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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.

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]

Biological plausibility suggests that fluoroquinolones may lead to mitral valve regurgitation or aortic valve regurgitation (MR/AR) through a collagen degradation pathway. However, available real‐world studies were limited and yielded inconsistent findings. We estimated the risk of MR/AR associated with fluoroquinolones compared with other antibiotics with similar indications in a population‐based cohort study. We identified adult patients who initiated fluoroquinolones or comparison antibiotics from the nationwide Taiwanese claims database. Patients were followed for up to 60 days after cohort entry. Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of MR/AR comparing fluoroquinolones to comparison antibiotics after 1:1 propensity score (PS) matching. All analyses were conducted by type of fluoroquinolone (fluoroquinolones as a class, respiratory fluoroquinolones, and non‐respiratory fluoroquinolones) and comparison antibiotic (amoxicillin/clavulanate or ampicillin/sulbactam, extended‐spectrum cephalosporins). Among 6,649,284 eligible patients, the crude incidence rates of MR/AR ranged from 1.44 to 4.99 per 1,000 person‐years across different types of fluoroquinolones and comparison antibiotics. However, fluoroquinolone use was not associated with an increased risk in each pairwise PS‐matched comparison. HRs were 1.00 (95% CI, 0.89–1.11) for fluoroquinolones as a class, 0.96 (95% CI, 0.83–1.12) for respiratory fluoroquinolones, and 0.87 (95% CI, 0.75–1.01) for non‐respiratory fluoroquinolones, compared with amoxicillin/clavulanate or ampicillin/sulbactam. Results were similar when fluoroquinolones were compared with extended‐spectrum cephalosporins (HRs of 0.96, 95% CI, 0.82–1.12, HR, 1.05, 95% CI, 0.86–1.28, and HR, 0.88, 95% CI, 0.75–1.03, respectively). This large‐scale cohort study did not find a higher risk of MR/AR with different types of fluoroquinolones in the adult population. [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]

Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general distributed methodology to fit Cox proportional hazards models without sharing individual-level data in multi-site studies. We make inferences on the log hazard ratios based on an approximated partial likelihood score function that uses only summary-level statistics. This approach can be applied to both stratified and unstratified models, accommodate both discrete and continuous exposure variables, and permit the adjustment of multiple covariates. In particular, the fitting of stratified Cox models can be carried out with only one file transfer of summary-level information. We derive the asymptotic properties of the proposed estimators and compare the proposed estimators with the maximum partial likelihood estimators using pooled individual-level data and meta-analysis methods through simulation studies. We apply the proposed method to a real-world data set to examine the effect of sleeve gastrectomy versus Roux-en-Y gastric bypass on the time to first postoperative readmission. [ABSTRACT FROM AUTHOR]

Objective: This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network.Material and Methods: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population.Results: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%.Discussion: The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population.Conclusions: This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe. [ABSTRACT FROM AUTHOR]