Using clinical risk models to predict outcomes: what are we predicting and why?

Area of Science:

• Emergency Medicine
• Clinical Epidemiology
• Biostatistics

Background:

• Clinical risk prediction models are crucial for emergency medicine decision-making.
• Current models may inaccurately direct interventions towards high-risk patients.
• The influence of treatment effects on model development is often overlooked.

Purpose of the Study:

• To examine the impact of treatment effects on prognostic clinical risk prediction models.
• To identify how treatment effects can lead to misclassification of patient benefit.
• To provide practical advice for the appropriate use of clinical prediction scores.

Main Methods:

• Conceptual analysis of prognostic model development.
• Examination of potential biases introduced by treatment effects.
• Review of literature on clinical prediction score application.

Main Results:

• Treatment effects can cause risk prediction models to identify patients unlikely to benefit from intervention.
• Models may fail to identify patients most likely to benefit, hindering effective resource allocation.
• Misinterpretation of model outputs can lead to suboptimal clinical decision-making.

Conclusions:

• Clinicians should be aware of the potential for treatment effects to bias risk prediction models.
• Clinical prediction scores should augment, not replace, clinical judgment.
• Careful consideration of model development and application is necessary to ensure patient benefit.