Clinical prediction rule

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A clinical prediction rule or clinical probability assessment specifies how to use medical signs, symptoms, and other findings to estimate the probability of a specific disease or clinical 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.


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 obtain a standard set of clinical observations on each patient and a test or clinical follow-up to define the true state of the patient. They then use statistical methods to identify the best clinical predictors of the patient's true state. The probability of disease will depend on the patient's key clinical predictors. Published methodological standards specify good practices for developing a clinical prediction rule.[3]

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",[4] confirming earlier findings from the diabetic literature.[5] The TRIPOD statement is now widely used to improve the quality of reporting of clinical prediction rules,[6] with an extension to provide guidance for clinical prediction rules developed using artificial intelligence methods[7]

Effect on health outcomes[edit]

Few prediction rules have had the consequences of their usage by physicians quantified.[8]

When studied, the impact of providing the information alone (for example, providing the calculated probability of disease) has been negative.[9][10]

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.[11]

The more intensively the prediction rule is implemented the more benefit will occur.[12]

Examples of prediction rules[edit]


  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. doi:10.7326/0003-4819-124-4-199602150-00005. PMID 8554250. S2CID 25470460.
  3. ^ Laupacis, Andreas (1997). "Clinical prediction rules: a review and suggested modifications of methodological standards". Journal of the American Medical Association. 297: 488–494. doi:10.1001/jama.1997.03540300056034.
  4. ^ Bouwmeester W, Zuithoff NP, Mallett S, Geerlings MI, Vergouwe Y, Steyerberg EW, et al. (2012). "Reporting and methods in clinical prediction research: a systematic review". PLOS Med. 9 (5): e1001221. doi:10.1371/journal.pmed.1001221. PMC 3358324. PMID 22629234.
  5. ^ Collins GS, Mallett S, Omar O, Yu LM (2011). "Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting". BMC Med. 9: 103. doi:10.1186/1741-7015-9-103. PMC 3180398. PMID 21902820.
  6. ^ Collins GS, Reitsma HB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Ann Intern Med. 2015;162:55-63
  7. ^ Collins GS, Moons KGM, Dhiman P, Riley RD, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385: e078378
  8. ^ 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. doi:10.7326/0003-4819-144-3-200602070-00009. PMID 16461965. S2CID 32179950.
  9. ^ 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. doi:10.7326/0003-4819-122-6-199503150-00006. PMID 7856992. S2CID 35487553.
  10. ^ 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. 15 (1): 65–75. doi:10.1177/0272989X9501500110. PMID 7898300. S2CID 72016252.
  11. ^ 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. doi:10.1001/jama.283.6.749. PMID 10683053.
  12. ^ 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. doi:10.7326/0003-4819-143-12-200512200-00006. PMID 16365469. S2CID 45414192.

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