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.

Development and Validation of Algorithms to Estimate Live Birth Gestational Age in Medicaid Analytic eXtract Data.

BACKGROUND: While healthcare utilization data are useful for postmarketing surveillance of drug safety in pregnancy, the start of pregnancy and gestational age at birth are often incompletely recorded or missing. Our objective was to develop and validate a claims-based live birth gestational age algorithm. METHODS: Using the Medicaid Analytic eXtract (MAX) linked to birth certificates in three states, we developed four candidate algorithms based on: preterm codes; preterm or postterm codes; timing of prenatal care; and prediction models - using conventional regression and machine-learning approaches with a broad range of prespecified and empirically selected predictors. We assessed algorithm performance based on mean squared error (MSE) and proportion of pregnancies with estimated gestational age within 1 and 2 weeks of the gold standard, defined as the clinical or obstetric estimate of gestation on the birth certificate. We validated the best-performing algorithms against medical records in a nationwide sample. We quantified misclassification of select drug exposure scenarios due to estimated gestational age as positive predictive value (PPV), sensitivity, and specificity. RESULTS: Among 114,117 eligible pregnancies, the random forest model with all predictors emerged as the best performing algorithm: MSE 1.5; 84.8% within 1 week and 96.3% within 2 weeks, with similar performance in the nationwide validation cohort. For all exposure scenarios, PPVs were >93.8%, sensitivities >94.3%, and specificities >99.4%. CONCLUSIONS: We developed a highly accurate algorithm for estimating gestational age among live births in the nationwide MAX data, further supporting the value of these data for drug safety surveillance in pregnancy. See video abstract at, http://links.lww.com/EDE/B989 .

Synthetic Negative Controls: Using Simulation to Screen Large-scale Propensity Score Analyses.

The propensity score has become a standard tool to control for large numbers of variables in healthcare database studies. However, little has been written on the challenge of comparing large-scale propensity score analyses that use different methods for confounder selection and adjustment. In these settings, balance diagnostics are useful but do not inform researchers on which variables balance should be assessed or quantify the impact of residual covariate imbalance on bias. Here, we propose a framework to supplement balance diagnostics when comparing large-scale propensity score analyses. Instead of focusing on results from any single analysis, we suggest conducting and reporting results for many analytic choices and using both balance diagnostics and synthetically generated control studies to screen analyses that show signals of bias caused by measured confounding. To generate synthetic datasets, the framework does not require simulating the outcome-generating process. In healthcare database studies, outcome events are often rare, making it difficult to identify and model all predictors of the outcome to simulate a confounding structure closely resembling the given study. Therefore, the framework uses a model for treatment assignment to divide the comparator population into pseudo-treatment groups where covariate differences resemble those in the study cohort. The partially simulated datasets have a confounding structure approximating the study population under the null (synthetic negative control studies). The framework is used to screen analyses that likely violate partial exchangeability due to lack of control for measured confounding. We illustrate the framework using simulations and an empirical example.

Glucagon-Like Peptide 1 Receptor Agonists and Risk of Anaphylactic Reaction Among Patients With Type 2 Diabetes: A Multisite Population-Based Cohort Study.

Case reports and a pharmacovigilance analysis have linked glucagon-like peptide 1 receptor agonists (GLP-1 RAs) with anaphylactic reactions, but real-world evidence for this possible association is lacking. Using databases from the United Kingdom (Clinical Practice Research Datalink) and the United States (Medicare, Optum (Optum, Inc., Eden Prairie, Minnesota), and IBM MarketScan (IBM, Armonk, New York)), we employed a new-user, active comparator study design wherein initiators of GLP-1 RAs were compared with 2 different active comparator groups (initiators of dipeptidyl peptidase 4 (DPP-4) inhibitors and initiators of sodium-glucose cotransporter 2 (SGLT-2) inhibitors) between 2007 and 2019. Propensity score fine stratification weighted Cox proportional hazards models were fitted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for an anaphylactic reaction. Database-specific HRs were pooled using random-effects models. Compared with the use of DPP-4 inhibitors (n = 1,641,520), use of GLP-1 RAs (n = 324,098) generated a modest increase in the HR for anaphylactic reaction, with a wide 95% CI (36.9 per 100,000 person-years vs. 32.1 per 100,000 person-years, respectively; HR = 1.15, 95% CI: 0.94, 1.42). Compared with SGLT-2 inhibitors (n = 366,067), GLP-1 RAs (n = 259,929) were associated with a 38% increased risk of anaphylactic reaction (40.7 per 100,000 person-years vs. 29.4 per 100,000 person-years, respectively; HR = 1.38, 95% CI: 1.02, 1.87). In this large, multisite population-based cohort study, GLP-1 RAs were associated with a modestly increased risk of anaphylactic reaction when compared with DPP-4 inhibitors and SGLT-2 inhibitors.

Transparency of high-dimensional propensity score analyses: Guidance for diagnostics and reporting.

PURPOSE: The high-dimensional propensity score (HDPS) is a semi-automated procedure for confounder identification, prioritisation and adjustment in large healthcare databases that requires investigators to specify data dimensions, prioritisation strategy and tuning parameters. In practice, reporting of these decisions is inconsistent and this can undermine the transparency, and reproducibility of results obtained. We illustrate reporting tools, graphical displays and sensitivity analyses to increase transparency and facilitate evaluation of the robustness of analyses involving HDPS. METHODS: Using a study from the UK Clinical Practice Research Datalink that implemented HDPS we demonstrate the application of the proposed recommendations. RESULTS: We identify seven considerations surrounding the implementation of HDPS, such as the identification of data dimensions, method for code prioritisation and number of variables selected. Graphical diagnostic tools include assessing the balance of key confounders before and after adjusting for empirically selected HDPS covariates and the identification of potentially influential covariates. Sensitivity analyses include varying the number of covariates selected and assessing the impact of covariates behaving empirically as instrumental variables. In our example, results were robust to both the number of covariates selected and the inclusion of potentially influential covariates. Furthermore, our HDPS models achieved good balance in key confounders. CONCLUSIONS: The data-adaptive approach of HDPS and the resulting benefits have led to its popularity as a method for confounder adjustment in pharmacoepidemiological studies. Reporting of HDPS analyses in practice may be improved by the considerations and tools proposed here to increase the transparency and reproducibility of study results.

Development of a Medicare Claims-Based Model to Predict Persistent High-Dose Opioid Use After Total Knee Replacement.

OBJECTIVE: To develop a claims-based model to predict persistent high-dose opioid use among patients undergoing total knee replacement (TKR). METHODS: Using Medicare claims (2010-2014), we identified patients ages ≥65 years who underwent TKR with no history of high-dose opioid use (mean >25 morphine milligram equivalents [MMEs]/day) in the year prior to TKR. We used group-based trajectory modeling to identify distinct opioid use patterns. The primary outcome was persistent high-dose opioid use in the year after TKR. We split the data into training (2010-2013) and test (2014) sets and used logistic regression with least absolute shrinkage and selection operator regularization, utilizing a total of 83 preoperative patient characteristics as candidate predictors. A reduced model with 10 prespecified variables, which included demographic characteristics, opioid use, and medication history was also considered. RESULTS: The final study cohort included 142,089 patients who underwent TKR. The group-based trajectory model identified 4 distinct trajectories of opioid use (group 1: short-term, low-dose; group 2: moderate-duration, low-dose; group 3: moderate-duration, high-dose; and group 4: persistent high-dose). The model predicting persistent high-dose opioid use achieved high discrimination (receiver operating characteristic area under the curve [AUC] 0.85 [95% confidence interval (95% CI) 0.84-0.86]) in the test set. The reduced model with 10 predictors performed equally well (AUC 0.84 [95% CI 0.84-0.85]). CONCLUSION: In this cohort of older patients, 10.6% became persistent high-dose (mean 22.4 MME/day) opioid users after TKR. Our model with 10 readily available clinical factors may help identify patients at high risk of future adverse outcomes from persistent opioid use after TKR.

When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?

The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real-world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design-related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high-quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.

Linking the Paul Coverdell National Acute Stroke Program to commercial claims to establish a framework for real-world longitudinal stroke research.

