Base editing of key residues in the BCL11A-XL-specific zinc finger domains derepresses fetal globin expression.

BCL11A-XL directly binds and represses the fetal globin (HBG1/2) gene promoters, using 3 zinc-finger domains (ZnF4, ZnF5, and ZnF6), and is a potential target for ß-hemoglobinopathy treatments. Disrupting BCL11A-XL results in derepression of fetal globin and high HbF, but also affects hematopoietic stem and progenitor cell (HSPC) engraftment and erythroid maturation. Intriguingly, neurodevelopmental patients with ZnF domain mutations have elevated HbF with normal hematological parameters. Inspired by this natural phenomenon, we used both CRISPR-Cas9 and base editing at specific ZnF domains and assessed the impacts on HbF production and hematopoietic differentiation. Generating indels in the various ZnF domains by CRISPR-Cas9 prevented the binding of BCL11A-XL to its site in the HBG1/2 promoters and elevated the HbF levels but affected normal hematopoiesis. Far fewer side effects were observed with base editing- for instance, erythroid maturation in vitro was near normal. However, we observed a modest reduction in HSPC engraftment and a complete loss of B cell development in vivo, presumably because current base editing is not capable of precisely recapitulating the mutations found in patients with BCL11A-XL-associated neurodevelopment disorders. Overall, our results reveal that disrupting different ZnF domains has different effects. Disrupting ZnF4 elevated HbF levels significantly while leaving many other erythroid target genes unaffected, and interestingly, disrupting ZnF6 also elevated HbF levels, which was unexpected because this region does not directly interact with the HBG1/2 promoters. This first structure/function analysis of ZnF4-6 provides important insights into the domains of BCL11A-XL that are required to repress fetal globin expression and provide framework for exploring the introduction of natural mutations that may enable the derepression of single gene while leaving other functions unaffected.

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.

Comparison of measures of medication adherence from pharmacy dispensing and insurer claims data.

OBJECTIVE: Medication nonadherence is linked to worsened clinical outcomes and increased costs. Existing system-level adherence interventions rely on insurer claims for patient identification and outcome measurement, yet suffer from incomplete capture and lags in data acquisition. Data from pharmacies regarding prescription filling, captured in retail dispensing, may be more efficient. DATA SOURCES: Pharmacy fill and insurer claims data. STUDY DESIGN: We compared adherence measured using pharmacy fill data to adherence using insurer claims data, expressed as proportion of days covered (PDC) over 12 months. Agreement was evaluated using correlation/validation metrics. We also explored the relationship between adherence in both sources and disease control using prediction modeling. DATA EXTRACTION METHODS: Large pragmatic trial of cardiometabolic disease in an integrated delivery network. PRINCIPAL FINDINGS: Among 1113 patients, adherence was higher in pharmacy fill (mean = 50.0%) versus claims data (mean = 47.4%), although they had moderately high correlation (R = 0.57, 95% CI: 0.53-0.61) with most patients (86.9%) being similarly classified as adherent or nonadherent. Sensitivity and specificity of pharmacy fill versus claims data were high (0.89, 95% CI: 0.86-0.91 and 0.80, 95% CI: 0.75-0.85). Pharmacy fill-based PDC predicted better disease control slightly more than claims-based PDC, although the difference was nonsignificant. CONCLUSIONS: Pharmacy fill data may be an alternative to insurer claims for adherence measurement.

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.

Association of Fluoroquinolones With the Risk of Aortic Aneurysm or Aortic Dissection.

