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.

Trends and comparative outcomes between operative approaches for segmentectomy in lung cancer.

Background: Segmentectomy is increasingly performed for non-small cell lung cancer. However, comparative outcomes data among open, robotic-assisted, and video-assisted thoracoscopic approaches are limited.

Methods: A retrospective cohort study of non-small cell lung cancer segmentectomy cases (2013-2021) from the Society of Thoracic Surgeons General Thoracic Surgery Database was performed.

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.

Real-World Safety Analysis of Paclitaxel Devices Used for the Treatment of Peripheral Artery Disease.

Purpose: To investigate the real-world safety of paclitaxel (PTX)-coated devices for treating lower extremity peripheral artery disease using a commercial claims database.

Materials And Methods: Data from FAIR Health, the largest commercial claims data warehouse in the United States, were used for this study. The study consisted of patients who underwent femoropopliteal revascularization procedures between January 1, 2015, and December 31, 2019, with PTX and non-PTX devices.

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.

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.

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.

Assessing the cost burden of United States FDA-mandated post-approval studies for medical devices.

Approved medical devices frequently undergo FDA mandated post-approval studies (PAS). However, there is uncertainty as to the value of PAS in assessing the safety of medical devices and the cost of these studies to the healthcare system is unknown. Since PAS costs are funded through device manufacturers who do not share the costs with regulators, we sought to estimate the total PAS costs through interviews with a panel of experts in medical device clinical trial design in order to design a general cost model for PAS which was then applied to the FDA PAS.

Registry-Based Prospective, Active Surveillance of Medical-Device Safety.

Background: The process of assuring the safety of medical devices is constrained by reliance on voluntary reporting of adverse events. We evaluated a strategy of prospective, active surveillance of a national clinical registry to monitor the safety of an implantable vascular-closure device that had a suspected association with increased adverse events after percutaneous coronary intervention (PCI).

Methods: We used an integrated clinical-data surveillance system to conduct a prospective, propensity-matched analysis of the safety of the Mynx vascular-closure device, as compared with alternative approved vascular-closure devices, with data from the CathPCI Registry of the National Cardiovascular Data Registry.