Use of Registries, Claims and Health System Data to Enhance the Evaluation of Cardiovascular Devices

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PROJECT SUMMARY/ABSTRACT There is an urgent need to develop and implement more efficient approaches to evaluate cardiovascular devices in representative patient populations. Data from registries, health care systems, and payers, often include detailed clinical baseline information and longitudinal outcomes on a large number of patients representative of those cared for in clinical practice. However, the most commonly performed non- randomized evaluations using these data have high risk of bias due to a number of methodological challenges, including unmeasured differences between patients receiving different treatments (confounding) and misalignment of treatment eligibility, treatment initiation and beginning of follow up (selection bias). Thus, there is great enthusiasm for exploiting newer study design and analysis strategies that can more closely approximate the results of a desired but yet-to-be-performed randomized trial, while gaining the efficiency and representativeness of using data routinely collected in the course of patient care. Applying state-of-the-science methods to diverse and rich datasets may identify specific populations with different responses to device treatment - a key step in the ability to deliver individualized patient-centered care. In this renewal application, we will continue to pursue the overarching goal of developing innovative approaches to enhance the efficiency, fidelity and generalizability of cardiovascular device evaluation through the analysis of unique multidimensional linked datasets. In Aim 1, we will apply new methods to transport inferences about treatment effects from pivotal randomized trials of high-risk cardiovascular devices to new target populations representative of patients seen in contemporary practice. In Aim 2, we will evaluate the safety and effectiveness of high-risk cardiovascular devices through application of the target trial framework, a set of conceptual and practical tools for designing observational emulations of randomized trials that is well suited to overcome common forms of selection bias (e.g., immortal time bias) in cardiovascular device comparisons. In Aim 3, we will apply quasi-experimental methods to evaluate the safety and effectiveness of these devices, including instrumental variable and instrumented difference-in-difference designs. In each of the three aims, we will develop and apply methods to examine subgroups of patients under-represented in trials, specifically women, Black patients, and patients from socially disadvantaged communities. This research will inform the safety and effectiveness of several cardiovascular devices that have not been well studied, provide important clinical information to practicing physicians in the community, and create new standards for the future regulatory evaluation of medical devices using transportability, observational, and quasi-experimental approaches to complement standard randomized trials.