Importance: Previous observational studies have suggested that fluoroquinolones are associated with aortic aneurysm or dissection, but these studies may be subject to confounding by indication or surveillance bias. Objective: To assess the association of fluoroquinolones with risk of aortic aneurysm or aortic dissection (AA/AD) while accounting for potential confounding by fluoroquinolone indication and bias owing to differential surveillance. Design, Setting, and Participants: In an observational cohort study using a US commercial claims database, 2 pairwise 1:1 propensity score-matched cohorts were identified: patients aged 50 years or older with a diagnosis of pneumonia 3 days or less before initiating treatment with a fluoroquinolone or azithromycin and patients aged 50 years or older with a urinary tract infection (UTI) diagnosis 3 days or less before initiating a fluoroquinolone or combined trimethoprim and sulfamethoxazole. Hazard ratios (HRs) and 95% CIs were estimated controlling for 85 baseline confounders. In a secondary analysis, patients receiving fluoroquinolones were compared with those receiving amoxicillin, both with and without considering baseline aortic imaging, to address differences in detection of AA/AD before antibiotic use. Data on patients within the database from January 1, 2003, through September 30, 2015, were analyzed. Data analysis was conducted from July 23, 2019, to July 6, 2020. Main Outcomes and Measures: Hospitalization for AA/AD occurring within 60 days following treatment initiation. Results: After propensity score matching, patient characteristics were well balanced, with 279 554 patients (mean [SD] age, 63.66 [10.93] years; 149 976 women [53.6%]) in the pneumonia cohort and 948 364 patients (mean [SD] age, 62.06 [10.33] years; 823 667 women [86.9%]) in the UTI cohort. Initiators of fluoroquinolones (n = 139 772 pairs in the pneumonia cohort and n = 474 182 pairs in the UTI cohort) had an increased rate of AA/AD compared with initiators of azithromycin (HR, 2.57; 95% CI, 1.36-4.86; incidence, 0.03% for fluoroquinolones vs 0.01% for azithromycin) but no increased rate compared with initiators of combined trimethoprim and sulfamethoxazole (HR, 0.99; 95% CI, 0.62-1.57; incidence, <0.01% in both UTI groups). Secondary analysis using amoxicillin as a comparator (n = 3 976 162 pairs) produced results consistent with those from earlier studies (HR, 1.54; 95% CI, 1.33-1.79; incidence, <0.01% in both groups). Requiring baseline imaging in this cohort (n = 542 649 pairs) to address surveillance bias attenuated the increased rate (HR, 1.13; 95% CI, 0.96-1.33; incidence, 0.06% for fluoroquinolones vs 0.05% for amoxicillin). Conclusions and Relevance: The findings of this nationwide cohort study of adults with pneumonia or UTI suggest an increased relative rate of AA/AD associated with fluoroquinolones within the pneumonia cohort but not within the UTI cohort. In both cohorts, the absolute rate of AA/AD appeared to be low (<0.1%). The increased relative rate observed in the pneumonia cohort may be due to residual confounding or surveillance bias.

Comparison of a new 3-item self-reported measure of adherence to medication with pharmacy claims data in patients with cardiometabolic disease.

BACKGROUND: Less than half of patients with cardiometabolic disease consistently take prescribed medications. While health insurers and some delivery organizations use claims to measure adherence, most clinicians do not have access during routine interactions. Self-reported scales exist, but their practical utility is often limited by length or cost. By contrast, the accuracy of a new 3-item self-reported measure has been demonstrated in individuals with HIV. We evaluated its concordance with claims-based adherence measures in cardiometabolic disease. METHODS: We used data from a recently-completed pragmatic trial of patients with cardiometabolic conditions. After 12 months of follow-up, intervention subjects were mailed a survey with the 3-item measure that queries about medication use in the prior 30 days. Responses were linearly transformed and averaged. Adherence was also measured in claims in month 12 and months 1-12 of the trial using proportion of days covered (PDC) metrics. We compared validation metrics for non-adherence for self-report (average <0.80) compared with claims (PDC <0.80). RESULTS: Of 459 patients returning the survey (response rate: 43.5%), 50.1% were non-adherent in claims in month 12 while 20.9% were non-adherent based on the survey. Specificity of the 3-item metric for non-adherence was high (month 12: 0.83). Sensitivity was relatively poor (month 12: 0.25). Month 12 positive and negative predictive values were 0.59 and 0.52, respectively. CONCLUSIONS: A 3-item self-reported measure has high specificity but poor sensitivity for non-adherence versus claims in cardiometabolic disease. Despite this, the tool could help target those needing adherence support, particularly in the absence of claims data.

Prevalence and Impact of Having Multiple Barriers to Medication Adherence in Nonadherent Patients With Poorly Controlled Cardiometabolic Disease.

