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Statistical Primer: developing and validating a risk prediction model
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DOI：10.1093/ejcts/ezy180.png)
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A risk prediction model is a mathematical equation that uses patient risk factor data to estimate the probability of a patient experiencing a healthcare outcome. Risk prediction models are widely studied in the cardiothoracic surgical literature with most developed using logistic regression. For a risk prediction model to be useful, it must have adequate discrimination, calibration, face validity and clinical usefulness. A basic understanding of the advantages and potential limitations of risk prediction models is vital before applying them in clinical practice. This article provides a brief overview for the clinician on the various issues to be considered when developing or validating a risk prediction model. An example of how to develop a simple model is also included.
Keyword:
Risk prediction
Calibration
EuroSCORE
EuroSCORE II
Receiver operating characteristic
Risk assessment
Surgical mortality
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