A population-based cohort defined risk of hyperkalemia after initiating SGLT-2 inhibitors, GLP1 receptor agonists or DPP-4 inhibitors to patients with chronic kidney disease and type 2 diabetes.

Hyperkalemia is a common adverse event in patients with chronic kidney disease (CKD) and type 2 diabetes and limits the use of guideline-recommended therapies such as renin-angiotensin system inhibitors. Here, we evaluated the comparative effects of sodium-glucose cotransporter-2 inhibitors (SGLT-2i), glucagon-like peptide-1 receptor agonists (GLP-1RA) and dipeptidyl peptidase-4 inhibitors (DPP-4i) on the risk of hyperkalemia. We conducted a population-based active-comparator, new-user cohort study using claims data from Medicare and two large United States commercial insurance databases (April 2013-April 2022). People with CKD stages 3-4 and type 2 diabetes who newly initiated SGLT-2i vs. DPP-4i (141671 patients), GLP-1RA vs. DPP-4i (159545 patients) and SGLT-2i vs. GLP-1RA (93033 patients) were included. The primary outcome was hyperkalemia diagnosed in inpatient or outpatient settings. Secondary outcomes included hyperkalemia diagnosed in inpatient or emergency department setting, and serum potassium levels of 5.5 mmol/L or more. Pooled hazard ratios and rate differences were estimated after propensity score matching to adjust for over 140 potential confounders. Initiation of SGLT-2i was associated with a lower risk of hyperkalemia compared with DPP-4i (hazard ratio 0.74; 95% confidence interval 0.68-0.80) and contrasted to GLP-1RA (0.92; 0.86-0.99). Compared with DPP-4i, GLP-1RA were also associated with a lower risk of hyperkalemia (0.80; 0.75-0.86). Corresponding absolute rate differences/1000 person-years were -24.8 (95% confidence interval -31.8 to -17.7), -5.0 (-10.9 to 0.8), and -17.7 (-23.4 to -12.1), respectively. Similar findings were observed for the secondary outcomes, among subgroups, and across single agents within the SGLT-2i and GLP-1RA classes. Thus, SGLT-2i and GLP-1RA are associated with a lower risk of hyperkalemia than DPP-4i in patients with CKD and type 2 diabetes, further supporting the use of these drugs in this population.

Risk of fall or fracture with concomitant use of prescription opioids and other medications in osteoarthritis patients.

BACKGROUND: Osteoarthritis (OA) patients taking prescription opioids for pain are at increased risk of fall or fracture, and the concomitant use of interacting drugs may further increase the risk of these events. AIMS: To identify prescription opioid-related medication combinations associated with fall or fracture. MATERIALS & METHODS: We conducted a case-crossover-based screening of two administrative claims databases spanning 2003 through 2021. OA patients were aged 40 years or older with at least 365 days of continuous enrollment and 90 days of continuous prescription opioid use before their first eligible fall or fracture event. The primary analysis quantified the odds ratio (OR) between fall and non-opioid medications dispensed in the 90 days before the fall date after adjustment for prescription opioid dosage and confounding using a case-time-control design. A secondary analogous analysis evaluated medications associated with fracture. The false discovery rate (FDR) was used to account for multiple testing. RESULTS: We identified 41 693 OA patients who experienced a fall and 24 891 OA patients who experienced a fracture after at least 90 days of continuous opioid therapy. Top non-opioid medications by ascending p-value with OR > 1 for fall were meloxicam (OR 1.22, FDR = 0.08), metoprolol (OR 1.06, FDR >0.99), and celecoxib (OR 1.13, FDR > 0.99). Top non-opioid medications for fracture were losartan (OR 1.20, FDR = 0.80), alprazolam (OR 1.14, FDR > 0.99), and duloxetine (OR 1.12, FDR = 0.97). CONCLUSION: Clinicians may seek to monitor patients who are co-prescribed drugs that act on the central nervous system, especially in individuals with OA.

