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.^[1]

Physicians have difficulty in estimated risks of diseases; frequently erring towards overestimation^[2], 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. It may involve, among other things, estimation of the clinical utility of diagnostic tests.

Effect on health outcomes

Few prediction rules have had the consequences of their usage by physicians quantified.^[3]

When studied, the impact of providing the information alone (for example, providing the calculated probability of disease) has been negative.^[4][5]

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.^[6]

The more intensively the prediction rule is implemented the more benefit will occur.^[7]

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)
• Ottawa ankle rules
• Pittsburgh knee rules

References

1. ^ McGinn TG, Guyatt GH, Wyer PC, Naylor CD, Stiell IG, Richardson WS (2000). "Users' guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group". JAMA 284 (1): 79–84. doi:10.1001/jama.284.1.79. PMID 10872017. 
2. ^ Friedmann PD, Brett AS, Mayo-Smith MF (1996). "Differences in generalists' and cardiologists' perceptions of cardiovascular risk and the outcomes of preventive therapy in cardiovascular disease". Ann. Intern. Med. 124 (4): 414–21. PMID 8554250. 
3. ^ Reilly BM, Evans AT (2006). "Translating clinical research into clinical practice: impact of using prediction rules to make decisions". Ann. Intern. Med. 144 (3): 201–9. PMID 16461965. 
4. ^ Lee TH, Pearson SD, Johnson PA, et al. (1995). "Failure of information as an intervention to modify clinical management. A time-series trial in patients with acute chest pain". Ann. Intern. Med. 122 (6): 434–7. PMID 7856992. 
5. ^ Poses RM, Cebul RD, Wigton RS (1995). "You can lead a horse to water--improving physicians' knowledge of probabilities may not affect their decisions". Medical decision making : an international journal of the Society for Medical Decision Making 15 (1): 65–75. doi:10.1177/0272989X9501500110. PMID 7898300. 
6. ^ Marrie TJ, Lau CY, Wheeler SL, Wong CJ, Vandervoort MK, Feagan BG (2000). "A controlled trial of a critical pathway for treatment of community-acquired pneumonia. CAPITAL Study Investigators. Community-Acquired Pneumonia Intervention Trial Assessing Levofloxacin". JAMA 283 (6): 749–55. PMID 10683053. 
7. ^ Yealy DM, Auble TE, Stone RA, et al. (2005). "Effect of increasing the intensity of implementing pneumonia guidelines: a randomized, controlled trial". Ann. Intern. Med. 143 (12): 881–94. PMID 16365469.