Comparative Effectiveness and Safety of Apixaban, Rivaroxaban, and Warfarin in Patients With Cirrhosis and Atrial Fibrillation : A Nationwide Cohort Study.

BACKGROUND: Apixaban, rivaroxaban, and warfarin have shown benefit for preventing major ischemic events, albeit with increased bleeding risk, among patients in the general population with atrial fibrillation (AF). However, data are scarce in patients with cirrhosis and AF. OBJECTIVE: To compare the effectiveness and safety of apixaban versus rivaroxaban and versus warfarin in patients with cirrhosis and AF. DESIGN: Population-based cohort study. SETTING: Two U.S. claims data sets (Medicare and Optum's de-identified Clinformatics Data Mart Database [2013 to 2022]). PARTICIPANTS: 1:1 propensity score (PS)-matched patients with cirrhosis and nonvalvular AF initiating use of apixaban, rivaroxaban, or warfarin. MEASUREMENTS: Primary outcomes included ischemic stroke or systemic embolism and major hemorrhage (intracranial hemorrhage or major gastrointestinal bleeding). Database-specific and pooled PS-matched rate differences (RDs) per 1000 person-years (PY) and Cox proportional hazard ratios (HRs) with 95% CIs were estimated, controlling for 104 preexposure covariates. RESULTS: Rivaroxaban initiators had significantly higher rates of major hemorrhagic events than apixaban initiators (RD, 33.1 per 1000 PY [95% CI, 12.9 to 53.2 per 1000 PY]; HR, 1.47 [CI, 1.11 to 1.94]) but no significant differences in rates of ischemic events or death. Consistently higher rates of major hemorrhage were found with rivaroxaban across subgroup and sensitivity analyses. Warfarin initiators also had significantly higher rates of major hemorrhage than apixaban initiators (RD, 26.1 per 1000 PY [CI, 6.8 to 45.3 per 1000 PY]; HR, 1.38 [CI, 1.03 to 1.84]), particularly hemorrhagic stroke (RD, 9.7 per 1000 PY [CI, 2.2 to 17.2 per 1000 PY]; HR, 2.85 [CI, 1.24 to 6.59]). LIMITATION: Nonrandomized treatment selection. CONCLUSION: Among patients with cirrhosis and nonvalvular AF, initiators of rivaroxaban versus apixaban had significantly higher rates of major hemorrhage and similar rates of ischemic events and death. Initiation of warfarin versus apixaban also contributed to significantly higher rates of major hemorrhagic events, including hemorrhagic stroke. PRIMARY FUNDING SOURCE: National Institutes of Health.

Development and Validation of a Claims-Based Model to Predict Categories of Obesity.

We developed and validated a claims-based algorithm that classifies patients into obesity categories. Using Medicare (2007-2017) and Medicaid (2000-2014) claims data linked to 2 electronic health record (EHR) systems in Boston, Massachusetts, we identified a cohort of patients with an EHR-based body mass index (BMI) measurement (calculated as weight (kg)/height (m)2). We used regularized regression to select from 137 variables and built generalized linear models to classify patients with BMIs of ≥25, ≥30, and ≥40. We developed the prediction model using EHR system 1 (training set) and validated it in EHR system 2 (validation set). The cohort contained 123,432 patients in the Medicare population and 40,736 patients in the Medicaid population. The model comprised 97 variables in the Medicare set and 95 in the Medicaid set, including BMI-related diagnosis codes, cardiovascular and antidiabetic drugs, and obesity-related comorbidities. The areas under the receiver-operating-characteristic curve in the validation set were 0.72, 0.75, and 0.83 (Medicare) and 0.66, 0.66, and 0.70 (Medicaid) for BMIs of ≥25, ≥30, and ≥40, respectively. The positive predictive values were 81.5%, 80.6%, and 64.7% (Medicare) and 81.6%, 77.5%, and 62.5% (Medicaid), for BMIs of ≥25, ≥30, and ≥40, respectively. The proposed model can identify obesity categories in claims databases when BMI measurements are missing and can be used for confounding adjustment, defining subgroups, or probabilistic bias analysis.

Targeted Learning with an Undersmoothed Lasso Propensity Score Model for Large-Scale Covariate Adjustment in Healthcare Database Studies.

Lasso regression is widely used for large-scale propensity score (PS) estimation in healthcare database studies. In these settings, previous work has shown that undersmoothing (overfitting) Lasso PS models can improve confounding control, but it can also cause problems of non-overlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale Lasso PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed Lasso PS-models, the use of cross-fitting was important for avoiding non-overlap in covariate distributions and reducing bias in causal estimates.

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.

Comparative Safety and Effectiveness of Warfarin or Rivaroxaban Versus Apixaban in Patients With Advanced CKD and Atrial Fibrillation: Nationwide US Cohort Study.

RATIONALE & OBJECTIVE: Head-to-head data comparing the effectiveness and safety of oral anticoagulants in patients with atrial fibrillation (AF) and advanced chronic kidney disease (CKD) are lacking. We compared the safety and effectiveness of warfarin or rivaroxaban versus apixaban in patients with AF and non-dialysis-dependent CKD stage 4/5. STUDY DESIGN: Propensity score-matched cohort study. SETTING & PARTICIPANTS: 2 nationwide US claims databases, Medicare and Optum's deidentified Clinformatics Data Mart Database, were searched for the interval from January 1, 2013, through March 31, 2022, for patients with nonvalvular AF and CKD stage 4/5 who initiated warfarin versus apixaban (matched cohort, n=12,488) and rivaroxaban versus apixaban (matched cohort, n = 5,720). EXPOSURES: Warfarin, rivaroxaban, or apixaban. OUTCOMES: Primary outcomes included major bleeding and ischemic stroke. Secondary outcomes included all-cause mortality, major gastrointestinal bleeding, and intracranial bleeding. ANALYTICAL APPROACH: Cox regression was used to estimate HRs, and 1:1 propensity-score matching was used to adjust for 80 potential confounders. RESULTS: Compared with apixaban, warfarin initiation was associated with a higher rate of major bleeding (HR, 1.85; 95% CI, 1.59-2.15), including major gastrointestinal bleeding (1.86; 1.53-2.25) and intracranial bleeding (2.15; 1.42-3.25). Compared with apixaban, rivaroxaban was also associated with a higher rate of major bleeding (1.69; 1.33-2.15). All-cause mortality was similar for warfarin (1.08; 0.98-1.18) and rivaroxaban (0.94; 0.81-1.10) versus apixaban. Furthermore, no statistically significant differences for ischemic stroke were observed for warfarin (1.14; 0.83-1.57) or rivaroxaban (0.71; 0.40-1.24) versus apixaban, but the CIs were wide. Similar results were observed for warfarin versus apixaban in the positive control cohort of patients with CKD stage 3, consistent with randomized trial findings. LIMITATIONS: Few ischemic stroke events, potential residual confounding. CONCLUSIONS: In patients with AF and advanced CKD, rivaroxaban and warfarin were associated with higher rates of major bleeding compared with apixaban, suggesting a superior safety profile for apixaban in this high-risk population. PLAIN-LANGUAGE SUMMARY: Different anticoagulants have been shown to reduce the risk of stroke in patients with atrial fibrillation, such as warfarin and direct oral anticoagulants like apixaban and rivaroxaban. Unfortunately, the large-scale randomized trials that compared direct anticoagulants versus warfarin excluded patients with advanced chronic kidney disease. Therefore, the comparative safety and effectiveness of warfarin, apixaban, and rivaroxaban are uncertain in this population. In this study, we used administrative claims data from the United States to answer this question. We found that warfarin and rivaroxaban were associated with increased risks of major bleeding compared with apixaban. There were few stroke events, with no major differences among the 3 drugs in the risk of stroke. In conclusion, this study suggests that apixaban has a better safety profile than warfarin and rivaroxaban.

Emulation of randomized trials of direct oral anticoagulants with claims data and implications for new Factor XI inhibitors.

Direct oral anticoagulants (DOACs) revolutionized the management of thromboembolic disorders. Clinical care may be further improved as Factor XIs undergo large-scale outcome trials. What role can non-randomized database studies play in expediting understanding of these drugs in clinical practice? The RCT-DUPLICATIVE Initiative emulated the design of eight DOAC randomized clinical trials (RCT) using non-randomized claims database studies. RCT study design parameters and measurements were closely emulated by the database studies and produced highly concordant results. The results of the single database study that did not meet all agreement metrics with the specific RCT it was emulating were aligned with a meta-analysis of six trials studying similar questions, suggesting the trial result was an outlier. Well-designed database studies using fit-for-purpose data came to the same conclusions as DOAC trials, illustrating how database studies could complement RCTs for Factor XI inhibitors-by accelerating insights in underrepresented populations, demonstrating effectiveness and safety in clinical practice, and testing broader indications.