Breaking Research Silos and Stimulating "Innovation at the Edges" in Epidemiology.

Epidemiological training often requires specialization in a subdiscipline (e.g., pharmacoepidemiology, genetic epidemiology, social epidemiology, or infectious disease epidemiology). While specialization is necessary and beneficial, it comes at the cost of decreased awareness of scientific developments in other subdisciplines of epidemiology. In this commentary, we argue for the importance of promoting an exchange of ideas across seemingly disparate epidemiologic subdisciplines. Such an exchange can lead to invaluable opportunities to learn from and merge knowledge across subdisciplines. It can promote "innovation at the edges," a process of borrowing and transforming methods from one subdiscipline in order to develop something new and advance another subdiscipline. Further, we outline specific actionable steps at the researcher, institution, and professional society level that can promote such innovation.

Association of Type of Oral Anticoagulant Dispensed With Adverse Clinical Outcomes in Patients Extending Anticoagulation Therapy Beyond 90 Days After Hospitalization for Venous Thromboembolism.

Importance: Guidelines for managing venous thromboembolism (VTE) recommend at least 90 days of therapy with oral anticoagulants. Limited evidence exists about the optimal drug for continuing therapy beyond 90 days. Objective: To compare having prescriptions dispensed for apixaban, rivaroxaban, or warfarin after an initial 90 days of anticoagulation therapy for the outcomes of hospitalization for recurrent VTE, major bleeding, and death. Design, Setting, and Participants: This exploratory retrospective cohort study used data from fee-for-service Medicare (2009-2017) and from 2 commercial health insurance (2004-2018) databases and included 64 642 adults who initiated oral anticoagulation following hospitalization discharge for VTE and continued treatment beyond 90 days. Exposures: Apixaban, rivaroxaban, or warfarin prescribed after an initial 90-day treatment for VTE. Main Outcomes and Measures: Primary outcomes included hospitalization for recurrent VTE and hospitalization for major bleeding. Analyses were adjusted using propensity score weighting. Patients were followed up from the end of the initial 90-day treatment episode until treatment cessation, outcome, death, disenrollment, or end of available data. Weighted Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs. Results: The study included 9167 patients prescribed apixaban (mean [SD] age, 71 [14] years; 5491 [59.9%] women), 12 468 patients prescribed rivaroxaban (mean [SD] age, 69 [14] years; 7067 [56.7%] women), and 43 007 patients prescribed warfarin (mean [SD] age, 70 [15] years; 25 404 [59.1%] women). The median (IQR) follow-up was 109 (59-228) days for recurrent VTE and 108 (58-226) days for major bleeding outcome. After propensity score weighting, the incidence rate of hospitalization for recurrent VTE was significantly lower for apixaban compared with warfarin (9.8 vs 13.5 per 1000 person-years; HR, 0.69 [95% CI, 0.49-0.99]), but the incidence rates were not significantly different between apixaban and rivaroxaban (9.8 vs 11.6 per 1000 person-years; HR, 0.80 [95% CI, 0.53-1.19]) or rivaroxaban and warfarin (HR, 0.87 [95% CI, 0.65-1.16]). Rates of hospitalization for major bleeding were 44.4 per 1000 person-years for apixaban, 50.0 per 1000 person-years for rivaroxaban, and 47.1 per 1000 person-years for warfarin, yielding HRs of 0.92 (95% CI, 0.78-1.09) for apixaban vs warfarin, 0.86 (95% CI, 0.71-1.04) for apixaban vs rivaroxaban, and 1.07 (95% CI, 0.93-1.24) for rivaroxaban vs warfarin. Conclusions and Relevance: In this exploratory analysis of patients prescribed extended-duration oral anticoagulation therapy after hospitalization for VTE, prescription dispenses for apixaban beyond 90 days, compared with warfarin beyond 90 days, were significantly associated with a modestly lower rate of hospitalization for recurrent VTE, but no significant difference in rate of hospitalization for major bleeding. There were no significant differences for comparisons of apixaban vs rivaroxaban or rivaroxaban vs warfarin.

