An introduction to clinical prediction models using logistic regression in acute care surgery research: Methodologic considerations and common pitfalls.

Abstract

Clinical prediction models can enhance timely clinical decision-making when appropriately developed and integrated within clinical workflows. A risk prediction model is typically a regression equation that uses patient risk factor data to estimate the probability of the presence of disease (diagnostic) or its future occurrence (prognostic). Risk prediction models are widely studied in the surgical literature and commonly developed using logistic regression. For a risk prediction model to be useful, it must balance statistical performance and clinical usefulness. This article provides a brief overview of the various methodologic issues to consider when developing or validating a risk prediction model and common pitfalls.