Clinical prediction rule
A clinical prediction rule is a 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.
In a prediction rule study, investigators identify a consecutive group of patients who are suspected of 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. It may involve, among other things, estimation of the clinical utility of diagnostic tests.
A survey of methods concluded "the majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability", confirming earlier findings from the diabetic literature
Effect on health outcomes
Few prediction rules have had the consequences of their usage by physicians quantified.
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
- Model for End-Stage Liver Disease
- Ranson criteria
- Centor criteria
- Pneumonia severity index
- Wells score (disambiguation)
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