug safety and effectiveness but concerns about validity remain. A principled approach to conducting RWE studies may alleviate concerns and increase confidence in findings. This study sought to predict the findings from the PRONOUNCE trial using a principled approach to generating RWE. METHODS: This propensity-score (PS) matched observational cohort study utilized 3 claims databases to compare the occurrence of major adverse cardiovascular events (MACE) among initiators of degarelix vs. leuprolide. Patients were included if they had history of prostate cancer and atherosclerotic cardiovascular disease. Subjects were excluded if they didn't have continuous database enrollment in the year prior to treatment initiation, were exposed to androgen deprivation therapy or experienced an acute cardiovascular event within 30 days prior to treatment initiation, or had a history or risk factors of QT prolongation. RESULTS: There were 12,448 leuprolide and 1,969 degarelix study-eligible patients before matching, with 1,887 in each arm after PS-matching. The results for MACE comparing degarelix to leuprolide in the observational analysis (hazard ratio= 1.35; 95% confidence interval = 0.94-1.93) was consistent with the subsequently released PRONOUNCE result (hazard ratio = 1.28; 95% confidence interval = 0.59-2.79). CONCLUSIONS: This study successfully predicted the result of a comparative cardiovascular safety trial in the oncology setting. Although the findings are encouraging, limitations of measuring cancer stage and tumor progression are representative of challenges in attempting to generalize whether claims-based RWE can be used as actionable evidence.

Gastrointestinal prophylaxis for COVID-19: an illustration of severe bias arising from inappropriate comparators in observational studies.

OBJECTIVES: We aimed to use setting-appropriate comparisons to estimate the effects of different gastrointestinal (GI) prophylaxis pharmacotherapies for patients hospitalized with COVID-19 and setting-inappropriate comparisons to illustrate how improper design choices could result in biased results. STUDY DESIGN AND SETTING: We identified 3,804 hospitalized patients aged ≥ 18 years with COVID-19 from March to November 2020. We compared the effects of different gastroprotective agents on clinical improvement of COVID-19, as measured by a published severity scale. We used propensity score-based fine-stratification for confounding adjustment. Based on guidelines, we prespecified comparisons between agents with clinical equipoise and inappropriate comparisons of users vs. nonusers of GI prophylaxis in the intensive care unit (ICU). RESULTS: No benefit was detected when comparing oral famotidine to omeprazole in patients treated in the general ward or ICUs. We also found no associations when comparing intravenous famotidine to intravenous pantoprazole. For inappropriate comparisons of users vs. nonusers in the ICU, the probability of improvement was reduced by 32%-45% in famotidine users and 21%-48% in omeprazole or pantoprazole users. CONCLUSION: We found no evidence that GI prophylaxis improved outcomes for patients hospitalized with COVID-19 in setting-appropriate comparisons. An improper comparator choice can lead to spurious associations in critically ill patients.

Cardiovascular and Mortality Risk with Intravitreal Vascular Endothelial Growth Factor Inhibitors in Patients with Diabetic Retinopathy.

OBJECTIVE: To investigate the cardiovascular (CV) safety associated with intravitreal anti-VEGF injections (IAVIs) in patients with diabetic retinopathy (DR). DESIGN: Population-based cohort study using Medicare and 2 commercial insurance claims databases in the United States from January 2009 to December 2017. SUBJECTS: Patients with DR aged ≥ 18 years in whom treatment with either IVAIs or laser procedure or intravitreal steroid injections was initiated. METHODS: We estimated the propensity score (PS) using multivariable logistic regression models, including 85 baseline covariates and PS-matched patients in a 1:1 ratio. We estimated the pooled hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analyses based on prior history of CV events were also conducted. MAIN OUTCOME MEASURES: A composite CV outcome of myocardial infarction (MI) or stroke, its individual components, and all-cause mortality in 180 and 365 days after treatment initiation. RESULTS: We identified 61 508 PS-matched patients in a 1:1 ratio in whom either IVAIs or laser or steroid treatment was initiated. Compared with laser or steroid treatment, IAVIs were not associated with an increased risk of the composite CV outcome (HR, 0.95; 95% CI, 0.83-1.09), MI (HR, 0.93; 95% CI, 0.76-1.13), or stroke (HR, 0.98; 95% CI, 0.80-1.19) or the risk of all-cause mortality (HR, 1.25; 95% CI, 0.97-1.62) at 180 days of follow-up. At 365 days, the risk of the composite CV outcome, stroke, and MI remained similar between the 2 groups, although the risk of all-cause mortality was increased with IAVIs (HR, 1.35; 95% CI, 1.14-1.60). The subgroup analysis showed that the risk of all-cause mortality was increased in patients with a prior history of CV events. CONCLUSIONS: Among > 60 000 patients with DR, those who received IAVIs had a risk of CV events similar to those who received laser or steroid treatment. However, the risk of all-cause mortality was higher in patients who received IAVIs for DR.