Aortic & peripheral vascular disease
Identifying Patients With Peripheral Artery Disease Using the Electronic Health Record: A Pragmatic Approach
Authors' conclusion
Statistical techniques can be used to build models which identify individuals at high risk for PAD using information accessible from the EHR.
Study context
- Population
- Patients with ankle-brachial indices (ABIs) in the electronic health record (EHR)
- Participants
- 3,298 patients
- Comparison
- Logistic regression model vs age-based model and random forest model
- Outcome
- Prediction of PAD status (AUC)
Methodological detailsmachine learning, external validation
- Machine learning
- External validation
Performance
As reported in the abstract
Prediction of PAD status
Model (validation set)
0.68
AUC
Model (external EHR population)
0.68
AUC
Age-based model
0.62
AUC
Random forest model
0.71
AUC
Model vs Age-based model: P < 0.001 · Random forest model vs Model: P = 0.31
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Identifying Patients With Peripheral Artery Disease Using the Electronic Health Record: A Pragmatic Approach. JACC Adv. Published 24 August 2023. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →