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BackgroundIn clinical research, important variables may be collected from multiple data sources. Physical pooling of patient-level data from multiple sources often raises several challenges, including proper protection of patient privacy and proprietary interests. We previously developed an SAS-based package to perform distributed regression—a suite of privacy-protecting methods that perform multivariable-adjusted regression analysis using only summary-level information—with horizontally partitioned data, a setting where distinct cohorts of patients are available from different data sources. We integrated the package with PopMedNet, an open-source file transfer software, to facilitate secure file transfer between the analysis center and the data-contributing sites. The feasibility of using PopMedNet to facilitate distributed regression analysis (DRA) with vertically partitioned data, a setting where the data attributes from a cohort of patients are available from different data sources, was unknown. ObjectiveThe objective of the study was to describe the feasibility of using PopMedNet and enhancements to PopMedNet to facilitate automatable vertical DRA (vDRA) in real-world settings. MethodsWe gathered the statistical and informatic requirements of using PopMedNet to facilitate automatable vDRA. We enhanced PopMedNet based on these requirements to improve its technical capability to support vDRA. ResultsPopMedNet can enable automatable vDRA. We identified and implemented two enhancements to PopMedNet that improved its technical capability to perform automatable vDRA in real-world settings. The first was the ability to simultaneously upload and download multiple files, and the second was the ability to directly transfer summary-level information between the data-contributing sites without a third-party analysis center. ConclusionsPopMedNet can be used to facilitate automatable vDRA to protect patient privacy and support clinical research in real-world settings.

BackgroundCertain medications may increase the risk of death or death from specific causes (eg, sudden cardiac death), but these risks may not be identified in premarket randomized trials. Having the capacity to examine death in postmarket safety surveillance activities is important to the US Food and Drug Administration’s (FDA) mission to protect public health. Distributed networks of electronic health plan databases used by the FDA to conduct multicenter research or medical product safety surveillance studies often do not systematically include death or cause-of-death information. ObjectiveThis study aims to develop reusable, generalizable methods for linking multiple health plan databases with the Centers for Disease Control and Prevention’s National Death Index Plus (NDI+) data. MethodsWe will develop efficient administrative workflows to facilitate multicenter institutional review board (IRB) review and approval within a distributed network of 6 health plans. The study will create a distributed NDI+ linkage process that avoids sharing of identifiable patient information between health plans or with a central coordinating center. We will develop standardized criteria for selecting and retaining NDI+ matches and methods for harmonizing linked information across multiple health plans. We will test our processes within a use case comprising users and nonusers of antiarrhythmic medications. ResultsWe will use the linked health plan and NDI+ data sets to estimate the incidences and incidence rates of mortality and specific causes of death within the study use case and compare the results with reported estimates. These comparisons provide an opportunity to assess the performance of the developed NDI+ linkage approach and lessons for future studies requiring NDI+ linkage in distributed database settings. This study is approved by the IRB at Harvard Pilgrim Health Care in Boston, MA. Results will be presented to the FDA at academic conferences and published in peer-reviewed journals. ConclusionsThis study will develop and test a reusable distributed NDI+ linkage approach with the goal of providing tested NDI+ linkage methods for use in future studies within distributed data networks. Having standardized and reusable methods for systematically obtaining death and cause-of-death information from NDI+ would enhance the FDA’s ability to assess mortality-related safety questions in the postmarket, real-world setting. International Registered Report Identifier (IRRID)DERR1-10.2196/21811

BackgroundA distributed data network approach combined with distributed regression analysis (DRA) can reduce the risk of disclosing sensitive individual and institutional information in multicenter studies. However, software that facilitates large-scale and efficient implementation of DRA is limited. ObjectiveThis study aimed to assess the precision and operational performance of a DRA application comprising a SAS-based DRA package and a file transfer workflow developed within the open-source distributed networking software PopMedNet in a horizontally partitioned distributed data network. MethodsWe executed the SAS-based DRA package to perform distributed linear, logistic, and Cox proportional hazards regression analysis on a real-world test case with 3 data partners. We used PopMedNet to iteratively and automatically transfer highly summarized information between the data partners and the analysis center. We compared the DRA results with the results from standard SAS procedures executed on the pooled individual-level dataset to evaluate the precision of the SAS-based DRA package. We computed the execution time of each step in the workflow to evaluate the operational performance of the PopMedNet-driven file transfer workflow. ResultsAll DRA results were precise (

Missing data complicates statistical analyses in multi-site studies, especially when it is not feasible to centrally pool individual-level data across sites. We combined meta-analysis with within-site multiple imputation for one-step estimation of the average causal effect (ACE) of a target population comprised of all individuals from all data-contributing sites within a multi-site distributed data network, without the need for sharing individual-level data to handle missing data. We considered two orders of combination and three choices of weights for meta-analysis, resulting in six approaches. The first three approaches, denoted as RR + metaF, RR + metaR and RR + std, first combined results from imputed data sets within each site using Rubin's rules and then meta-analyzed the combined results across sites using fixed-effect, random-effects and sample-standardization weights, respectively. The last three approaches, denoted as metaF + RR, metaR + RR and std + RR, first meta-analyzed results across sites separately for each imputation and then combined the meta-analysis results using Rubin's rules. Simulation results confirmed very good performance of RR + std and std + RR under various missing completely at random and missing at random settings. A direct application of the inverse-variance weighted meta-analysis based on site-specific ACEs can lead to biased results for the targeted network-wide ACE in the presence of treatment effect heterogeneity by site, demonstrating the need to clearly specify the target population and estimand and properly account for potential site heterogeneity in meta-analyses seeking to draw causal interpretations. An illustration using a large administrative claims database is presented.

BackgroundThere has been a dramatic shift in use of bariatric procedures, but little is known about their long-term comparative effectiveness.ObjectiveTo compare weight loss and safety among bariatric procedures.DesignRetrospective observational cohort study, January 2005 to September 2015. (ClinicalTrials.gov: NCT02741674).Setting41 health systems in the National Patient-Centered Clinical Research Network.Participants65 093 patients aged 20 to 79 years with body mass index (BMI) of 35 kg/m2 or greater who had bariatric procedures.Intervention32 208 Roux-en-Y gastric bypass (RYGB), 29 693 sleeve gastrectomy (SG), and 3192 adjustable gastric banding (AGB) procedures.MeasurementsEstimated percent total weight loss (TWL) at 1, 3, and 5 years; 30-day rates of major adverse events.ResultsTotal numbers of eligible patients with weight measures at 1, 3, and 5 years were 44 978 (84%), 20 783 (68%), and 7159 (69%), respectively. Thirty-day rates of major adverse events were 5.0% for RYGB, 2.6% for SG, and 2.9% for AGB. One-year mean TWLs were 31.2% (95% CI, 31.1% to 31.3%) for RYGB, 25.2% (CI, 25.1% to 25.4%) for SG, and 13.7% (CI, 13.3% to 14.0%) for AGB. At 1 year, RYGB patients lost 5.9 (CI, 5.8 to 6.1) percentage points more weight than SG patients and 17.7 (CI, 17.3 to 18.1) percentage points more than AGB patients, and SG patients lost 12.0 (CI, 11.6 to 12.5) percentage points more than AGB patients. Five-year mean TWLs were 25.5% (CI, 25.1% to 25.9%) for RYGB, 18.8% (CI, 18.0% to 19.6%) for SG, and 11.7% (CI, 10.2% to 13.1%) for AGB. Patients with diabetes, those with BMI less than 50 kg/m2, those aged 65 years or older, African American patients, and Hispanic patients lost less weight than patients without those characteristics.LimitationPotential unobserved confounding due to nonrandomized design; electronic health record databases had missing outcome data.ConclusionAdults lost more weight with RYGB than with SG or AGB at 1, 3, and 5 years; however, RYGB had the highest 30-day rate of major adverse events. Small subgroup differences in weight loss outcomes were observed.Primary funding sourcePatient-Centered Outcomes Research Institute.