BACKGROUND: Non-interventional large-scale research on real-world patients who had a stroke requires the use of multiple data sources ensuring access to longitudinal data from large populations with clinically-detailed information. We sought to establish a framework for longitudinal research on patients hospitalised with stroke by linking information-rich, deidentified inpatient data from the Paul Coverdell National Acute Stroke Program (PCNASP) to commercial and Medicare Advantage longitudinal claims data. METHODS: All stroke admissions in PCNASP between 2008 and 2015 were evaluated for linkage to longitudinal claims from a commercial insurer using an algorithm based on six available common data fields (patient age, gender, admission date, discharge date, discharge diagnosis and state) and a hospital match. We evaluated the linkage quality (via the percentage of unique records in the linked dataset) and the representativeness of the linked population. We also described medical history, stroke severity and patterns of medication use among the PCNASP-claims linked cohort. RESULTS: The linkage produced uniqueness equal to 99.1%. We identified 5644 linked and 98 896 unlinked patients who had an ischaemic stroke hospitalisation in claims data. Linked patients were younger than unlinked (69.7 vs 72.5 years), but otherwise similar by medical history, prestroke medication use or lab values. Stroke severity was mild and most patients were discharged home. Prestroke and discharge use of antihypertensive and statins in the PCNASP were greater than their use as measured by filled prescriptions in claims. CONCLUSIONS: High-quality linkage between the PCNASP and commercial claims data is feasible. This linkage identified differences between reported or recommended versus actual out-of-hospital medication utilisation, highlighting the importance of longitudinal data availability for research aimed to improve the care of patients who had a stroke.

Prevalence of Avoidable and Bias-Inflicting Methodological Pitfalls in Real-World Studies of Medication Safety and Effectiveness.

Many real-word evidence (RWE) studies that utilize existing healthcare data to evaluate treatment effects incur substantial but avoidable bias from methodologically flawed study design; however, the extent of preventable methodological pitfalls in current RWE is unknown. To characterize the prevalence of avoidable methodological pitfalls with potential for bias in published claims-based studies of medication safety or effectiveness, we conducted an English-language search of PubMed for articles published from January 1, 2010 to May 20, 2019 and randomly selected 75 studies (10 case-control and 65 cohort studies) that evaluated safety or effectiveness of cardiovascular, diabetes, or osteoporosis medications using US health insurance claims. General and methodological study characteristics were extracted independently by two reviewers, and potential for bias was assessed across nine bias domains. Nearly all studies (95%) had at least one avoidable methodological issue known to incur bias, and 81% had potentially at least one of the four issues considered major due to their potential to undermine study validity: time-related bias (57%), potential for depletion of outcome-susceptible individuals (44%), inappropriate adjustment for postbaseline variables (41%), or potential for reverse causation (39%). The median number of major issues per study was 2 (interquartile range (IQR), 1-3) and was lower in cohort studies with a new-user, active-comparator design (median 1, IQR 0-1) than in cohort studies of prevalent users with a nonuser comparator (median 3, IQR 3-4). Recognizing and avoiding known methodological study design pitfalls could substantially improve the utility of RWE and confidence in its validity.

Prevalence, predictors, and outcomes of both true- and pseudo-resistant hypertension in the action to control cardiovascular risk in diabetes trial: a cohort study.

Resistant hypertension (RH) has been poorly studied due to the difficulty in distinguishing it from nonadherence-the exclusion of which is necessary to accurately diagnose RH. Therefore, little is known about the prevalence, predictors, and outcomes of true RH. We evaluated 1838 patients from the standard blood pressure (BP) arm of the Action to Control Cardiovascular Risk in Diabetes Trial. We classified patients into three groups: "true RH", "pseudo-RH" (i.e., patients with BP levels that would classify them as RH but who were non-adherent), and "other" (i.e., those who could not be classified as having "true RH" or "pseudo-RH"). We examined predictors of true and pseudo-RH and the relationship between true RH and the composite outcome of nonfatal MI, nonfatal stroke, or cardiovascular death. Among 1838 participants with complete information, 489 (26.6%) met the definition of true RH, and 94 (16.1%) RH patients had "pseudo-RH" on ≥1 visit over the first 12 months. Predictors of RH included: baseline SBP ≥ 160 mmHg (OR = 8.79; 95% CI: 5.70-13.68) and baseline SBP between 140-159 (OR = 2.91; 95% CI: 2.13-4.00) compared to SBP < 140, additional baseline BP medication (OR = 3.40; 95% CI: 2.83-4.11), macroalbuminuria (OR = 2.35; 95% CI: 1.50-3.67), CKD (OR = 1.53; 95% CI: 0.99-2.33), history of stroke (OR = 1.73; 95% CI: 1.04-2.82), and black race (OR = 1.39; 95% CI: 1.02-1.88); the cross-validated C-statistic was 0.80. "True RH" patients had a 65% increased hazard in composite outcome (HR = 1.65; 95% CI: 1.13-2.42). In conclusion, the majority of patients classified as having RH had "true RH," which was more common among those who are black, have macroalbuminuria, CKD, stroke, higher baseline SBP, and are taking more baseline antihypertensives. These patients are at increased risk for cardiovascular and mortality events.

