A machine learning framework to adjust for learning effects in medical device safety evaluation.

Objectives: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects.

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.

Spontaneous Migration of Ventriculoperitoneal Shunt Into the Right Heart and Pulmonary Vasculature.

A 73-year-old woman with ventriculoperitoneal (VP) shunt presented for stress echocardiogram for evaluation of chest pain. Transthoracic echocardiogram revealed an incidental right heart mass representing a migrated VP shunt. This case highlights the role of multimodality cardiac imaging in diagnosing right heart masses and the multidisciplinary approach to management.

Testing a Cloud-Based Model for Active Surveillance of Medical Devices with Analyses of Coronary Stent Safety Using the Data Extraction and Longitudinal Trend Analysis (DELTA) System.

Objective: To demonstrate the use of the Data Extraction and Longitudinal Trend Analysis (DELTA) system in the National Evaluation System for health Technology's (NEST) medical device surveillance cloud environment by analyzing coronary stent safety using real world clinical data and comparing results to clinical trial findings.

Design And Setting: Electronic health record (EHR) data from two health systems, the Social Security Death Master File, and device databases were ingested into the NEST cloud, and safety analyses of two stents were performed using DELTA.

Participants And Interventions: This is an observational study of patients receiving zotarolimus drug-eluting coronary stents (ZES) or everolimus eluting coronary stents (EES) between July 1, 2015 and December 31, 2017.

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

Background: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships.

Advancing the Real-World Evidence for Medical Devices through Coordinated Registry Networks.

Objectives: Generating and using real-world evidence (RWE) is a pragmatic solution for evaluating health technologies. RWE is recognized by regulators, health technology assessors, clinicians, and manufacturers as a valid source of information to support their decision-making. Well-designed registries can provide RWE and become more powerful when linked with electronic health records and administrative databases in coordinated registry networks (CRNs).

Evaluation of intervertebral body implant performance using active surveillance of electronic health records.

Objectives: To assess the feasibility of using electronic health record (EHR) derived clinical data within an active surveillance setting to evaluate the safety of a novel intervertebral body implant (IVBI) stabilization device.

Design: Retrospective, longitudinal observational cohort study comparing clinical outcomes for patients seen through 1 year following spinal fusion surgery.

Setting: Lahey Health network, which includes academic tertiary hospitals, outpatient clinics, and independent provider offices in the New England region of the USA.

Vascular Quality Initiative Surveillance of Femoropopliteal Artery Paclitaxel Devices.

Objectives: This study sought to determine the safety and efficacy of paclitaxel (PTX) devices in the treatment of peripheral artery disease involving the femoropopliteal artery.

Background: A meta-analysis of PTX devices for the treatment of femoropopliteal artery disease reported a mortality signal.

Methods: This was a multicenter cohort study using an integrated clinical data surveillance system to conduct a prospective, propensity score-matched survival analysis of 2,456 patients in the Society for Vascular Surgery Vascular Quality Initiative from January 2017 to May 2020.

Development of a Novel Communication Liaison Program to Support COVID-19 Patients and Their Families.

In the spring of 2020, our hospital faced a surge of critically ill coronavirus disease 2019 patients, with intensive care unit (ICU) occupancy peaking at 204% of the baseline maximum capacity. In anticipation of this surge, we developed a remote communication liaison program to help the ICU and palliative care teams support families of critically ill patients. In just nine days from inception until implementation, we recruited and prepared ambulatory specialty providers to serve in this role effectively, despite minimal prior critical care experience.

Impact of early surveillance on safety signal identification in the CathPCI DELTA study.

Objectives: The CathPCI Data Extraction and Longitudinal Trend Analysis study was designed to determine the feasibility of conducting prospective surveillance of a large national registry to perform comparative safety analyses of medical devices. We sought to determine whether the complementary use of retrospective case data could improve safety signal detection time.

Design: We performed a simulated surveillance study of the comparative safety of the Mynx vascular closure device (VCD) with propensity score matched alternate VCD recipients, using both retrospective and prospective cohort data.

