Clinical prediction rule

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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

Cardiovascular disease

Rules predicting the probability of a disease.
Risk Score Purpose
: Acute MI/unstable angina
TIMI risk score Unstable Angina
TIMI risk score STEMI
Morrow et al (TIME II substudy) [8] Predicting mortality in STEMI
Silver et al [9] To predict preserved left ventricular ejection fraction in patients after MI
The GRACE risk score For risk of death in NSTEMI
ACI-TIPI risk score [10] To assess the likelihood of patients having acute cardiac ischemia and thus the appropriateness of admitting them to the coronary care unit (CCU).
Goldman algorithm [11] To predict the likelihood that a patient has a myocardial infarction.
Hathaway et al [12] Prognosis based on initial ECG
Selker et al [13] Risk of death in AMI
Piombo et al [14] To stratify risk in unstable angina.
Solomon et al [15] To identify which patients benefit from immediate coronary angiography and revascularization.
: Stroke, Atrial fibrillation
CHADS2 Risk of stroke with AFIB
HAS-BLED Bleeding risk stratification score for those on oral anticoagulants in atrial fibrillation.
Wang et al [16] Framingham Heart Study [2] Stroke after Atrial fibrillation
Wang et al [16] Framingham Heart Study [3] Stroke or death after Atrial fibrillation.
Schnabel et al [17] Framingham Heart Study [4] 10 years risk of Atrial fibrillation
D'Agostino et al [18] Framingham Heart Study [5] Risk of stroke
: DVT & PE
Wells score Pulmonary embolism
: Hypertension, Lipid & Screening
Parikh et al [19] Framingham Heart Study [6] Hypertension Risk Score
Murabito et al [20] Framingham Heart Study [7] Intermittent claudication
: Chest pain and CAD
Vancouver chest pain rule [21] For early discharge of patients with chest pain.
North American Chest Pain Rule [22] For early discharge of patients with chest pain.
Pencina et al [23] Framingham Heart Study [8] 30-year risk of cardiovascular disease.
Wilson et al [24] Framingham Heart Study [9] 10-year risk of coronary heart disease.
: Angioplasty
Fortescue et al [25] Angioplasty complication rate
Resnic et al [26] Angioplasty complication rate
Singh et al [27] Angioplasty complication rate
Moscucci et al [28] Mortality after angioplasty.
Halkin et al [29] Angioplasty complication rate

Other organ system

Risk Score Purpose
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 [10]
Ottawa ankle rules To decide for offering Xray to patient with foot or ankle pain.
Rules predicting complications in diseased patients
Risk Score Purpose
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 [30] To calculate pulmonary complications after thoracic surgery for primary Lung Cancer