From Buns to Braids and Ponytails: Entering a New Era of Female Military Hair-Grooming Standards.

Each of the US Military services imposes strict hair-grooming standards to ensure professionalism and the safety of servicemembers. Although in recent years there have been some progressive changes in grooming policies, they have not adequately accounted for the diversity within the US Armed Forces or variations in hair and skin types. Review of some antiquated grooming regulations resulted in the authorized wearing of locs across all 4 services over the last 4 years. The largest catalyst for improved grooming standards occurred in 2020 when former Defense Secretary Mark Esper requested that the Department of Defense review military policies for racial bias. To embrace diversity and inclusivity in the military services while addressing grooming-related health concerns, the US Air Force and the US Army recently authorized women to wear longer braids and ponytails. The updated hair-grooming regulations are anticipated to decrease the numbers of female servicemembers impacted by scalp symptoms and hair disease. This review highlights the history of female military hair-grooming standards and the most commonly associated scalp symptoms and disorders, including trichorrhexis nodosa (TN), extracranial headaches, and traction alopecia (TA).

Real-world evidence to support regulatory decision making: New or expanded medical product indications.

There is increasing interest in utilizing real-world data (RWD) to produce real-world evidence (RWE) on the benefits and risks of medical products that could support regulatory approval decisions. The field of pharmacoepidemiology has a long history of focusing on data and evidence that would now be termed "real-world," including evidence from healthcare claims, registries, and electronic health records. However, several emerging trends over the past decade are converging to support the use of these and other RWD sources for approval decisions, and there are several recent examples and ongoing research that demonstrate how RWE may be used to support regulatory approval of new or expanded indications. The goal of this article is to review the current landscape and future directions of the use of RWE in this context. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).

How well can we assess the validity of non-randomised studies of medications? A systematic review of assessment tools.

OBJECTIVE: To determine whether assessment tools for non-randomised studies (NRS) address critical elements that influence the validity of NRS findings for comparative safety and effectiveness of medications. DESIGN: Systematic review and Delphi survey. DATA SOURCES: We searched PubMed, Embase, Google, bibliographies of reviews and websites of influential organisations from inception to November 2019. In parallel, we conducted a Delphi survey among the International Society for Pharmacoepidemiology Comparative Effectiveness Research Special Interest Group to identify key methodological challenges for NRS of medications. We created a framework consisting of the reported methodological challenges to evaluate the selected NRS tools. STUDY SELECTION: Checklists or scales assessing NRS. DATA EXTRACTION: Two reviewers extracted general information and content data related to the prespecified framework. RESULTS: Of 44 tools reviewed, 48% (n=21) assess multiple NRS designs, while other tools specifically addressed case-control (n=12, 27%) or cohort studies (n=11, 25%) only. Response rate to the Delphi survey was 73% (35 out of 48 content experts), and a consensus was reached in only two rounds. Most tools evaluated methods for selecting study participants (n=43, 98%), although only one addressed selection bias due to depletion of susceptibles (2%). Many tools addressed the measurement of exposure and outcome (n=40, 91%), and measurement and control for confounders (n=40, 91%). Most tools have at least one item/question on design-specific sources of bias (n=40, 91%), but only a few investigate reverse causation (n=8, 18%), detection bias (n=4, 9%), time-related bias (n=3, 7%), lack of new-user design (n=2, 5%) or active comparator design (n=0). Few tools address the appropriateness of statistical analyses (n=15, 34%), methods for assessing internal (n=15, 34%) or external validity (n=11, 25%) and statistical uncertainty in the findings (n=21, 48%). None of the reviewed tools investigated all the methodological domains and subdomains. CONCLUSIONS: The acknowledgement of major design-specific sources of bias (eg, lack of new-user design, lack of active comparator design, time-related bias, depletion of susceptibles, reverse causation) and statistical assessment of internal and external validity is currently not sufficiently addressed in most of the existing tools. These critical elements should be integrated to systematically investigate the validity of NRS on comparative safety and effectiveness of medications. SYSTEMATIC REVIEW PROTOCOL AND REGISTRATION: https://osf.io/es65q.

