# Incorporating Learning Effects into Medical Device Active Safety Surveillance Methods

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2020 · $828,534

## Abstract

Implantable medical devices have revolutionized contemporary cardiovascular care, and
are used in a wide spectrum of acute and chronic cardiovascular conditions. However, medical
device design fault or incorrect use may lead to significant risk of patient injury and represents
an important preventable public health risk in the United States. To help identify device-related
safety issues, a strategy of active, prospective, post-market safety surveillance has been
recommended by the FDA, and evaluated methodologically. This type of surveillance offers
significant advantages over traditional adverse event reporting strategies. However, all such
approaches are challenged by the need to incorporate learning effects into expectations
regarding safety. These learning impacts been repeatedly shown to have dramatic impacts on
outcomes during early device experience. Quantifying learning effects on the outcomes
associated with high-risk cardiovascular devices will improve our understanding of intrinsic
device performance, thereby identifying patient populations best treated with such devices while
simultaneously providing necessary feedback to device manufacturers to support iterative
improvement in device design. Separately, understanding the impacts of learning may identify
opportunities for targeted training as well as help to tease apart institutional and operator
characteristics that may accelerate the achievement of optimal outcomes in the use of the
specific cardiovascular device.
 This proposal seeks to extend the previously validated, open-source, active, prospective
device safety surveillance tool, by developing and validating robust learning curve (LC)
detection and quantification algorithms, designed to simultaneously account for the effects at
the operator and institutional levels. We propose a “blinded” development strategy, in which
one team will generate robust synthetic clinical data simulator with LC impacts, and the other
team develops and applies LC detection and quantification algorithms, without knowledge of the
underlying relationships, determine performance and accuracy through sequential refinement
and validation steps. We propose to formally validate the optimized LC tools in real-world data
through re-analysis of previously published LC effects on transcatheter valves and vascular
closure devices using national cardiovascular registries. In addition, the LC tools will be
incorporated into two active, prospective device safety surveillance studies of novel implantable
cardiovascular devices using large clinical registries.

## Key facts

- **NIH application ID:** 9863048
- **Project number:** 1R01HL149948-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** MICHAEL E. MATHENY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $828,534
- **Award type:** 1
- **Project period:** 2020-02-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9863048

## Citation

> US National Institutes of Health, RePORTER application 9863048, Incorporating Learning Effects into Medical Device Active Safety Surveillance Methods (1R01HL149948-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9863048. Licensed CC0.

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