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.

Bias in case-crossover studies of medications due to persistent use: A simulation study.

PURPOSE: The case-crossover design is increasingly used to evaluate the effects of chronic medications; however, as traditionally implemented in pharmacoepidemiology, with referent period preceding the outcome, it may lead to bias in the presence of persistent exposures. We aimed to evaluate the extent and magnitude of bias in case-crossover analyses of chronic and persistent exposures, using simulations. METHODS: We simulated cohorts with either 30-day, 180-day, or 2-year exposure duration; and with varying degrees of persistence (10%, 30%, 50%, 70%, or 90% of patients not stopping exposure). We evaluated all scenarios under the null and the scenario with 30% persistence under varying exposure effects (odds ratios of 0.25 to 4.0). Cohorts were analyzed using conditional logistic regression that compared the odds of exposure on the outcome day to the odds of exposure on a referent day 30 days prior to the outcome. We further implemented the case-time-control design to evaluate its ability to adjust for bias from persistence. RESULTS: Case-crossover analyses produced unbiased estimates across all scenarios without persistent users, regardless of exposure duration. In scenarios where some patients persisted on treatment, case-crossover analyses resulted in upward bias, which increased with increasing proportion of persistent users, but did not vary substantially in relation to the magnitude of the true effect. Case-time-control analyses removed bias in all scenarios. CONCLUSIONS: Investigators should be aware of bias due to treatment persistence in unidirectional case-crossover analyses of chronic medications, which can be remedied with a control group of similarly persistent noncases.

Comparison of Self-controlled Designs for Evaluating Outcomes of Drug-Drug Interactions: Simulation Study.

BACKGROUND: Self-controlled designs, both case-crossover and self-controlled case series, are well suited for evaluating outcomes of drug-drug interactions in electronic healthcare data. Their comparative performance in this context, however, is unknown. METHODS: We simulated cohorts of patients exposed to two drugs: a chronic drug (object) and a short-term drug (precipitant) with an associated interaction of 2.0 on the odds ratio scale. We analyzed cohorts using case-crossover and self-controlled case series designs evaluating exposure to the precipitant drug within person-time exposed to the object drug. Scenarios evaluated violations of key design assumptions: (1) time-varying, within-person confounding; (2) time trend in precipitant drug exposure prevalence; (3) nontransient precipitant exposure; and (4) event-dependent object drug discontinuation. RESULTS: Case-crossover analysis produced biased estimates when 30% of patients persisted on the precipitant drug (estimated OR 2.85) and when the use of the precipitant drug was increasing in simulated cohorts (estimated OR 2.56). Self-controlled case series produced biased estimates when patients discontinued the object drug following the occurrence of an outcome (estimated incidence ratio [IR] of 2.09 [50% of patients stopping therapy] and 2.22 [90%]). Both designs yielded similarly biased estimates in the presence of time-varying, within-person confounding. CONCLUSION: In settings with independent or rare outcomes and no substantial event-dependent censoring (<50%), self-controlled case series may be preferable to case-crossover design for evaluating outcomes of drug-drug interactions. With frequent event-dependent drug discontinuation, a case-crossover design may be preferable provided there are no time-related trends in drug exposure.

The Case-Crossover Design for Drug-Drug Interactions: Considerations for Implementation.