Weight gain associated with antidepressants is a commonly reported side effect and may influence whether patients take or continue to take these medications. This study examines the electronic health record information of patients who were newly prescribed 1 of 8 common antidepressant medications to estimate weight gain associated with each over time. Visual Abstract. Medication-Induced Weight Change Across Common Antidepressant Treatments: Weight gain associated with antidepressants is a commonly reported side effect and may influence whether patients take or continue to take these medications. This study examines the electronic health record information of patients who were newly prescribed 1 of 8 common antidepressant medications to estimate weight gain associated with each over time. Background: Antidepressants are among the most commonly prescribed medications, but evidence on comparative weight change for specific first-line treatments is limited. Objective: To compare weight change across common first-line antidepressant treatments by emulating a target trial. Design: Observational cohort study over 24 months. Setting: Electronic health record (EHR) data from 2010 to 2019 across 8 U.S. health systems. Participants: 183 118 patients. Measurements: Prescription data determined initiation of treatment with sertraline, citalopram, escitalopram, fluoxetine, paroxetine, bupropion, duloxetine, or venlafaxine. The investigators estimated the population-level effects of initiating each treatment, relative to sertraline, on mean weight change (primary) and the probability of gaining at least 5% of baseline weight (secondary) 6 months after initiation. Inverse probability weighting of repeated outcome marginal structural models was used to account for baseline confounding and informative outcome measurement. In secondary analyses, the effects of initiating and adhering to each treatment protocol were estimated. Results: Compared with that for sertraline, estimated 6-month weight gain was higher for escitalopram (difference, 0.41 kg [95% CI, 0.31 to 0.52 kg]), paroxetine (difference, 0.37 kg [CI, 0.20 to 0.54 kg]), duloxetine (difference, 0.34 kg [CI, 0.22 to 0.44 kg]), venlafaxine (difference, 0.17 kg [CI, 0.03 to 0.31 kg]), and citalopram (difference, 0.12 kg [CI, 0.02 to 0.23 kg]); similar for fluoxetine (difference, −0.07 kg [CI, −0.19 to 0.04 kg]); and lower for bupropion (difference, −0.22 kg [CI, −0.33 to −0.12 kg]). Escitalopram, paroxetine, and duloxetine were associated with 10% to 15% higher risk for gaining at least 5% of baseline weight, whereas bupropion was associated with 15% reduced risk. When the effects of initiation and adherence were estimated, associations were stronger but had wider CIs. Six-month adherence ranged from 28% (duloxetine) to 41% (bupropion). Limitation: No data on medication dispensing, low medication adherence, incomplete data on adherence, and incomplete data on weight measures across time points. Conclusion: Small differences in mean weight change were found between 8 first-line antidepressants, with bupropion consistently showing the least weight gain, although adherence to medications over follow-up was low. Clinicians could consider potential weight gain when initiating antidepressant treatment. Primary Funding Source: National Institutes of Health. [ABSTRACT FROM AUTHOR]

Objectives Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions. Materials and methods PheNorm is a general-purpose automated approach to creating computable phenotype algorithms based on natural language processing, machine learning, and (low cost) silver-standard training labels. We applied PheNorm to cohorts of potential COVID-19 patients from 2 institutions and used gold-standard manual chart review data to investigate the impact on performance of alternative feature engineering options and implementing externally trained models without local retraining. Results Models at each institution achieved AUC, sensitivity, and positive predictive value of 0.853, 0.879, 0.851 and 0.804, 0.976, and 0.885, respectively, at quantiles of model-predicted risk that maximize F1. We report performance metrics for all combinations of silver labels, feature engineering options, and models trained internally versus externally. Discussion Phenotyping algorithms developed using PheNorm performed well at both institutions. Performance varied with different silver-standard labels and feature engineering options. Models developed locally at one site also worked well when implemented externally at the other site. Conclusion PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches. [ABSTRACT FROM AUTHOR]

Purpose: Observational studies assessing effects of medical products on suicidal behavior often rely on health record data to account for pre‐existing risk. We assess whether high‐dimensional models predicting suicide risk using data derived from insurance claims and electronic health records (EHRs) are superior to models using data from insurance claims alone. Methods: Data were from seven large health systems identified outpatient mental health visits by patients aged 11 or older between 1/1/2009 and 9/30/2017. Data for the 5 years prior to each visit identified potential predictors of suicidal behavior typically available from insurance claims (e.g., mental health diagnoses, procedure codes, medication dispensings) and additional potential predictors available from EHRs (self‐reported race and ethnicity, responses to Patient Health Questionnaire or PHQ‐9 depression questionnaires). Nonfatal self‐harm events following each visit were identified from insurance claims data and fatal self‐harm events were identified by linkage to state mortality records. Random forest models predicting nonfatal or fatal self‐harm over 90 days following each visit were developed in a 70% random sample of visits and validated in a held‐out sample of 30%. Performance of models using linked claims and EHR data was compared to models using claims data only. Results: Among 15 845 047 encounters by 1 574 612 patients, 99 098 (0.6%) were followed by a self‐harm event within 90 days. Overall classification performance did not differ between the best‐fitting model using all data (area under the receiver operating curve or AUC = 0.846, 95% CI 0.839–0.854) and the best‐fitting model limited to data available from insurance claims (AUC = 0.846, 95% CI 0.838–0.853). Competing models showed similar classification performance across a range of cut‐points and similar calibration performance across a range of risk strata. Results were similar when the sample was limited to health systems and time periods where PHQ‐9 depression questionnaires were recorded more frequently. Conclusion: Investigators using health record data to account for pre‐existing risk in observational studies of suicidal behavior need not limit that research to databases including linked EHR data. [ABSTRACT FROM AUTHOR]