Validation of alopecia coding in US claims data among women of childbearing age.

BACKGROUND: Accurately identifying alopecia in claims data is important to study this rare medication side effect. OBJECTIVES: To develop and validate a claims-based algorithm to identify alopecia in women of childbearing age. METHODS: We linked electronic health records from a large healthcare system in Massachusetts (Mass General Brigham) with Medicaid claims data from 2016 through 2018 to identify all women aged 18 to 50 years with an ICD-10 code for alopecia, including alopecia areata, androgenic alopecia, non-scarring alopecia, or cicatricial alopecia, from a visit to the MGB system. Using eight predefined algorithms to identify alopecia in Medicaid claims data, we randomly selected 300 women for whom we reviewed their charts to validate the alopecia diagnosis. Positive predictive values (PPVs) were computed for the primary algorithm and seven algorithm variations, stratified by race. RESULTS: Out of 300 patients with at least 1 ICD-10 code for alopecia in the Medicaid claims, 286 had chart-confirmed alopecia (PPV = 95.3%). The algorithm requiring two diagnosis codes plus one prescription claim for alopecia treatment identified 55 patients (PPV = 100%). The algorithm requiring 1 diagnosis code for alopecia plus 1 procedure claim for intralesional triamcinolone injection identified 35 patients (PPV = 100%). Across all 8 algorithms tested, the PPV varied between 95.3% and 100%. The PPV for alopecia ranged from 94% to 100% in White and 96%-100% in 48 non-White women. The exact date of alopecia onset was difficult to determine in charts. CONCLUSION: At least one recorded ICD-10 code for alopecia in claims data identified alopecia in women of childbearing age with high accuracy.

Identifying Functional Status Impairment in People Living With Dementia Through Natural Language Processing of Clinical Documents: Cross-Sectional Study.

BACKGROUND: Assessment of activities of daily living (ADLs) and instrumental ADLs (iADLs) is key to determining the severity of dementia and care needs among older adults. However, such information is often only documented in free-text clinical notes within the electronic health record and can be challenging to find. OBJECTIVE: This study aims to develop and validate machine learning models to determine the status of ADL and iADL impairments based on clinical notes. METHODS: This cross-sectional study leveraged electronic health record clinical notes from Mass General Brigham's Research Patient Data Repository linked with Medicare fee-for-service claims data from 2007 to 2017 to identify individuals aged 65 years or older with at least 1 diagnosis of dementia. Notes for encounters both 180 days before and after the first date of dementia diagnosis were randomly sampled. Models were trained and validated using note sentences filtered by expert-curated keywords (filtered cohort) and further evaluated using unfiltered sentences (unfiltered cohort). The model's performance was compared using area under the receiver operating characteristic curve and area under the precision-recall curve (AUPRC). RESULTS: The study included 10,000 key-term-filtered sentences representing 441 people (n=283, 64.2% women; mean age 82.7, SD 7.9 years) and 1000 unfiltered sentences representing 80 people (n=56, 70% women; mean age 82.8, SD 7.5 years). Area under the receiver operating characteristic curve was high for the best-performing ADL and iADL models on both cohorts (>0.97). For ADL impairment identification, the random forest model achieved the best AUPRC (0.89, 95% CI 0.86-0.91) on the filtered cohort; the support vector machine model achieved the highest AUPRC (0.82, 95% CI 0.75-0.89) for the unfiltered cohort. For iADL impairment, the Bio+Clinical bidirectional encoder representations from transformers (BERT) model had the highest AUPRC (filtered: 0.76, 95% CI 0.68-0.82; unfiltered: 0.58, 95% CI 0.001-1.0). Compared with a keyword-search approach on the unfiltered cohort, machine learning reduced false-positive rates from 4.5% to 0.2% for ADL and 1.8% to 0.1% for iADL. CONCLUSIONS: In this study, we demonstrated the ability of machine learning models to accurately identify ADL and iADL impairment based on free-text clinical notes, which could be useful in determining the severity of dementia.

Effectiveness research in oncology with electronic health record data: A retrospective cohort study emulating the PALOMA-2 trial.