BACKGROUND: The case-crossover design may be useful for evaluating the clinical impact of drug-drug interactions in electronic healthcare data; however, experience with the design in this context is limited. METHODS: Using US healthcare claims data (1994-2013), we evaluated two examples of interacting drugs with prior evidence of harm: (1) cytochrome P450 (CYP)3A4-metabolized statins + clarithromycin or erythromycin and rhabdomyolysis; and (2) clopidogrel + fluoxetine or fluvoxamine and ischemic events. We conducted case-crossover analyses with (1) a three-parameter model with a product term and a six-parameter saturated model that distinguished initiation order of the two drugs; and (2) with or without active comparators. RESULTS: In the statin example, the three-parameter model produced estimates consistent with prior evidence with the active comparator (product term odds ratio [OR] = 2.05, 95% confidence interval [CI] = 1.00, 4.23) and without (OR = 1.99, 95% CI = 1.04, 3.81). In the clopidogrel example, this model produced results opposite of expectation (OR = 0.78, 95% = 0.68, 0.89), but closer to what was observed in prior studies when active comparator was used (OR = 1.03, 95% CI = 0.90, 1.19). The saturated model revealed heterogeneity of estimates across strata and considerable confounding; strata with concordant clopidogrel exposure likely produced the least biased estimates. CONCLUSION: The three-parameter model assumes a common drug-drug interaction effect, whereas the saturated model is useful for identifying potential effect heterogeneity or differential confounding across strata. Restriction to certain strata or use of an active comparator may be necessary in the presence of within-person confounding.

Glucose-lowering medications and the risk of cancer: A methodological review of studies based on real-world data.

AIM: To review the methodology of observational studies examining the association between glucose-lowering medications and cancer to identify the most common methodological challenges and sources of bias. METHODS: We searched PubMed systematically to identify observational studies on glucose-lowering medications and cancer published between January 2000 and January 2016. We assessed the design and analytical methods used in each study, with a focus on their ability to achieve study validity, and further evaluated the prevalence of major methodological choices over time. RESULTS: Of 155 studies evaluated, only 26% implemented a new-user design, 41% used an active comparator, 33% implemented a lag or latency period, and 51% adjusted for diabetes duration. Potential for immortal person-time bias was identified in 63% of the studies; 55% of the studies adjusted for variables measured during the follow-up without appropriate statistical methods. Aside from a decreasing trend in adjusting for variables measured during the follow-up, we observed no trends in methodological choices over time. CONCLUSIONS: The prevalence of well-known design and analysis flaws that may lead to biased results remains high among observational studies on glucose-lowering medications and cancer, limiting the conclusions that can be drawn from these studies. Avoiding known pitfalls could substantially improve the quality and validity of real-world evidence in this field.

Clinical Outcomes of Concomitant Use of Warfarin and Selective Serotonin Reuptake Inhibitors: A Multidatabase Observational Cohort Study.

BACKGROUND: Patients treated with warfarin are often coprescribed selective serotonin reuptake inhibitors (SSRIs) for coexisting depression. Some SSRIs are potent CYP2C9 inhibitors that may increase warfarin plasma concentrations and the risk of bleeding. We aimed to examine the effect of the putative CYP2C9-mediated warfarin-SSRI interaction on clinical outcomes. METHODS: We conducted an observational cohort study among warfarin initiators who had a subsequent SSRI prescription in 5 US claims databases. Patients were followed for up to 180 days as long as they were exposed to both warfarin and their index SSRI groups. Cox regression models were used to estimate hazard ratios and 95% confidence intervals for bleeding events, ischemic or thromboembolic events, and mortality comparing patients treated with SSRIs that are potent CYP2C9 inhibitors (fluoxetine, fluvoxamine) with those treated with other SSRIs after propensity score matching. FINDINGS: The eligible cohort comprised 52,129 patients. Hazard ratios were 1.14 (95% confidence interval [CI], 0.94-1.38) for bleeding events, 1.03 (95% CI, 0.87-1.21) for ischemic or thromboembolic events, and 0.90 (95% CI, 0.72-1.14) for mortality. Results were consistent across individual component outcomes, different warfarin stabilization periods, and subgroup analyses. CONCLUSIONS: Patients concomitantly treated with warfarin and SSRIs that are potent CYP2C9 inhibitors had comparable rates of bleeding events, ischemic or thromboembolic events, and mortality as did patients cotreated with warfarin and other SSRIs, although small but potentially meaningful effects on bleeding cannot be completely excluded. SSRI inhibition of CYP2C9 does not appear to affect major safety or effectiveness outcomes of warfarin treatment in clinical practice, where patients may be closely monitored.

