Cardinality matching versus propensity score matching for addressing cluster-level residual confounding in implantable medical device and surgical epidemiology: a parametric and plasmode simulation study.

Background: Rapid innovation and new regulations lead to an increased need for post-marketing surveillance of implantable devices. However, complex multi-level confounding related not only to patient-level but also to surgeon or hospital covariates hampers observational studies of risks and benefits. We conducted parametric and plasmode simulations to compare the performance of cardinality matching (CM) vs propensity score matching (PSM) to reduce confounding bias in the presence of cluster-level confounding.

A Comprehensive Framework for Evaluating the Value Created by Real-World Evidence for Diverse Stakeholders: The Case for Coordinated Registry Networks.

Objectives: This manuscript presents a comprehensive framework for the assessment of the value of real-world evidence (RWE) in healthcare decision-making. While RWE has been proposed to overcome some limitations of traditional, one-off studies, no systematic framework exists to measure if RWE actually lowers the burden. This framework aims to fill that gap by providing conceptual approaches for evaluating the time and cost efficiencies of RWE, thus guiding strategic investments in RWE infrastructure.

Persistent racial disparities in postoperative management after tibia fracture fixation: A matched analysis of US medicaid beneficiaries.

Introduction: Racial and ethnic disparities in orthopaedic surgery are well documented. However, the extent to which these persist in fracture care is unknown. This study sought to assess racial disparities in the postoperative surgical and medical management of patients after diaphyseal tibia fracture fixation.

Does radiofrequency ablation procedural data improve the accuracy of identifying atrial fibrillation recurrence?

Radiofrequency ablation (RFA) using the CARTO 3D mapping system is a common approach for pulmonary vein isolation to treat atrial fibrillation (AF). Linkage between CARTO procedural data and patients' electronical health records (EHR) provides an opportunity to identify the ablation-related parameters that would predict AF recurrence. The objective of this study is to assess the incremental accuracy of RFA procedural data to predict post-ablation AF recurrence using machine learning model.

Predictive utility of the impedance drop on AF recurrence using digital intraprocedural data linked to electronic health record data.

Background: Local impedance drop in cardiac tissue during catheter ablation may be a valuable measure to guide atrial fibrillation (AF) ablation procedures for greater effectiveness.

Objective: The study sought to assess whether local impedance drop during catheter ablation to treat AF predicts 1-year AF recurrence and what threshold of impedance drop is most predictive.

Methods: We identified patients with AF undergoing catheter ablation in the Mercy healthcare system.