A correlation analysis to assess event-free survival as a trial-level surrogate for overall survival in early breast cancer.

BACKGROUND: Event-free survival (EFS) has been listed on the FDA Table of Surrogate Endpoints as a surrogate measure that can be considered for accelerated or traditional approval in breast cancer. However, no studies have evaluated the correlation between the treatment effects on EFS and treatment effects on overall survival (OS). METHODS: We performed a systematic search of the literature until May 2020 according to the PRISMA guideline for all published randomized controlled trials (RCTs) in early breast cancer in the neoadjuvant setting. Data on EFS and OS, including the hazard ratio (HR) and 95% confidence intervals (CI), were extracted from each study and the association between the trial-level EFS HR and the trial-level OS HR was estimated using a linear mixed-effects model on the log scale. FINDINGS: Of the 7 RCTs (N = 2211) included in the analysis, 5 included patients with HER2 positive tumor type. The estimated linear association between log HR EFS and log HR OS indicated a positive slope ( ß  = 0.58 [95% CI: -0.32-1.48]) and the coefficient of determination confirmed a moderate trial-level association between log HRs for OS and EFS (R² 0.76 [95% CI 0.34-1.00], but with wide confidence intervals. INTERPRETATION: Treatment effects in EFS are moderately correlated with treatment effects in OS in early breast cancer in the neoadjuvant setting, but the association was not significant. Thus, there is currently insufficient evidence to support EFS for use as a surrogate endpoint for traditional approval, although it may be considered for accelerated approval. FUNDING: Arnold Ventures.

Real-World Evidence for Assessing Pharmaceutical Treatments in the Context of COVID-19.

The emergence and global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an urgent need for evidence on medical interventions and outcomes of the resulting disease, coronavirus disease 2019 (COVID-19). Although many randomized controlled trials (RCTs) evaluating treatments and vaccines for COVID-19 are already in progress, the number of clinical questions of interest greatly outpaces the available resources to conduct RCTs. Therefore, there is growing interest in whether nonrandomized real-world evidence (RWE) can be used to supplement RCT evidence and aid in clinical decision making, but concerns about nonrandomized RWE have been highlighted by a proliferation of RWE studies on medications and COVID-19 outcomes with widely varying conclusions. The objective of this paper is to review some clinical questions of interest, potential data types, challenges, and merits of RWE in COVID-19, resulting in recommendations for nonrandomized RWE designs and analyses based on established RWE principles.

Identifying Risk Factors for Diabetic Ketoacidosis Associated with SGLT2 Inhibitors: a Nationwide Cohort Study in the USA.

