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Description

We inform an active clinical debate around routine perioperative testing for acute myocardial infarction using predictive analytics to examine troponin ordering and risk of an elevated result or in-hospital death in non-cardiac surgery. Our model better predicts elevated results and in-hospital mortality when compared to traditional risk stratification tools and illustrates that clinicians incorporate this and more information in their troponin ordering decisions. These results suggest routine testing would increase low value perioperative testing.

Learning Objective 1: After participating in this session, the learner should be better able to: Understand potential methods to study clinician decision making using various clinical data sources and predictive modeling.

Authors:

Victor Lei (Presenter)
University of Pennsylvania

Mark Neuman, University of Pennsylvania
Mark Weiner, Temple University
ThaiBinh Luong, University of Pennsylvania
Alex Bain, University of Pennsylvania
Daniel Polsky, University of Pennsylvania
Kevin Volpp, University of Pennsylvania
John Holmes, University of Pennsylvania
Amol Navathe, University of Pennsylvania

Presentation Materials:

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