Confounding of the association between statins and Parkinson disease: systematic review and meta-analysis.

PURPOSE: Both statins and higher serum cholesterol have been reported to reduce to risk of Parkinson Disease (PD). Given the importance of adjusting for cholesterol levels when evaluating the effect of statins, we assessed whether the protective effect of statins would remain when adjustment for cholesterol is performed. METHODS: We conducted a systematic review of epidemiologic studies that reported quantitative estimates of the association between statins and incident PD and were published through February 2016. Random-effects meta-analysis was used to assess the effect of statins on PD separately among the studies that adjusted for either cholesterol or hyperlipidemia and studies that did not. RESULTS: Ten eligible studies that evaluated the use of statins and incident PD were identified. Among the six studies that did not adjust for cholesterol, a protective effect of statins was observed (relative risk 0.75; 95% confidence intervals (CI) 0.60 to 0.92). Excluding studies with possible bias because of reverse causation or stratifying on study design did not affect the results. No protective effect was observed among the four studies that adjusted for either cholesterol of hyperlipidemia (relative risk 0.91; 95%CI 0.68 to 1.22). The effect estimate for studies that adjusted for cholesterol was 1.04 (95%CI 0.68 to 1.59) when a study with immortal time bias was excluded. CONCLUSIONS: The apparent protective effect of statins on risk of PD is at least partially explained by confounding by statin indication. Immortal time bias and healthy user effects also could have contributed to biased results. Copyright © 2016 John Wiley & Sons, Ltd.

Persistent opioid use following cesarean delivery: patterns and predictors among opioid-naïve women.

BACKGROUND: The incidence of opioid-related death in women has increased 5-fold over the past decade. For many women, their initial opioid exposure will occur in the setting of routine medical care. Approximately 1 in 3 deliveries in the United States is by cesarean, and opioids are commonly prescribed for postsurgical pain management. OBJECTIVE: The objective of this study was to determine the risk that opioid-naïve women prescribed opioids after cesarean delivery will subsequently become consistent prescription opioid users in the year following delivery and to identify predictors for this behavior. STUDY DESIGN: We identified women in a database of commercial insurance beneficiaries who underwent cesarean delivery and who were opioid naïve in the year prior to delivery. To identify persistent users of opioids, we used trajectory models, which group together patients with similar patterns of medication filling during follow-up, based on patterns of opioid dispensing in the year following cesarean delivery. We then constructed a multivariable logistic regression model to identify independent risk factors for membership in the persistent user group. RESULTS: A total of 285 of 80,127 (0.36%, 95% confidence interval, 0.32-0.40), opioid-naïve women became persistent opioid users (identified using trajectory models based on monthly patterns of opioid dispensing) following cesarean delivery. Demographics and baseline comorbidity predicted such use with moderate discrimination (c statistic = 0.73). Significant predictors included a history of cocaine abuse (risk, 7.41%; adjusted odds ratio, 6.11, 95% confidence interval, 1.03-36.31) and other illicit substance abuse (2.36%; adjusted odds ratio, 2.78, 95% confidence interval, 1.12-6.91), tobacco use (1.45%; adjusted odds ratio, 3.04, 95% confidence interval, 2.03-4.55), back pain (0.69%; adjusted odds ratio, 1.74, 95% confidence interval, 1.33-2.29), migraines (0.91%; adjusted odds ratio, 2.14, 95% confidence interval, 1.58-2.90), antidepressant use (1.34%; adjusted odds ratio, 3.19, 95% confidence interval, 2.41-4.23), and benzodiazepine use (1.99%; adjusted odds ratio, 3.72, 95% confidence interval, 2.64-5.26) in the year prior to the cesarean delivery. CONCLUSION: A very small proportion of opioid-naïve women (approximately 1 in 300) become persistent prescription opioid users following cesarean delivery. Preexisting psychiatric comorbidity, certain pain conditions, and substance use/abuse conditions identifiable at the time of initial opioid prescribing were predictors of persistent use.