INTRODUCTION: Sodium glucose co-transporter-2 inhibitors (SGLT2) are commonly prescribed to patients with type 2 diabetes mellitus, but can increase the risk of diabetic ketoacidosis. Identifying patients prone to diabetic ketoacidosis may help mitigate this risk. METHODS: We conducted a population-based cohort study of adults initiating SGLT2 inhibitor use from 2013 through 2017. The primary objective was to identify potential predictors of diabetic ketoacidosis. Two machine-learning methods were applied to model high-dimensional pre-exposure data: gradient boosted trees and least absolute shrinkage and selection operator (LASSO) regression. We rank ordered the variables produced from LASSO by the size of their estimated coefficient (largest to smallest). With gradient boosted trees, a relative importance measure for each variable is provided rather than a coefficient. The "top variables" were identified after reviewing the distributions of the effect estimates from LASSO and gradient boosted trees to identify where there was a substantial decrease in variable importance. The identified predictors were then assessed in a logistic regression model and reported as odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: We identified 111,442 adults who started SGLT2 inhibitor use. The mean age was 57 years, 44% were female, the mean hemoglobin A1C was 8.7%, and the mean creatinine was 0.89 mg/dL. During a mean follow-up of 180 days, 192 patients (0.2%, i.e., 2 per 1000) were diagnosed and hospitalized with diabetic ketoacidosis (DKA) and 475 (0.4%, i.e., 4 per 1000) were diagnosed in either an inpatient or outpatient setting. Using gradient boosted trees, the strongest predictors were prior DKA, baseline hemoglobin A1C level, baseline creatinine level, use of medications for dementia, and baseline bicarbonate level. Using LASSO regression not including laboratory test results due to missing data, the strongest predictors were prior DKA, digoxin use, use of medications for dementia, and recent hypoglycemia. The logistic regression model incorporating the variables identified from gradient boosted trees and LASSO regression suggested the following pre-exposure characteristics had the strongest association with a hospitalization for DKA: use of dementia medications (OR = 7.76, 95% CI 2.60, 23.1), prior intracranial hemorrhage (OR = 11.5, 95% CI 1.46, 91.1), a prior diagnosis of hypoglycemia (OR = 5.41, 95% CI 1.92,15.3), prior DKA (OR = 2.45, 95% CI 0.33, 18.0), digoxin use (OR = 4.00, 95% CI 1.21, 13.2), a baseline hemoglobin A1C above 10% (OR = 3.14, 95% CI 1.95, 5.06), and baseline bicarbonate below 18 mmol/L (OR 5.09, 95% CI 1.58, 16.4). CONCLUSION: Diabetic ketoacidosis affected approximately 2 per 1000 patients starting to use an SGLT2 inhibitor. We identified both anticipated, e.g., low baseline serum bicarbonate, and unanticipated, e.g., digoxin, dementia medications, risk factors for SGLT2 inhibitor-induced DKA.

Emulating Randomized Clinical Trials With Nonrandomized Real-World Evidence Studies: First Results From the RCT DUPLICATE Initiative.

BACKGROUND: Regulators are evaluating the use of noninterventional real-world evidence (RWE) studies to assess the effectiveness of medical products. The RCT DUPLICATE initiative (Randomized, Controlled Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology) uses a structured process to design RWE studies emulating randomized, controlled trials (RCTs) and compare results. We report findings of the first 10 trial emulations, evaluating cardiovascular outcomes of antidiabetic or antiplatelet medications. METHODS: We selected 3 active-controlled and 7 placebo-controlled RCTs for replication. Using patient-level claims data from US commercial and Medicare payers, we implemented inclusion and exclusion criteria, selected primary end points, and comparator populations to emulate those of each corresponding RCT. Within the trial-mimicking populations, we conducted propensity score matching to control for >120 preexposure confounders. All study measures were prospectively defined and protocols registered before hazard ratios and 95% CIs were computed. Success criteria for the primary analysis were prespecified for each replication. RESULTS: Despite attempts to emulate RCT design as closely as possible, differences between the RCT and corresponding RWE study populations remained. The regulatory conclusions were equivalent in 6 of 10. The RWE emulations achieved a hazard ratio estimate that was within the 95% CI from the corresponding RCT in 8 of 10 studies. In 9 of 10, either the regulatory or estimate agreement success criteria were fulfilled. The largest differences in effect estimates were found for RCTs where second-generation sulfonylureas were used as a proxy for placebo regarding cardiovascular effects. Nine of 10 replications had a standardized difference between effect estimates of <2, which suggests differences within expected random variation. CONCLUSIONS: Agreement between RCT and RWE findings varies depending on which agreement metric is used. Interim findings indicate that selection of active comparator therapies with similar indications and use patterns enhances the validity of RWE. Even in the context of active comparators, concordance between RCT and RWE findings is not guaranteed, partially because trials are not emulated exactly. More trial emulations are needed to understand how often and in what contexts RWE findings match RCTs. Registration: URL: https://www.clinicaltrials.gov; Unique identifiers: NCT03936049, NCT04215523, NCT04215536, NCT03936010, NCT03936036, NCT03936062, NCT03936023, NCT03648424, NCT04237935, NCT04237922.

Prescription opioid use after vaginal delivery and subsequent persistent opioid use and misuse.

BACKGROUND: Vaginal delivery is the most common reason for hospitalization in the United States, and approximately 30% of women fill an opioid prescription after vaginal delivery, making this a common source of opioid exposure in women of reproductive age. OBJECTIVE: This study aimed to evaluate the effect of receiving an opioid prescription after vaginal delivery on the risk of subsequent persistent opioid use, opioid use disorders, and overdose. STUDY DESIGN: We assembled a nationwide cohort of Medicaid beneficiaries in the United States using the Medicaid Analytic eXtract 2009-2014. The study population included pregnant women who delivered vaginally between 2009 and 2013 and were continuously enrolled in Medicaid from 90 days before to 365 days after delivery. We identified patients with prescription opioids dispensed within 7 days of the date of vaginal delivery. Persistent opioid use was defined as ≥10 opioid fills or >120 days' supply dispensed from 30 to 365 days after delivery. Incident diagnoses of opioid use disorder and overdose were ascertained during the same interval. Propensity score matching was used to control for potential confounding factors. RESULTS: Among 459,829 pregnancies ending in vaginal deliveries, 140,807 (30.62%) had an opioid dispensed within 7 days of delivery. Overall, 5770 of 140,807 (4.10%) women who filled an opioid prescription vs 2668 of 319,022 (0.84%) unexposed women had subsequent persistent opioid use, with an unadjusted relative risk of 4.90 (95% confidence interval, 4.68-5.13) and a risk difference of 3.26% (95% confidence interval, 3.15-3.37). After propensity score matching, the risk remained higher among pregnancies with an opioid prescription dispensed, with a relative risk of 2.57 (95% confidence interval, 2.43-2.72) and a risk difference of 2.21% (95% confidence interval, 2.08-2.33), which was confirmed by the instrumental variable analysis with a risk difference of 1.31% (95% confidence interval, 1.06-1.56) by using the rate of opioid prescribing at the delivery facility in a given geographic region as the instrument. The adjusted relative risk of newly diagnosed opioid use disorder and overdose was 1.48 (95% confidence interval, 1.40-1.57) and 1.92 (95% confidence interval, 1.20-3.09), respectively. CONCLUSION: Opioid dispensing following vaginal delivery is associated with future persistent opioid use and misuse, independent of confounding factors. Opioid prescriptions to women after vaginal delivery should be avoided, except in rare circumstances.

Postoperative inpatient utilization of opioid and opioid-sparing analgesics in the United States hospitals, 2007-2017.

PURPOSE: To evaluate recent trends in inpatient postoperative utilization of opioid and non-opioid analgesics in US hospitals. METHODS: Using Premier Research database (October 2007-September 2017), we identified adults who were hospitalized for inpatient surgical procedures (N = 6 068 133). For each month, we calculated proportion of patients admitted that month who were administered (1) opioids, (2) acetaminophen, (3) non-steroidal anti-inflammatory drugs (NSADs), and (4) gabapentinoids (gabapentin or pregabalin) during the postoperative period, defined as inpatient postoperative days 1-7, unless discharged earlier. For patients administered opioids, we estimated total and average daily postoperative opioid dose in morphine milligram equivalents (MMEs). Monthly measures were standardized to the distribution of surgeries and the length of postoperative stay within each surgery during the last year of available data. RESULTS: Overall, 90.8% of patients were administered opioids postoperatively; mean total postoperative dose was 304 MMEs (median 130). Both the frequency and the amount of opioids administered remained stable over 2007-2017. Postoperative use of acetaminophen increased from mean standardized monthly prevalence of 78% in 2007-2008 to 85% in 2017, while the use of NSAIDs remained stable at a standardized mean of 37%. The use of gabapentinoids increased from below 10% in 2007-2008 to the mean standardized monthly prevalence of 23% in 2017. CONCLUSION: Despite growing awareness of risks associated with postoperative opioid use, we observed no change in postoperative utilization of opioids in US hospitals. Increasing the use of non-opioid pain management approaches could constitute an important target in our efforts to curtail US opioid epidemic.