Outcomes of COVID-19 in Patients With a History of Cancer and Comorbid Cardiovascular Disease.

Background: Cancer and cardiovascular disease (CVD) are independently associated with adverse outcomes in patients with COVID-19. However, outcomes in patients with COVID-19 with both cancer and comorbid CVD are unknown.

Methods: This retrospective study included 2,476 patients who tested positive for SARS-CoV-2 at 4 Massachusetts hospitals between March 11 and May 21, 2020.

Use of data from the Vascular Quality Initiative registry to support regulatory decisions yielded a high return on investment.

Background: Real-world data (RWD) from the Society for Vascular Surgery Vascular Quality Initiative (VQI) registry has been used to support US Food and Drug Administration (FDA) regulatory decisions regarding vascular devices. The variables of cost and time needed for these registry-based studies have not been previously compared to traditional, independent, industry studies that would otherwise have been conducted to support regulatory decisions.

Objectives: To determine the potential value (cost and time saving and return on investment) created by device evaluation studies using the VQI registry infrastructure.

Management of Cardiovascular Disease During Coronavirus Disease (COVID-19) Pandemic.

Patients with pre-existing cardiovascular disease and risk factors are more likely to experience adverse outcomes associated with the novel coronavirus disease-2019 (COVID-19). Additionally, consistent reports of cardiac injury and de novo cardiac complications, including possible myocarditis, arrhythmia, and heart failure in patients without prior cardiovascular disease or significant risk factors, are emerging, possibly due to an accentuated host immune response and cytokine release syndrome. As the spread of the virus increases exponentially, many patients will require medical care either for COVID-19 related or traditional cardiovascular issues.

Active Surveillance of the Implantable Cardioverter-Defibrillator Registry for Defibrillator Lead Failures.

Background: Several defibrillator leads have been recalled due to early lead failure leading to significant patient harm. Confirming the safety of contemporary defibrillator leads is essential to optimizing treatment for patients receiving implantable cardioverter-defibrillators (ICDs). We therefore sought to assess the comparative long-term safety of the 4 most commonly implanted ICD leads within the National Cardiovascular Data Registry ICD Registry.

Comparative Safety of Aspiration Thrombectomy Catheters Utilizing Prospective, Active Surveillance of the NCDR CathPCI Registry.

Background Current strategies for ensuring the postmarket safety of medical devices are limited by small sample size and reliance on voluntary reporting of adverse events. Prospective, active surveillance of clinical registries may provide early warnings in the postmarket evaluation of medical device safety but has not been demonstrated in national clinical data registries. Methods and Results The CathPCI DELTA (Data Extraction and Longitudinal Trend Analysis) study was designed to assess the feasibility of prospective, active safety surveillance of medical devices within a national cardiovascular registry.

Challenges in outlier surgeon assessment in the era of public reporting.

Objective: To assess the effect of various evaluation and reporting strategies in determining outlier surgeons, defined by having worse-than-expected mortality after cardiac surgery.

Methods: Our study included 33 394 isolated coronary artery bypass graft (CABG) procedures performed by 136 surgeons and 12 172 surgical aortic valve replacement (SAVR) procedures performed by 113 surgeons between 2010 and 2014. Three current methodologies based on the framework of comparing observed and expected (O/E ratio) mortality, with different distributional assumptions, were examined.

Real Data Applications of Learning Curves In Cardiac Devices and Procedures.

Background: In the use of medical device procedures, learning effects have been shown to have a significant impact on the outcome, and are a critical component of medical device safety surveillance. To support estimation of these effects, we evaluated our methods for modeling these rates within several different actual datasets representing patients treated by physicians clustered within institutions to show the flexibility of this method across applications.

Methods: In order to estimate the learning curve effects, we employed our unique modeling for the learning curves to incorporate the learning hierarchy between institution and physicians, and then modeled them within established methods that work with hierarchical data such as generalized estimating equations (GEE).