Thiazolidinediones and Parkinson Disease: A Cohort Study.

Thiazolidinediones, a class of medications indicated for the treatment of type 2 diabetes mellitus, reduce inflammation and have been shown to provide a therapeutic benefit in animal models of Parkinson disease. We examined the association between treatment with thiazolidinediones and the onset of Parkinson disease in older individuals. We performed a cohort study of 29,397 Medicare patients enrolled in state pharmaceutical benefits programs who initiated treatment with thiazolidinediones or sulfonylureas during the years 1997 through 2005 and had no prior diagnosis of Parkinson disease. New users of thiazolidinediones were propensity score matched to new users of sulfonylureas and followed to determine whether they were diagnosed with Parkinson disease. We used Cox proportional hazards models to compare time to diagnosis of Parkinson disease in the propensity score-matched populations. To assess the association with duration of use, we performed several analyses that required longer continuous use of medications. In the primary analysis, thiazolidinedione users had a hazard ratio for a diagnosis of Parkinson disease of 1.09 (95% confidence interval: 0.71, 1.66) when compared with sulfonylurea users. Increasing the duration-of-use requirements to 10 months did not substantially change the association; the hazard ratios ranged from 1.00 (95% confidence interval: 0.49, 2.05) to 1.17 (95% confidence interval: 0.60, 2.25). Thiazolidinedione use was not associated with a longer time to diagnosis of Parkinson disease than was sulfonylurea use, regardless of duration of exposure.

Evaluating Cardiovascular Health Disparities Using Estimated Race/Ethnicity: A Validation Study.

BACKGROUND: Methods of estimating race/ethnicity using administrative data are increasingly used to examine and target disparities; however, there has been no validation of these methods using clinically relevant outcomes. OBJECTIVE: To evaluate the validity of the indirect method of race/ethnicity identification based on place of residence and surname for assessing clinically relevant outcomes. DATA SOURCES: A total of 2387 participants in the Post-MI Free Rx Event and Economic Evaluation (MI FREEE) trial who had both self-reported and Bayesian Improved Surname Geocoding method (BISG)-estimated race/ethnicity information available. STUDY DESIGN: We used tests of interaction to compare differences in the effect of providing full drug coverage for post-MI medications on adherence and rates of major vascular events or revascularization for white and nonwhite patients based upon self-reported and indirect racial/ethnic assignment. RESULTS: The impact of full coverage on clinical events differed substantially when based upon self-identified race (HR=0.97 for whites, HR=0.65 for nonwhites; interaction P-value=0.05); however, it did not differ among race/ethnicity groups classified using indirect methods (HR=0.87 for white and nonwhites; interaction P-value=0.83). The impact on adherence was the same for self-reported and BISG-estimated race/ethnicity for 2 of the 3 medication classes studied. CONCLUSIONS: Quantitatively and qualitatively different results were obtained when indirectly estimated race/ethnicity was used, suggesting that these techniques may not accurately describe aspects of race/ethnicity related to actual health behaviors.

A unified framework for classification of methods for benefit-risk assessment.

