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Decision curve analysis to evaluate the clinical benefit of prediction models

Open access
Vickers, Andrew J., Holland, Ford·2021·The Spine Journal

Abstract

There is increased interest in the use of prediction models to guide clinical decision-making in orthopedics. Prediction models are typically evaluated in terms of their accuracy: discrimination (area-under-the-curve [AUC] or concordance index) and calibration (a plot of predicted vs. observed risk). But it can be hard to know how high an AUC has to be in order to be “high enough” to warrant use of a prediction model, or how much miscalibration would be disqualifying. Decision curve analysis was developed as a method to determine whether use of a prediction model in the clinic to inform decision-making would do more good than harm. Here we give a brief introduction to decision curve analysis, explaining the critical concepts of net benefit and threshold probability. We briefly review some prediction models reported in the orthopedic literature, demonstrating how use of decision curves has allowed conclusions as to the clinical value of a prediction model. Conversely, papers without decision curves were unable to address questions of clinical value. We recommend increased use of decision curve analysis to evaluate prediction models in the orthopedics literature.

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Metadata

Authors
Vickers, A. J., & Holland, F.
Venue
The Spine Journal
Publisher
Elsevier BV
Published
2021-10
Volume
21
Issue
10
Pages
1643-1648
ISSN
1529-9430 (ISSN-L)
OpenAlex
W3135278199
Cited by
394
References
21
FWCI
34.39
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Decision curve analysis to evaluate the clinical benefit of prediction models. https://app.cochat.ai/papers/10.1016/j.spinee.2021.02.024
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Vickers, A. J., & Holland, F. (2021). Decision curve analysis to evaluate the clinical benefit of prediction models. The Spine Journal. https://doi.org/10.1016/j.spinee.2021.02.024

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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