PURPOSE: Oncology electronic health record (EHR) databases have increased in quality and availability over the past decade, yet it remains unclear whether these clinical practice data can be used to conduct reliable comparative effectiveness studies. We sought to emulate a clinical trial with EHR data in the advanced breast cancer population and compare our results against the trial. METHODS: This cohort study used EHR data from US oncology practices. All elements of the study were defined to mimic the PALOMA-2 trial as closely as possible. Patients with hormone-positive, HER-2 negative metastatic breast cancer with no prior treatment for metastatic disease were included. Patients initiating palbociclib and letrozole on the same day following the earliest record of metastasis were compared to those initiating letrozole only. The primary associational measure was the conditional hazard ratio for time-to-next treatment (TTNT). TTNT is well-measured in our data source and amenable for calibration against the randomized study results of the PALOMA-2 trial. We used multiple imputation for several patient characteristics with missing values. RESULTS: There were 3836 study-eligible women with advanced breast cancer. The hazard ratio for TTNT in the observational study (HR: 0.62; 95% CI: 0.56-0.68) was closely aligned with that of the randomized trial (HR: 0.64; 95% CI: 0.52-0.78). CONCLUSIONS: Under our assumptions on missing data and comparability of the two study populations, results from our non-randomized study closely matched that of the randomized trial. Further studies are needed to determine whether EHR data can yield reliable conclusions on treatment effects in oncology.

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

BACKGROUND: We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. METHODS: We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including >40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 h. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. RESULTS: Overall, 3706 patients (2587 in the development and 1119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79-0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. CONCLUSIONS: Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors.

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.

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.

Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.

PURPOSE: Supplementing investigator-specified variables with large numbers of empirically identified features that collectively serve as 'proxies' for unspecified or unmeasured factors can often improve confounding control in studies utilizing administrative healthcare databases. Consequently, there has been a recent focus on the development of data-driven methods for high-dimensional proxy confounder adjustment in pharmacoepidemiologic research. In this paper, we survey current approaches and recent advancements for high-dimensional proxy confounder adjustment in healthcare database studies. METHODS: We discuss considerations underpinning three areas for high-dimensional proxy confounder adjustment: (1) feature generation-transforming raw data into covariates (or features) to be used for proxy adjustment; (2) covariate prioritization, selection, and adjustment; and (3) diagnostic assessment. We discuss challenges and avenues of future development within each area. RESULTS: There is a large literature on methods for high-dimensional confounder prioritization/selection, but relatively little has been written on best practices for feature generation and diagnostic assessment. Consequently, these areas have particular limitations and challenges. CONCLUSIONS: There is a growing body of evidence showing that machine-learning algorithms for high-dimensional proxy-confounder adjustment can supplement investigator-specified variables to improve confounding control compared to adjustment based on investigator-specified variables alone. However, more research is needed on best practices for feature generation and diagnostic assessment when applying methods for high-dimensional proxy confounder adjustment in pharmacoepidemiologic studies.

Validation of obesity-related diagnosis codes in claims data.

AIM: To determine whether body mass index (BMI) can be accurately identified in epidemiological studies using claims databases. MATERIALS AND METHODS: Using the Mass General Brigham Research Patient Data Repository-Medicare-linked database, we identified a cohort of patients with a BMI measurement for the periods January 1 to June 31, 2014 or January 1 to June 31, 2016, to capture both the International Classification of Disease (ICD)-9 and ICD-10 eras. Patients were divided into two groups, with or without an obesity-related ICD code in the 6 months before or after the BMI measurement date. We created two binary measures, first for composite overweight, obesity, or severe obesity (BMI ≥25 kg/m2 ), and second for obesity or severe obesity (BMI ≥30 kg/m2 ). We calculated accuracy measures (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) for each obesity category for the overall cohort, and stratified by type 2 diabetes and ICD-code era. RESULTS: The cohort included 73 644 patients with a BMI measurement in 2014 or 2016, of whom 16 280 had an obesity-related ICD code. The specificity of obesity-related ICD codes (ICD-9 and ICD-10) was 99.7% for underweight/normal weight, 97.4% for overweight, 99.7% for obese and 98.9% for severely obese. For binary categories capturing BMI ≥25 kg/m2 and BMI ≥30 kg/m2 , specificity was 97.0% and 98.2%, and PPV was 86.9% and 97.3%. Sensitivity was low overall (<40%). Codes for patients with type 2 diabetes and codes in the ICD-10 era had higher sensitivity, PPV and NPV. CONCLUSION: Obesity-related ICD codes can accurately identify patients with obesity in epidemiological studies using claims databases.