Adherence to medications remains poor despite numerous efforts to identify and intervene upon nonadherence. One potential explanation is the limited focus of many interventions on one barrier. Little is known about the prevalence and impact of having multiple barriers in contemporary practice. Our objective was to quantify adherence barriers for patients with poorly controlled cardiometabolic condition, identify patient characteristics associated with having multiple barriers, and determine its impact on adherence. We used a linked electronic health records and insurer claims dataset from a large health system from a recent pragmatic trial. Barriers to medication taking before the start of the intervention were elicited by clinical pharmacists using structured interviews. We used multivariable modified Poisson regression models to examine the association between patient factors and multiple barriers and multivariable linear regression to evaluate the relation between multiple barriers and claims-based adherence. Of the 1,069 patients (mean: 61 years of age) in this study, 25.1% had multiple barriers to adherence; the most common co-occurring barriers were forgetfulness and health beliefs (31%, n = 268). Patients with multiple barriers were more likely to be non-white (relative risk [RR] 1.57, 95% confidence interval [CI] 1.21 to 1.74), be single/unpartnered (RR 1.36, 95% CI 1.06 to 1.74), use tobacco (RR 1.54, 95% CI 1.13 to 2.11), and have poor glycemic control (RR 1.77, 95% CI 1.31 to 2.39) versus those with 0 or 1 barrier. Each additional barrier worsened average adherence by 3.1% (95% CI -4.6%, -1.5%). In conclusion, >25% of nonadherent patients present with multiple barriers to optimal use, leading to meaningful differences in adherence. These findings should inform quality improvement interventions aimed at nonadherence.

Comparative Outcomes of Treatment Initiation With Brand vs. Generic Warfarin in Older Patients.

The anticoagulant response to warfarin, a narrow therapeutic index drug, increases with age, which may make older patients susceptible to adverse outcomes resulting from small differences in bioavailability between generic and brand products. Using US Medicare claims linked to electronic medical records from two large hospitals in Boston, we designed a cohort study of ≥ 65-year-old patients. Patients were followed for a composite effectiveness outcome of ischemic stroke or venous thromboembolism, a composite safety outcome, including major hemorrhage, and a 1-year all-cause mortality outcome. After propensity score fine-stratification and weighting to account for > 90 confounders, hazard ratios comparing brand vs. generic warfarin initiators (95% confidence intervals) for the effectiveness, safety, and all-cause mortality outcomes, were 0.97 (0.65-1.46), 0.94 (0.65-1.35), and 0.84 (0.62-1.13), respectively. Results from subgroup analyses of patients with atrial fibrillation, CHADS-VASc score ≥ 3, and HAS-BLED score ≥ 3 were consistent with the primary analysis.

Safety and Effectiveness of Dabigatran and Other Direct Oral Anticoagulants Compared With Warfarin in Patients With Atrial Fibrillation.

The study objective was to evaluate the safety and effectiveness of dabigatran and other direct oral anticoagulants (DOACs) compared with warfarin among patients with nonvalvular atrial fibrillation using a prospective monitoring program. We implemented a cohort design with propensity score matching to compare initiators of DOACs and warfarin between 2010 and 2015 in two US healthcare databases. Proportional hazards regression was used to estimate hazard ratios (HRs) for stroke and major bleeding. The final analyses included 29,448 dabigatran, 35,520 rivaroxaban, and 19,588 apixaban initiators, matched to warfarin initiators. The pooled HR for stroke was 0.75 (95% confidence interval (CI) 0.58-0.98) for dabigatran, 0.77 (95% CI 0.61-0.98) for rivaroxaban, and 0.69 (95% CI 0.50-0.96) for apixaban, consistent with findings from randomized trials. For major hemorrhage, the HRs were 0.72 (95% CI 0.65-0.80), 1.02 (95% CI 0.94-1.12), and 0.56 (95% CI 0.49-0.64), respectively, showing a decreased risk of major bleeding for both dabigatran and apixaban, as compared with trial evidence.

Using Real-World Data to Predict Findings of an Ongoing Phase IV Cardiovascular Outcome Trial: Cardiovascular Safety of Linagliptin Versus Glimepiride.