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. 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. 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.
  8. Morrow DA, Antman EM, Giugliano RP, Cairns R, Charlesworth A, Murphy SA, de Lemos JA, McCabe CH, Braunwald E (2001). "A simple risk index for rapid initial triage of patients with ST-elevation myocardial infarction: an InTIME II substudy". Lancet. 358 (9293): 1571–5. doi:10.1016/S0140-6736(01)06649-1. PMID 11716882. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  9. Silver MT, Rose GA, Paul SD, O'Donnell CJ, O'Gara PT, Eagle KA (1994). "A clinical rule to predict preserved left ventricular ejection fraction in patients after myocardial infarction". Ann. Intern. Med. 121 (10): 750–6. PMID 7944052. Unknown parameter |month= ignored (help); |access-date= requires |url= (help)
  10. Selker HP, Griffith JL, D'Agostino RB (1991). "A tool for judging coronary care unit admission appropriateness, valid for both real-time and retrospective use. A time-insensitive predictive instrument (TIPI) for acute cardiac ischemia: a multicenter study". Med Care. 29 (7): 610–27. PMID 2072767. Unknown parameter |month= ignored (help); |access-date= requires |url= (help)
  11. Goldman L, Cook EF, Brand DA, Lee TH, Rouan GW, Weisberg MC, Acampora D, Stasiulewicz C, Walshon J, Terranova G (1988). "A computer protocol to predict myocardial infarction in emergency department patients with chest pain". N. Engl. J. Med. 318 (13): 797–803. doi:10.1056/NEJM198803313181301. PMID 3280998. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  12. Hathaway WR, Peterson ED, Wagner GS, Granger CB, Zabel KM, Pieper KS, Clark KA, Woodlief LH, Califf RM (1998). "Prognostic significance of the initial electrocardiogram in patients with acute myocardial infarction. GUSTO-I Investigators. Global Utilization of Streptokinase and t-PA for Occluded Coronary Arteries". JAMA. 279 (5): 387–91. PMID 9459474. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  13. Selker HP, Griffith JL, D'Agostino RB (1991). "A time-insensitive predictive instrument for acute myocardial infarction mortality: a multicenter study". Med Care. 29 (12): 1196–211. PMID 1745078. Unknown parameter |month= ignored (help); |access-date= requires |url= (help)
  14. Piombo AC, Gagliardi JA, Guetta J, Fuselli J, Salzberg S, Fairman E, Bertolasi C (2003). "A new scoring system to stratify risk in unstable angina". BMC Cardiovasc Disord. 3: 8. doi:10.1186/1471-2261-3-8. PMC 194644. PMID 12930562. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  15. Solomon DH, Stone PH, Glynn RJ, Ganz DA, Gibson CM, Tracy R, Avorn J (2001). "Use of risk stratification to identify patients with unstable angina likeliest to benefit from an invasive versus conservative management strategy". J. Am. Coll. Cardiol. 38 (4): 969–76. PMID 11583866. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  16. 16.0 16.1 Wang TJ, Massaro JM, Levy D, Vasan RS, Wolf PA, D'Agostino RB, Larson MG, Kannel WB, Benjamin EJ (2003). "A risk score for predicting stroke or death in individuals with new-onset atrial fibrillation in the community: the Framingham Heart Study". JAMA. 290 (8): 1049–56. doi:10.1001/jama.290.8.1049. PMID 12941677. Retrieved 2012-05-14. Unknown parameter |month= ignored (help)
  17. 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)
  18. D'Agostino RB, Wolf PA, Belanger AJ, Kannel WB (1994). "Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study". Stroke. 25 (1): 40–3. PMID 8266381. Unknown parameter |month= ignored (help); |access-date= requires |url= (help)
  19. Parikh NI, Pencina MJ, Wang TJ, Benjamin EJ, Lanier KJ, Levy D, D'Agostino RB, Kannel WB, Vasan RS (2008). "A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study". Ann. Intern. Med. 148 (2): 102–10. PMID 18195335. Unknown parameter |month= ignored (help); |access-date= requires |url= (help)
  20. Murabito JM, D'Agostino RB, Silbershatz H, Wilson WF (1997). "Intermittent claudication. A risk profile from The Framingham Heart Study". Circulation. 96 (1): 44–9. PMID 9236415. Retrieved 2012-05-14. Unknown parameter |month= ignored (help)
  21. Christenson J, Innes G, McKnight D, Thompson CR, Wong H, Yu E, Boychuk B, Grafstein E, Rosenberg F, Gin K, Anis A, Singer J (2006). "A clinical prediction rule for early discharge of patients with chest pain". Ann Emerg Med. 47 (1): 1–10. doi:10.1016/j.annemergmed.2005.08.007. PMID 16387209. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  22. Hess EP, Wells GA, Jaffe A, Stiell IG (2008). "A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology". BMC Emerg Med. 8: 3. doi:10.1186/1471-227X-8-3. PMC 2275746. PMID 18254973. Retrieved 2012-05-15.
  23. 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)
  24. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB (1998). "Prediction of coronary heart disease using risk factor categories". Circulation. 97 (18): 1837–47. PMID 9603539. Retrieved 2012-05-14. Unknown parameter |month= ignored (help)
  25. Fortescue EB, Kahn K, Bates DW (2003). "Major adverse outcomes after percutaneous transluminal coronary angioplasty: a clinical prediction rule". J Clin Epidemiol. 56 (1): 17–27. PMID 12589866. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  26. Resnic FS, Ohno-Machado L, Selwyn A, Simon DI, Popma JJ (2001). "Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention". Am. J. Cardiol. 88 (1): 5–9. PMID 11423050. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  27. Singh M, Lennon RJ, Holmes DR, Bell MR, Rihal CS (2002). "Correlates of procedural complications and a simple integer risk score for percutaneous coronary intervention". J. Am. Coll. Cardiol. 40 (3): 387–93. PMID 12142101. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  28. Moscucci M, Kline-Rogers E, Share D, O'Donnell M, Maxwell-Eward A, Meengs WL, Kraft P, DeFranco AC, Chambers JL, Patel K, McGinnity JG, Eagle KA (2001). "Simple bedside additive tool for prediction of in-hospital mortality after percutaneous coronary interventions". Circulation. 104 (3): 263–8. PMID 11457742. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  29. Halkin A, Singh M, Nikolsky E, Grines CL, Tcheng JE, Garcia E, Cox DA, Turco M, Stuckey TD, Na Y, Lansky AJ, Gersh BJ, O'Neill WW, Mehran R, Stone GW (2005). "Prediction of mortality after primary percutaneous coronary intervention for acute myocardial infarction: the CADILLAC risk score". J. Am. Coll. Cardiol. 45 (9): 1397–405. doi:10.1016/j.jacc.2005.01.041. PMID 15862409. Retrieved 2012-05-15. Unknown parameter |month= ignored (help)
  30. 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)

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