Continuation Versus Discontinuation of Sodium-Glucose Cotransporter-2 Inhibitors and Cardiorenal Outcomes Among Patients With Type 2 Diabetes and Chronic Kidney Disease: A Nationwide Cohort Study With a Target Trial Emulation Framework.

Clinical guidelines recommend continuation of sodium-glucose cotransporter-2 inhibitor (SGLT2i) treatment when renal function deteriorates among patients with type 2 diabetes. However, the recommendation is not currently supported by research specifically designed to compare continuation versus discontinuation of SGLT2is for patients who have received the treatment for a while before experiencing renal function deterioration. Using linked Taiwanese databases with claims and clinical data and a target trial emulation framework, we conducted a nationwide cohort study to investigate the association between SGLT2i continuation and major cardiorenal outcomes after renal function declined.

Neural tube defect risk in relation to opioid analgesic exposure during early pregnancy.

We evaluated neural tube defect (NTD) risk associated with prescription opioid analgesic use during early pregnancy. We conducted a cohort study of liveborn singletons during 2001-2014 among nine U.S.

Interplay of Spontaneous Reporting and Longitudinal Healthcare Databases for Signal Management: Position Statement from the Real-World Evidence and Big Data Special Interest Group of the International Society of Pharmacovigilance.

Signal management, defined as the set of activities from signal detection to recommendations for action, is conducted using different data sources and leveraging data from spontaneous reporting databases (SRDs), which represent the cornerstone of pharmacovigilance. However, the exponentially increasing generation and availability of real-world data collected in longitudinal healthcare databases (LHDs), along with the rapid evolution of artificial intelligence-based algorithms and other advanced analytical methods, offers a wide range of opportunities to complement SRDs throughout all stages of signal management, especially signal detection. Integrating information derived from SRDs and LHDs may reduce their respective limitations, thus potentially enhancing post-marketing surveillance.

Characteristics and Medication Use Patterns of Pregnancies With COVID-19 Ending in Live-Birth in the Sentinel System.

Background: Pregnant women are at high risk for developing severe illness related to COVID-19. We adapted the "COVID-19 infectiOn aNd medicineS In pregnancy" (CONSIGN) study protocol as part of an international collaboration to examine medication use patterns among pregnancies in the US.

Methods: We identified eligible women aged 12-55 years with documented live-birth deliveries in the Sentinel Distributed Database who had at least one qualifying diagnosis for COVID-19 or a positive-confirmed test for SARS-CoV-2, by trimester of COVID-19 infection.

High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates.

Multiple imputation (MI) models can be improved with auxiliary covariates (AC), but their performance in high-dimensional data remains unclear. We aimed to develop and compare high-dimensional MI (HDMI) methods using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation with acute kidney injury as outcome and simulated 100 cohorts with a null treatment effect, incorporating creatinine labs, atrial fibrillation (AFib), and other investigator-derived confounders in the outcome generation.

Real-World Effectiveness of Bebtelovimab Versus Nirmatrelvir/Ritonavir in Outpatients with COVID-19.

Introduction: This real-world study assessed the effectiveness of bebtelovimab (BEB) versus nirmatrelvir/ritonavir (NR) among outpatients with COVID-19 during the Omicron variant era.

Methods: We conducted a cohort study evaluating patients treated with BEB or NR from February to August 2022 (study period). Follow-up began the day after treatment and continued for 30 days.

A simple Cox approach to estimating risk ratios without sharing individual-level data in multisite studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at the sites of multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (eg, distributed regression) or only provide approximate estimation of the risk ratio (eg, meta-analysis).

Medication-Induced Weight Change Across Common Antidepressant Treatments : A Target Trial Emulation Study.

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.

Use of the Sentinel System to Examine Medical Product Use and Outcomes During Pregnancy.

While many pregnant individuals use prescription medications, evidence supporting product safety during pregnancy is often inadequate. Existing electronic healthcare data sources provide large, diverse samples of health plan members to allow for the study of medical product utilization during pregnancy, as well as pregnancy, maternal, and infant outcomes. The Sentinel System is a national medical product surveillance system that includes administrative claims and electronic health record databases from large national and regional health insurers.

A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records.

Objective: Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods.

Methods: Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework.

Transparency, reproducibility, and replicability of pharmacoepidemiology studies in a distributed network environment.

Purpose: Our objective is to describe how the U.S. Food and Drug Administration (FDA)'s Sentinel System implements best practices to ensure trust in drug safety studies using real-world data from disparate sources.

Application of the U.S. Food and Drug Administration's Sentinel Routine Querying Tools to the Taiwan Sentinel Data Model-formatted National Health Insurance Research Database.

The U.S. Food and Drug Administration's Sentinel System is a leading distributed data network for drug safety surveillance in the world.

smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.

Objectives: Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions.

Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease.

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.

Association Between Use of Fluoroquinolones and Risk of Mitral or Aortic Valve Regurgitation: A Nationwide Cohort Study.

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.