BACKGROUND: Patients, physicians, and other decision makers make implicit but inevitable trade-offs among risks and benefits of treatments. Many methods have been proposed to promote transparent and rigorous benefit-risk analysis (BRA). OBJECTIVE: To propose a framework for classifying BRA methods on the basis of key factors that matter most for patients by using a common mathematical notation and compare their results using a hypothetical example. METHODS: We classified the available BRA methods into three categories: 1) unweighted metrics, which use only probabilities of benefits and risks; 2) metrics that incorporate preference weights and that account for the impact and duration of benefits and risks; and 3) metrics that incorporate weights based on decision makers' opinions. We used two hypothetical antiplatelet drugs (a and b) to compare the BRA methods within our proposed framework. RESULTS: Unweighted metrics include the number needed to treat and the number needed to harm. Metrics that incorporate preference weights include those that use maximum acceptable risk, those that use relative-value-adjusted life-years, and those that use quality-adjusted life-years. Metrics that use decision makers' weights include the multicriteria decision analysis, the benefit-less-risk analysis, Boers' 3 by 3 table, the Gail/NCI method, and the transparent uniform risk benefit overview. Most BRA methods can be derived as a special case of a generalized formula in which some are mathematically identical. Numerical comparison of methods highlights potential differences in BRA results and their interpretation. CONCLUSIONS: The proposed framework provides a unified, patient-centered approach to BRA methods classification based on the types of weights that are used across existing methods, a key differentiating feature.

Comparison of Benefit-Risk Assessment Methods for Prospective Monitoring of Newly Marketed Drugs: A Simulation Study.

OBJECTIVES: To compare benefit-risk assessment (BRA) methods for determining whether and when sufficient evidence exists to indicate that one drug is favorable over another in prospective monitoring. METHODS: We simulated prospective monitoring of a new drug (A) versus an alternative drug (B) with respect to two beneficial and three harmful outcomes. We generated data for 1000 iterations of six scenarios and applied four BRA metrics: number needed to treat and number needed to harm (NNT|NNH), incremental net benefit (INB) with maximum acceptable risk, INB with relative-value-adjusted life-years, and INB with quality-adjusted life-years. We determined the proportion of iterations in which the 99% confidence interval for each metric included and excluded the null and we calculated mean time to alerting. RESULTS: With no true difference in any outcome between drugs A and B, the proportion of iterations including the null was lowest for INB with relative-value-adjusted life-years (64%) and highest for INB with quality-adjusted life-years (76%). When drug A was more effective and the drugs were equally safe, all metrics indicated net favorability of A in more than 70% of the iterations. When drug A was safer than drug B, NNT|NNH had the highest proportion of iterations indicating net favorability of drug A (65%). Mean time to alerting was similar among methods across the six scenarios. CONCLUSIONS: BRA metrics can be useful for identifying net favorability when applied to prospective monitoring of a new drug versus an alternative drug. INB-based approaches similarly outperform unweighted NNT|NNH approaches. Time to alerting was similar across approaches.

Prospective Benefit-Risk Monitoring of New Drugs for Rapid Assessment of Net Favorability in Electronic Health Care Data.

BACKGROUND: Benefit-risk assessment (BRA) methods can combine measures of benefits and risks into a single value. OBJECTIVES: To examine BRA metrics for prospective monitoring of new drugs in electronic health care data. METHODS: Using two electronic health care databases, we emulated prospective monitoring of three drugs (rofecoxib vs. nonselective nonsteroidal anti-inflammatory drugs, prasugrel vs. clopidogrel, and denosumab vs. bisphosphonates) using a sequential propensity score-matched cohort design. We applied four BRA metrics: number needed to treat and number needed to harm; incremental net benefit (INB) with maximum acceptable risk; INB with relative-value-adjusted life-years; and INB with quality-adjusted life-years (QALYs). We determined whether and when the bootstrapped 99% confidence interval (CI) for each metric excluded zero, indicating net favorability of one drug over the other. RESULTS: For rofecoxib, all four metrics yielded a negative value, suggesting net favorability of nonselective nonsteroidal anti-inflammatory drugs over rofecoxib, and the 99% CI for all but the number needed to treat and number needed to harm excluded the null during follow-up. For prasugrel, only the 99% CI for INB-QALY excluded the null, but trends in values over time were similar across the four metrics, suggesting overall net favorability of prasugrel versus clopidogrel. The 99% CI for INB-relative-value-adjusted life-years and INB-QALY excluded the null in the denosumab example, suggesting net favorability of denosumab over bisphosphonates. CONCLUSIONS: Prospective benefit-risk monitoring can be used to determine net favorability of a new drug in electronic health care data. In three examples, existing BRA metrics produced qualitatively similar results but differed with respect to alert generation.