OBJECTIVE: Using real-world data (RWD) from three U.S. claims data sets, we aim to predict the findings of the CARdiovascular Outcome Trial of LINAgliptin Versus Glimepiride in Type 2 Diabetes (CAROLINA) comparing linagliptin versus glimepiride in patients with type 2 diabetes (T2D) at increased cardiovascular risk by using a novel framework that requires passing prespecified validity checks before analyzing the primary outcome. RESEARCH DESIGN AND METHODS: Within Medicare and two commercial claims data sets (May 2011-September 2015), we identified a 1:1 propensity score-matched (PSM) cohort of T2D patients 40-85 years old at increased cardiovascular risk who initiated linagliptin or glimepiride by adapting eligibility criteria from CAROLINA. PSM was used to balance >120 confounders. Validity checks included the evaluation of expected power, covariate balance, and two control outcomes for which we expected a positive association and a null finding. We registered the protocol (NCT03648424, ClinicalTrials.gov) before evaluating the composite cardiovascular outcome based on CAROLINA's primary end point. Hazard ratios (HR) and 95% CIs were estimated in each data source and pooled with a fixed-effects meta-analysis. RESULTS: We identified 24,131 PSM pairs of linagliptin and glimepiride initiators with sufficient power for noninferiority (>98%). Exposure groups achieved excellent covariate balance, including key laboratory results, and expected associations between glimepiride and hypoglycemia (HR 2.38 [95% CI 1.79-3.13]) and between linagliptin and end-stage renal disease (HR 1.08 [0.66-1.79]) were replicated. Linagliptin was associated with a 9% decreased risk in the composite cardiovascular outcome with a CI including the null (HR 0.91 [0.79-1.05]), in line with noninferiority. CONCLUSIONS: In a nonrandomized RWD study, we found that linagliptin has noninferior risk of a composite cardiovascular outcome compared with glimepiride.

Evaluating utilization patterns of oral anticoagulants in routine care.

Essentials Unlike warfarin, treatment with DOACs do not require regular plasma level monitoring. We compared persistence of patients treated with DOACs compared to warfarin. Persistence at 12 months was higher for all DOACs compared to warfarin. Persistence to anticoagulant therapy was generally poor in commercially insured patients. BACKGROUND: Direct oral anticoagulants (DOACs) were developed as an alternative to vitamin K antagonists for a variety of indications. Unlike warfarin, DOACs do not require regular plasma level monitoring. OBJECTIVE: We investigated whether this simplification in management affects persistence on DOACs vs warfarin. METHODS: Within two US commercial health insurance databases (MarketScan and Clinformatics™ DataMart), we compared baseline characteristics and evaluated rates of nonpersistence (≥30-day treatment gap or switching) among patients with nonvalvular atrial fibrillation who initiated an oral anticoagulant between October 2010 and September 2015. RESULTS: In the larger of the two data sources (MarketScan), we identified 166 690 anticoagulant initiators during the study period. After propensity score (PS) matching, 24 141 dabigatran initiators, 26 066 rivaroxaban, and 12 578 apixaban initiators were included along with the 1:1 matched warfarin initiators. The proportion of patients who were nonpersistent after 12 months was lower for DOAC users (dabigatran 66%, rivaroxaban 60%, apixaban 53%) compared with warfarin users (72%). The same relative ranking was observed in direct comparisons among the DOACs after PS-matching. Findings in Clinformatics DataMart were similar. CONCLUSION: Results from this long-term surveillance program showed that patients who initiated DOACs were more likely to be persistent to therapy compared with those who initiated warfarin. Persistence to anticoagulant therapy was generally poor in commercially insured patients.

Cardiovascular safety of linagliptin compared with other oral glucose-lowering agents in patients with type 2 diabetes: A sequential monitoring programme in routine care.

