Real Data Applications of Learning Curves In Cardiac Devices and Procedures

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.

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). Within the actual datasets, we looked at two device types and also two procedure types which had not been observed before: off pump coronary artery bypass (CABG) experience, and radial access experience. We also tried mediation analyses within the GEE framework for these various devices/procedures as well.

We found that the choice of shape used to produce the "learning-free" dataset would still be dataset specific depending upon needs for modeling fast or slow learning but that in general the power series or logarithmic shapes would be better for modeling slower learning while exponential may be better for faster learning. Mediation analysis also showed promise in adapting the modeling of the learning curve.

In showing the flexibility of using our method in various applications; this time utilizing more than one possible procedure done per patient so that each physician had more volume, we were able to show the flexibility of applying our method in different data applications to allow for more accurately capturing the learning curve rates in physicians nested within institutions. This can, therefore, be used across the board for device and procedure safety.