Clinical prediction rule

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This editable Main Article is under development and subject to a disclaimer.

[edit intro]

A clinical prediction rule is type of medical research study in which researchers try to identify the best combination of medical signs, symptoms, and other findings in predicting the probability of a specific disease or outcome.[1] Prediction rules may offer a more practical alternative to Bayesian reasoning.[2]

Physicians have difficulty in estimated risks of diseases; frequently erring towards overestimation[3], perhaps due to cognitive biases such as base rate fallacy in which the risk of an adverse outcome is exaggerated.

## Contents

* 1 Methods
* 2 Effect on health outcomes
* 2.1 Changing decisions of health care providers
* 2.2 Changing decisions of patients
* 3 Adverse effects
* 4 Examples of prediction rules
* 5 References

## Methods[edit]

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.

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]

## Effect on health outcomes[edit]

Using clinical prediction rules for "Teaching physicians to make better judgments of disease probability may not alter their treatment decisions." [5]

### Changing decisions of health care providers[edit]

Few prediction rules have had the consequences of their usage by physicians quantified.[6]

When studied, the impact of providing the information alone (for example, providing the calculated probability of disease) has been negative.[7][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.[8]

The more intensively the prediction rule is implemented the more benefit will occur.[9]

### Changing decisions of patients[edit]

For more information, see: Patient participation.

A randomized controlled trial of patients at very high risk of coronary events found that use of two equations (http://www.chiprehab.com/CVD/) for predicting coronary events along with tailored feedback, may improve cholesterol values.[10]

## Adverse effects[edit]

Patients may not be comfortable with the way a doctor may integrate decision support into practice.[11]

## Examples of prediction rules[edit]

* 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)

## References[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. PMID 10872017. [e]
2. ↑ Feinstein AR (1994). ""Clinical Judgment" revisited: the distraction of quantitative models". Ann. Intern. Med. 120 (9): 799–805. PMID 8147553. [e]
3. ↑ 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. [e]
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. PMID 22629234. Research Blogging.
5. ↑ 5.0 5.1 Poses RM, Cebul RD, Wigton RS (1995). "You can lead a horse to water--improving physicians' knowledge of probabilities may not affect their decisions.". Med Decis Making 15 (1): 65-75. PMID 7898300. [e]
6. ↑ 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. [e]
7. ↑ 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. [e]
8. ↑ 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. [e]
9. ↑ 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. [e]
10. ↑ Steven A. Grover et al., “Patient Knowledge of Coronary Risk Profile Improves the Effectiveness of Dyslipidemia Therapy: The CHECK-UP Study: A Randomized Controlled Trial,” Arch Intern Med 167, no. 21 (November 26, 2007), http://archinte.ama-assn.org/cgi/content/abstract/167/21/2296 (accessed November 27, 2007).
11. ↑ Shaffer VA, Probst CA, Merkle EC, Arkes HR, Medow MA (2013). "Why do patients derogate physicians who use a computer-based diagnostic support system?". Med Decis Making 33 (1): 108-18. DOI:10.1177/0272989X12453501. PMID 22820049. Research Blogging.