AIM: To evaluate the safety of linagliptin versus other glucose-lowering medications in a multi-year monitoring programme using insurance claims data. METHODS: In two commercial US claims databases, we identified three pairwise 1:1 propensity-score (PS)-matched cohorts of patients with type 2 diabetes (T2D) aged ≥18 years initiating linagliptin or a comparator (other dipeptidyl peptidase-4 [DPP-4] inhibitors [n = 31 492 pairs], pioglitazone [n = 23 316 pairs], or second-generation sulphonylureas [n = 19 731 pairs]) between May 2011 and December 2015. The primary endpoint was the risk of a composite cardiovascular (CV) outcome (hospitalization for myocardial infarction, stroke, unstable angina, or coronary revascularization). We estimated pooled hazard ratios (HRs) and 95% confidence intervals (CIs), controlling for >100 baseline characteristics. RESULTS: Patient characteristics were well balanced after PS-matching. The mean age was 55 years and mean follow-up was 0.8 years. Linagliptin conferred a similar risk of the composite CV outcome compared to other DPP-4 inhibitors (HR 0.91, 95% CI 0.79-1.05) and pioglitazone (HR 0.98, 95% CI 0.84-1.15), and showed a reduced risk of CV outcomes compared to second-generation sulphonylureas (HR 0.76, 95% CI 0.64--0.92). Key findings were signalled at the first interim analysis in June 2013 and solidified during ongoing monitoring until 2015. CONCLUSION: Analyses from a large monitoring programme in routine care of patients with T2D, showed that linagliptin had similar CV safety compared to other DPP-4 inhibitors and pioglitazone, and a reduced CV risk compared to sulphonylureas.

Sequential Monitoring of the Comparative Effectiveness and Safety of Dabigatran in Routine Care.

Background The increasing availability of electronic healthcare data enables ongoing monitoring of the effectiveness and safety of newly marketed medications. We sought to demonstrate a 5-year prospective monitoring system of dabigatran for stroke prevention that may expedite discovery and allow ongoing evidence development. Methods and Results Between 2011 and 2015, we conducted 9 sequential analyses of dabigatran versus warfarin users in a sequential cohort design in 2 US claims databases. Analyses 4 through 9 were prespecified, and analyses 1 through 3 were added subsequently using the same methodology. New users of anticoagulants with nonvalvular atrial fibrillation were followed until a study outcome of hospitalization for stroke (hemorrhagic and ischemic) or hospitalization for major hemorrhage (intracranial and extracranial). Hazard ratios and 95% CIs were estimated after 1:1 propensity score matching. Sequential analyses 1 through 3 on stroke prevention using data through June 2012 were limited by few events leading to wide CIs. As data accumulated the effect estimate in analysis 4 visually stabilized at a 25% risk reduction with increasingly narrower CIs (-46% to +9% in December 2012 and -42% to -2% in September 2015). Improved data-adaptive confounding adjustment with high-dimensional propensity score reached a stable state already at analysis 3 and was slightly closer to the randomized clinical trial finding (-39%). The risk of major hemorrhage was 28% lower in dabigatran initiators (-35% to -20%) a finding that was stable throughout analyses 2 to 9. Conclusions Prospectively monitoring the effectiveness and safety of dabigatran for stroke prevention allowed for early insights with increasing precision over time.

Evaluation of Socioeconomic Status Indicators for Confounding Adjustment in Observational Studies of Medication Use.

Methodologic research evaluating confounding due to socioeconomic status (SES) in observational studies of medications is limited. We identified 7,109 patients who initiated brand or generic atorvastatin from Medicare claims (2011-2013) linked to electronic medical records and census data. We created a propensity score (PS) containing only claims-based covariates and augmented it with additional claims-based proxies for SES, ZIP code, and block group level SES. Cox models with PS fine-stratification and weighting were used to compare rates of a cardiovascular end point and emergency department visits. Adjustment with only claims-based variables substantially improved balance on all SES variables compared with the unadjusted. Although inclusion of SES in PS models further improved balance on SES variables compared with models with claims-based covariates only, it did not materially change point estimates for either outcome. Inclusion of claims-based proxies may mitigate confounding by SES when aggregate-level SES information is unavailable.

Claims Data Studies of Direct Oral Anticoagulants Can Achieve Balance in Important Clinical Parameters Only Observable in Electronic Health Records.

