Clinical prediction rule
Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1]
Overview
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.
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
| Rules predicting the probability of a disease. | |
|---|---|
| Risk Score | Purpose |
| Cardiovascular diseases | |
| TIMI risk score | Unstable Angina |
| TIMI risk score | STEMI |
| CHADS2 | Risk of stroke with AFIB |
| Wells score | Pulmonary embolism |
| Schnabel et al (Framingham Heart Study) [8] | Atrial fibrillation |
| Pencina et al (Framingham Heart Study) [9] | 30-year risk of cardiovascular disease. |
| Gastroentestinal diseases | |
| Ranson criteria | To predict the severity of acute pancreatitis. |
| Tygerberg score | To diffrentiate tuberculosis as a cause of pericarditis. |
| Orthopedic diseases | |
| QFracture score | Osteoporosis [2] |
| Ottawa ankle rules | To decide for offering Xray to patient with foot or ankle pain. |
| Rules predicting complications in diseased patients | |
| Pneumonia severity index | To calculate the probability of morbidityand mortality among patients with community acquired pneumonia. |
| CURB-65 | To predict mortality in community-acquired pneumonia. |
| MELD | To assess the severity of chronic liver disease. |
| Apnea-hypopnea index | To assess the overall severity of sleep apnea. |
| Amar et al [10] | To calculate pulmonary complications after thoracic surgery for primary Lung Cancer |
- Various rules are used in the Intensive Care Unit and are grouped as ICU scoring systems (Glasgow coma scale, Pediatric Glasgow Coma Scale, APACHE II, SAPS II, PIM2).
References
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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. PMID 7898300.
- ↑ 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.
- ↑ 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.
- ↑ Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB, Newton-Cheh C, Yamamoto JF, Magnani JW, Tadros TM, Kannel WB, Wang TJ, Ellinor PT, Wolf PA, Vasan RS, Benjamin EJ (2009). "Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study". Lancet. 373 (9665): 739–45. doi:10.1016/S0140-6736(09)60443-8. PMC 2764235. PMID 19249635. Retrieved 2012-05-14. Unknown parameter
|month=ignored (help) - ↑ Pencina MJ, D'Agostino RB, Larson MG, Massaro JM, Vasan RS (2009). "Predicting the 30-year risk of cardiovascular disease: the framingham heart study". Circulation. 119 (24): 3078–84. doi:10.1161/CIRCULATIONAHA.108.816694. PMC 2748236. PMID 19506114. Retrieved 2012-05-14. Unknown parameter
|month=ignored (help) - ↑ Amar D, Munoz D, Shi W, Zhang H, Thaler HT (2010). "A clinical prediction rule for pulmonary complications after thoracic surgery for primary lung cancer". Anesth. Analg. 110 (5): 1343–8. doi:10.1213/ANE.0b013e3181bf5c99. PMID 19861366. Retrieved 2012-05-14. Unknown parameter
|month=ignored (help)