Non-steroidal anti-inflammatory drug administration after coronary artery bypass surgery: utilization persists despite the boxed warning.

PURPOSE: In 2005, the US Food and Drug Administration (FDA) issued a boxed warning against the administration of non-steroidal anti-inflammatory drugs (NSAIDs) after coronary artery bypass graft (CABG) surgery because of cardiovascular safety concerns. We assessed utilization rates before and after the advisory and evaluated predictors of NSAID administration following CABG. METHODS: We assembled a cohort of 277,576 patients who underwent CABG from 2004 to 2010. Temporal trends in NSAID exposure were evaluated, and predictors of postoperative NSAID use were identified using generalized estimating equations. RESULTS: Over the study period, 92,938 CABG patients (33.5%) received NSAIDs following surgery. The frequency of NSAID administration declined steadily over time, from a peak of 38.9% in 2004 to a low of 29.0% in 2010 (p < 0.0007). Ketorolac was the most frequent NSAID prescribed, commonly on the first postoperative day. Surgery performed after the boxed warning was independently associated with a 20% lower odds of NSAID administration [odds ratio (OR): 0.80; p = 0.0003]. Other factors that predicted a lower odds of NSAID use following surgery included a history of renal disease (OR: 0.33; p < 0.0001) and liver disease (OR: 0.66; p < 0.0001), and the need for concurrent valve surgery (OR: 0.78; p < 0.0001). A mammary graft at the time of surgery increased the odds of NSAID administration (OR: 1.23; p < 0.0001). CONCLUSIONS: The frequency of NSAID administration after CABG has declined since the FDA advisory, yet many patients continue to receive them in recent years. Our data highlight the need for future research initiatives to further define the risks associated with NSAID use in this population.

Burden of changes in pill appearance for patients receiving generic cardiovascular medications after myocardial infarction: cohort and nested case-control studies.

BACKGROUND: Generic prescription drugs made by different manufacturers may vary in color or shape, and switching among these drug products may interrupt medication use. OBJECTIVE: To determine whether nonpersistent use of generic drugs among patients with cardiovascular disease after myocardial infarction (MI) is associated with inconsistent appearance of their medications. DESIGN: Cohort and nested case-control studies. SETTING: Claims from a commercial health insurance database in the United States. PATIENTS: Patients discharged after hospitalization for MI between 2006 and 2011 who initiated treatment with a generic ß-blocker, angiotensin-converting enzyme inhibitor, angiotensin II-receptor blocker, or statin. Case patients discontinued their index medication for at least 1 month; control patients continued treatment. Control patients were matched to case patients on therapeutic class, number of dispensings before nonpersistence, sex, and age. MEASUREMENTS: Rates of changes in pill color and shape during the year after MI were calculated. Next, 2 refills preceding nonpersistence were evaluated to determine whether pill color or shape had changed. Odds of discordance among case and control patients were compared using conditional logistic regression. RESULTS: A total of 29% of patients (3286 of 11,513) had a change in pill shape or color during the study. Statins had the most changes in appearance, whereas ß-blockers had the fewest. A total of 4573 episodes of nonpersistence was matched to 19,881 control episodes. The odds of nonpersistence in case patients increased by 34% after a change in pill color (adjusted odds ratio, 1.34 [95% CI, 1.12 to 1.59]) and 66% after a change in pill shape (adjusted odds ratio, 1.66 [CI, 1.43 to 1.94]). LIMITATION: Only 3 categories of drugs indicated after MI were evaluated, and clinical outcomes were not addressed. CONCLUSION: Variation in the appearance of generic pills is associated with nonpersistent use of these essential drugs after MI among patients with cardiovascular disease. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality and the Harvard Program in Therapeutic Science.