Claims databases provide information on the effects of direct oral anticoagulants (DOACs) as used in routine care but may not contain important data on clinical characteristics, which may be captured in electronic health records (EHRs). Within a US claims database, we identified patients initiating a DOAC or warfarin between October 2010 and December 2014. Propensity score (PS) matching, 1:1, was used to balance 78 claims-defined baseline characteristics. We evaluated whether balance was achieved in patient characteristics immeasurable in the claims data study by evaluating the balance in clinical information (using absolute standardized differences (aSDs)) from linked EHR data. From a claims data cohort study of 140,187 patients, 5,935 (4.2%) were linked to EHR data. After PS matching, almost all EHR-defined patient characteristics were well balanced (aSD < 0.1). A new user active comparator design with 1:1 PS matching on many patient characteristics improved balance on clinical risk factors observed in EHRs but not in claims data.

Effect of a Remotely Delivered Tailored Multicomponent Approach to Enhance Medication Taking for Patients With Hyperlipidemia, Hypertension, and Diabetes: The STIC2IT Cluster Randomized Clinical Trial.

Importance: Approximately half of patients with chronic conditions are nonadherent to prescribed medications, and interventions have been only modestly effective. Objective: To evaluate the effect of a remotely delivered multicomponent behaviorally tailored intervention on adherence to medications for hyperlipidemia, hypertension, and diabetes. Design, Setting, and Participants: Two-arm pragmatic cluster randomized controlled trial at a multispecialty group practice including participants 18 to 85 years old with suboptimal hyperlipidemia, hypertension, or diabetes disease control, and who were nonadherent to prescribed medications for these conditions. Interventions: Usual care or a multicomponent intervention using telephone-delivered behavioral interviewing by trained clinical pharmacists, text messaging, pillboxes, and mailed progress reports. The intervention was tailored to individual barriers and level of activation. Main Outcomes and Measures: The primary outcome was medication adherence from pharmacy claims data. Secondary outcomes were disease control based on achieved levels of low-density lipoprotein cholesterol, systolic blood pressure, and hemoglobin A1c from electronic health records, and health care resource use from claims data. Outcomes were evaluated using intention-to-treat principles and multiple imputation for missing values. Results: Fourteen practice sites with 4078 participants had a mean (SD) age of 59.8 (11.6) years; 45.1% were female. Seven sites were each randomized to intervention or usual care. The intervention resulted in a 4.7% (95% CI, 3.0%-6.4%) improvement in adherence vs usual care but no difference in the odds of achieving good disease control for at least 1 (odds ratio [OR], 1.10; 95% CI, 0.94-1.28) or all eligible conditions (OR, 1.05; 95% CI, 0.91-1.22), hospitalization (OR, 1.02; 95% CI, 0.78-1.34), or having a physician office visit (OR, 1.11; 95% CI, 0.91-1.36). However, intervention participants were significantly less likely to have an emergency department visit (OR, 0.62; 95% CI, 0.45-0.85). In as-treated analyses, the intervention was associated with a 10.4% (95% CI, 8.2%-12.5%) increase in adherence, a significant increase in patients achieving disease control for at least 1 eligible condition (OR, 1.24; 95% CI, 1.03-1.50), and nonsignificantly improved disease control for all eligible conditions (OR, 1.18; 95% CI, 0.99-1.41). Conclusions and Relevance: A remotely delivered multicomponent behaviorally tailored intervention resulted in a statistically significant increase in medication adherence but did not change clinical outcomes. Future work should focus on identifying which groups derive the most clinical benefit from adherence improvement efforts. Trial Registration: ClinicalTrials.gov identifier: NCT02512276.

The relative benefits of claims and electronic health record data for predicting medication adherence trajectory.

BACKGROUND: Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims. METHODS: In a linked database of complete Medicare Advantage claims and comprehensive EHR from a multi-specialty outpatient practice, we identified patients who filled a prescription for a statin, antihypertensive, or oral antidiabetic during 2011 to 2012. We followed patients to identify subsequent medication filling patterns and used group-based trajectory models to assign patients to adherence trajectories. We then identified potential predictors from both claims and EHR data and fit a series of models to evaluate the accuracy of each data source in predicting medication adherence. RESULTS: Claims were highly predictive of patients in the worst adherence trajectory (C=0.78), but EHR data also provided good predictions (C=0.72). Among claims predictors, presence of a prior gap in filling of at least 6 days was by far the most influential predictor. In contrast, good predictions from EHR data required complex models with many variables. CONCLUSION: EHR data can provide good predictions of adherence trajectory and therefore may be useful for providers seeking to deploy resource-intensive interventions. However, prior adherence information derived from claims is most predictive, and can supplement EHR data when it is available.

Preferential prescribing and utilization trends of diabetes medications among patients with renal impairment: Emerging role of linagliptin and other dipeptidyl peptidase 4 inhibitors.

OBJECTIVES: Although many newer diabetes medications have become available in the last decade, most have not been widely studied in populations with chronic kidney disease under routine care. Linagliptin, a recently marketed dipeptidyl peptidase 4 (DPP-4) inhibitor, is the only agent in the U.S. that does not require dose adjustment in patients with diabetes mellitus type 2 (T2DM) and renal impairment. We sought to describe baseline kidney function and other key characteristics among patients with diabetes mellitus type 2 (T2DM) initiating linagliptin and other diabetes medications, and to explore prescribing patterns among T2DM patients with moderate to severe renal impairment before and after the launch of linagliptin. DESIGN: Using a population-based cohort study design nested in a large U.S. commercial healthcare dataset linked to laboratory values, we described characteristics of T2DM patients initiating linagliptin and other diabetes medications between May 2011 (launch of linagliptin) and September 2015. We also explored prescribing trends among T2DM patients with moderate to severe renal impairment (ICD-9 diagnosis code 585.3x-6x) who initiated linagliptin and other diabetes medications between January 2006 to September 2015 (before and after the launch of linagliptin). PATIENTS: We identified 1,174,476 T2DM patients initiating a diabetes medication (28,900 linagliptin initiators) between 05/2011-09/2015. We also identified 100,847 T2DM patients with moderate to severe renal impairment initiating a diabetes agent between 01/2006-09/2015. RESULTS AND CONCLUSION: Among patients initiating newer diabetes medications between 05/2011-09/2015, those initiating linagliptin had the highest prevalence of moderate to severe renal impairment, suggesting preferential prescribing in routine care. DPP-4 inhibitors overall were among the most frequently chosen agents among T2DM patients with moderate to severe renal impairment between 01/2006-09/2015. Further investigation of the safety and effectiveness of DPP-4 inhibitors in routine care of T2DM patients with renal impairment is needed to either corroborate or discourage current prescribing patterns.

Claims-based studies of oral glucose-lowering medications can achieve balance in critical clinical variables only observed in electronic health records.

AIM: To evaluate the extent to which balance in unmeasured characteristics of patients with type 2 diabetes (T2DM) was achieved in claims data, by comparing against more detailed information from linked electronic health records (EHR) data. METHODS: Within a large US commercial insurance database and using a cohort design, we identified patients with T2DM initiating linagliptin or a comparator agent within class (ie, another dipeptidyl peptidase-4 inhibitor) or outside class (ie, pioglitazone or a sulphonylurea) between May 2011 and December 2012. We focused on comparators used at a similar stage of diabetes to linagliptin. For each comparison, 1:1 propensity score (PS) matching was used to balance >100 baseline claims-based characteristics, including proxies of diabetes severity and duration. Additional clinical data from EHR were available for a subset of patients. We assessed representativeness of the claims-EHR-linked subset, evaluated the balance of claims- and EHR-based covariates before and after PS-matching via standardized differences (SDs), and quantified the potential bias associated with observed imbalances. RESULTS: From a claims-based study population of 166 613 patients with T2DM, 7219 (4.3%) patients were linked to their EHR data. Claims-based characteristics in the EHR-linked and EHR-unlinked patients were similar (SD < 0.1), confirming the representativeness of the EHR-linked subset. The balance of claims-based and EHR-based patient characteristics appeared to be reasonable before PS-matching and generally improved in the PS-matched population, to be SD < 0.1 for most patient characteristics and SD < 0.2 for select laboratory results and body mass index categories, which was not large enough to cause meaningful confounding. CONCLUSION: In the context of pharmacoepidemiological research on diabetes therapy, choosing appropriate comparison groups paired with a new-user design and 1:1 PS matching on many proxies of diabetes severity and duration improves balance in covariates typically unmeasured in administrative claims datasets, to the extent that residual confounding is unlikely.