Clinical prediction rule

A clinical prediction rule is type of medical research study in which researchers try to identify the best combination of medical sign, symptoms, and other findings in predicting the probability of a specific disease or outcome.

Physicians have difficulty in estimated risks of diseases; frequently erring towards overestimation, perhaps due to cognitive biases such as base rate fallacy in which the risk of an adverse outcome is exaggerated.

Methods
In a prediction rule study, investigators identify a consecutive group of patients who are suspected of a having a specific disease or outcome. The investigators then compare the value of clinical findings available to the physician versus the results of more intensive testing or the results of delayed clinical follow up.

Effect on health outcomes
Few prediction rules have had the consequences of their usage by physicians quantified.

When studied, the impact of providing the information alone (for example, providing the calculated probability of disease) has been negative.

However, when the prediction rule is implemented as part of a critical pathway, so that a hospital or clinic has procedures and policies established for how to manage patients identified as high or low risk of disease, the prediction rule has more impact on clinical outcomes.

The more intensively the prediction rule is implemented the more benefit will occur.

Examples of prediction rules

 * Apache II
 * CHADS2 for risk of stroke with AFIB
 * CURB-65
 * Model for End-Stage Liver Disease
 * Ranson criteria
 * Pneumonia severity index
 * Wells score (disambiguation)