Effectiveness and Safety of Apixaban Compared With Rivaroxaban for Patients With Atrial Fibrillation in Routine Practice: A Cohort Study.

Background: Apixaban and rivaroxaban are the most commonly prescribed direct oral anticoagulants for adults with atrial fibrillation, but head-to-head data comparing their safety and effectiveness are lacking. Objective: To compare the safety and effectiveness of apixaban versus rivaroxaban for patients with nonvalvular atrial fibrillation. Design: New-user, active-comparator, retrospective cohort study. Setting: A U.S. nationwide commercial health care claims database from 28 December 2012 to 1 January 2019. Patients: Adults newly prescribed apixaban (n = 59 172) or rivaroxaban (n = 40 706). Measurements: The primary effectiveness outcome was a composite of ischemic stroke or systemic embolism. The primary safety outcome was a composite of intracranial hemorrhage or gastrointestinal bleeding. Results: 39 351 patients newly prescribed apixaban were propensity score matched to 39 351 patients newly prescribed rivaroxaban. Mean age was 69 years, 40% of patients were women, and mean follow-up was 288 days for new apixaban users and 291 days for new rivaroxaban users. The incidence rate of ischemic stroke or systemic embolism was 6.6 per 1000 person-years for adults prescribed apixaban compared with 8.0 per 1000 person-years for those prescribed rivaroxaban (hazard ratio [HR], 0.82 [95% CI, 0.68 to 0.98]; rate difference, 1.4 fewer events per 1000 person-years [CI, 0.0 to 2.7]). Adults prescribed apixaban also had a lower rate of gastrointestinal bleeding or intracranial hemorrhage (12.9 per 1000 person-years) compared with those prescribed rivaroxaban (21.9 per 1000 person-years), corresponding to an HR of 0.58 (CI, 0.52 to 0.66) and a rate difference of 9.0 fewer events per 1000 person-years (CI, 6.9 to 11.1). Limitation: Unmeasured confounding, incomplete laboratory data. Conclusion: In routine care, adults with atrial fibrillation prescribed apixaban had a lower rate of both ischemic stroke or systemic embolism and bleeding compared with those prescribed rivaroxaban. Primary Funding Source: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital.

Prescribing systemic steroids for acute respiratory tract infections in United States outpatient settings: A nationwide population-based cohort study.

BACKGROUND: Evidence and guidelines do not support use of systemic steroids for acute respiratory tract infections (ARTIs), but such practice appears common. We aim to quantify such use and determine its predictors. METHODS AND FINDINGS: We conducted a cohort study based on a large United States national commercial claims database, the IBM MarketScan, to identify patients aged 18-64 years with an ARTI diagnosis (acute bronchitis, sinusitis, pharyngitis, otitis media, allergic rhinitis, influenza, pneumonia, and unspecified upper respiratory infections) recorded in ambulatory visits from 2007 to 2016. We excluded those with systemic steroid use in the prior year and an extensive list of steroid-indicated conditions, including asthma, chronic obstructive pulmonary disease, and various autoimmune diseases. We calculated the proportion receiving systemic steroids within 7 days of the ARTI diagnosis and determined its significant predictors. We identified 9,763,710 patients with an eligible ARTI encounter (mean age 39.6, female 56.0%) and found 11.8% were prescribed systemic steroids (46.1% parenteral, 47.3% oral, 6.6% both). All ARTI diagnoses but influenza predicted receiving systemic steroids. There was high geographical variability: the adjusted odds ratio (aOR) of receiving parenteral steroids was 14.48 (95% confidence interval [CI] 14.23-14.72, p < 0.001) comparing southern versus northeastern US. The corresponding aOR was 1.68 (95% CI 1.66-1.69, p < 0.001) for oral steroids. Other positive predictors for prescribing included emergency department (ED) or urgent care settings (versus regular office), otolaryngologist/ED doctors (versus primary care), fewer comorbidities, and older patient age. There was an increasing trend from 2007 to 2016 (aOR 1.93 [95% CI 1.91-1.95] comparing 2016 to 2007, p < 0.001). Our findings are based on patients between 18 and 64 years old with commercial medical insurance and may not be generalizable to older or uninsured populations. CONCLUSIONS: In this study, we found that systemic steroid use in ARTI is common with a great geographical variability. These findings call for an effective education program about this practice, which does not have a clear clinical net benefit.

Incorporating patient generated health data into pharmacoepidemiological research.

Epidemiology and pharmacoepidemiology frequently employ Real-World Data (RWD) from healthcare teams to inform research. These data sources usually include signs, symptoms, tests, and treatments, but may lack important information such as the patient's diet or adherence or quality of life. By harnessing digital tools a new fount of evidence, Patient (or Citizen/Person) Generated Health Data (PGHD), is becoming more readily available. This review focusses on the advantages and considerations in using PGHD for pharmacoepidemiological research. New and corroborative types of data can be collected directly from patients using digital devices, both passively and actively. Practical issues such as patient engagement, data linking, validation, and analysis are among important considerations in the use of PGHD. In our ever increasingly patient-centric world, PGHD incorporated into more traditional Real-Word data sources offers innovative opportunities to expand our understanding of the complex factors involved in health and the safety and effectiveness of disease treatments. Pharmacoepidemiologists have a unique role in realizing the potential of PGHD by ensuring that robust methodology, governance, and analytical techniques underpin its use to generate meaningful research results.

Out-of-system Care and Recording of Patient Characteristics Critical for Comparative Effectiveness Research.

BACKGROUND: It is unclear how out-of-system care or electronic health record (EHR) discontinuity (i.e., receiving care outside of an EHR system) may affect validity of comparative effectiveness research using these data. We aimed to compare the misclassification of key variables in patients with high versus low EHR continuity. METHODS: The study cohort comprised patients ages ≥65 identified in electronic health records from two US provider networks linked with Medicare insurance claims data from 2007 to 2014. By comparing electronic health records and claims data, we quantified EHR continuity by the proportion of encounters captured by the EHRs (i.e., "capture proportion"). Within levels of EHR continuity, for 40 key variables, we quantified misclassification by mean standardized differences between coding based on EHRs alone versus linked claims and EHR data. RESULTS: Based on 183,739 patients, we found that mean capture proportion in a single electronic health record system was 16%-27% across two provider networks. Patients with highest level of EHR continuity (capture proportion ≥ 80%) had 11.4- to 17.4-fold less variable misclassification, when compared with those with lowest level of EHR continuity (capture proportion< 10%). Capturing at least 60% of the encounters in an EHR system was required to have reasonable variable classification (mean standardized difference <0.1). We found modest differences in comorbidity profiles between patients with high and low EHR continuity. CONCLUSIONS: EHR discontinuity may lead to substantial misclassification in key variables. Restricting comparative effectiveness research to patients with high EHR continuity may confer a favorable benefit (reducing information bias) to risk (losing generalizability) ratio.

Assessing the Benefits and Harms of Pharmacotherapy in Older Adults with Frailty: Insights from Pharmacoepidemiologic Studies of Routine Health Care Data.

The objective of this review is to summarize and appraise the research methodology, emerging findings, and future directions in pharmacoepidemiologic studies assessing the benefits and harms of pharmacotherapies in older adults with different levels of frailty. Older adults living with frailty are at elevated risk for poor health outcomes and adverse effects from pharmacotherapy. However, current evidence is limited due to the under-enrollment of frail older adults and the lack of validated frailty assessments in clinical trials. Recent advancements in measuring frailty in administrative claims and electronic health records (database-derived frailty scores) have enabled researchers to identify patients with frailty and to evaluate the heterogeneity of treatment effects by patients' frailty levels using routine health care data. When selecting a database-derived frailty score, researchers must consider the type of data (e.g., different coding systems), the length of the predictor assessment period, the extent of validation against clinically validated frailty measures, and the possibility of surveillance bias arising from unequal access to care. We reviewed 13 pharmacoepidemiologic studies published on PubMed from 2013 to 2023 that evaluated the benefits and harms of cardiovascular medications, diabetes medications, anti-neoplastic agents, antipsychotic medications, and vaccines by frailty levels. These studies suggest that, while greater frailty is positively associated with adverse treatment outcomes, older adults with frailty can still benefit from pharmacotherapy. Therefore, we recommend routine frailty subgroup analyses in pharmacoepidemiologic studies. Despite data and design limitations, the findings from such studies may be informative to tailor pharmacotherapy for older adults across the frailty spectrum.