Evidence-Based Practice Lecture 4

2. The Culture of Health Care
Evidence-Based Practice
Lecture d
This material (Comp 2 Unit 5) was developed by Oregon Health & Science University, funded by the Department
of Health and Human Services, Office of the National Coordinator for Health Information Technology under
Award Number IU24OC000015. This material was updated in 2016 by Bellevue College under Award
Number 90WT0002.
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.

3. Evidence-Based Practice
Learning Objectives
• Define the key tenets of evidence-based medicine (EBM) and its
role in the culture of health care (Lectures a, b).
• Construct answerable clinical questions and critically appraise
evidence answering them (Lecture b).
• Explain how EBM can be applied to intervention studies, including
the phrasing of answerable questions, finding evidence to answer
them, and applying them to given clinical situations (Lecture c).
• Describe how EBM can be applied to key clinical questions of
diagnosis, harm, and prognosis (Lectures d, e).
• Discuss the benefits and limitations to summarizing evidence
(Lecture f).
• Describe how EBM is used in clinical settings through clinical
practice guidelines and decision analysis (Lecture g).
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4. Using EBM to Assess Questions
about Diagnosis
• Diagnostic process involves logical reasoning
and pattern recognition
• Consists of two essential steps:
– Enumerate diagnostic possibilities and estimate their
relative likelihood, generating differential diagnosis
– Incorporate new information from diagnostic tests to
change probabilities, rule out some possibilities, and
choose most likely diagnosis
• Two variations on diagnosis to be discussed:
– Screening
– Clinical prediction rules
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5. Diagnostic (Un)Certainty Can Be
Expressed as Probabilities
• Probability is expressed from 0.0 to 1.0
– Probability of heads on a coin flip = 0.5
• Alternative expression is odds
– Odds = Probability of event occurring /
Probability of event not occurring
– Odds of heads on a coin flip = 0.5/0.5 = 1
• Rolling a die
– Probability of any number = 1/6
– Odds of any number = 1/5
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6. Some Other Probability Principles
• Sum of all probabilities should equal 1
– e.g., p[heads] + p[tails] = 0.5 + 0.5 = 1
• Bayes’ theorem in diagnosis
– Post-test (posterior) probability a function of
pre-test (prior) probability and results of test
– Post-test probability variable increases with
positive test and decreases with negative test
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7. Diagnostic and Therapeutic
Thresholds (Guyatt, 2008)
5.4 Figure: Adapted from Guyatt, Rennie, Meade, & Cook, 2008
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8. Screening Tests for Disease
• “Identification of unrecognized disease”
• Aim to keep disease (or complications) from
occurring (primary prevention) or stop
progression (secondary prevention)
• Requirements for a screening test
– Low cost
– Intervention effective—ideally shown in randomized
controlled trial
– High sensitivity—do not want to miss any cases;
usually follow up with test of high specificity
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9. Americans Love Screening Tests
Despite Lack of Evidence
• Despite their limitations, screening tests for cancer are
very popular with Americans (Schwartz, Woloshin,
Fowler, & Welch, 2004)
• But cost of false-positive tests is substantial; in a study of
screening for prostate, lung, colorectal, and ovarian
cancer (Lafata et al., 2004):
– 43% of sample had at least one false-positive test
– Increased medical spending in following year by over $1000 per
person screened
• Controversies in recent years over screening for
– Breast cancer (Nelson et al., 2009; Kolata, 2009)
– Prostate cancer (Chou et al., 2011; Harris, 2011)
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10. Clinical Prediction Rules
• Use of results of multiple “tests” to predict diagnosis
(Adams, 2012)
• Best evidence establishes rule in one population and
validates in another independent one
• Examples of clinical prediction rules:
o Predicting deep venous thrombosis (DVT) (Wells et al., 2000;
Wells, Owen, Doucette, Fergusson, & Tran, 2006; Righini,
2013)
 High sensitivity, moderate specificity
 Better for ruling out than ruling in disease
o Coronary risk prediction—newer risk markers do not add
more to known basic risk factors (Folsom et al., 2006)
o Inconsistent results for prognostic ability of popular risk
prediction models (Siontis, 2012) 10

11. Evidence-Based Practice
Summary – Lecture d
• Another common type of question for
which we seek evidence is diagnosis
• Process of diagnosis involves logical
reasoning and pattern recognition
• Diagnosis consists of two essential steps:
– Generating a differential diagnosis
– Incorporating new information from diagnostic
tests to choose the most likely diagnosis
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12. Evidence-Based Practice
References – Lecture d
References
Adams, S. & Leveson, S. (2012) Clinical prediction rules. BMJ 2012;344:d8312. Retrieved from
http://www.bmj.com/content/344/bmj.d8312 (doi: http://dx.doi.org/10.1136/bmj.d8312)
Chou, R., et. al. (2011). Screening for prostate cancer: A review of the evidence for the U.S.
Preventive Services Task Force. Annals of Internal Medicine, 155(11), 762–771.
Ebright, P. (2014). Culture of safety, part one: Moving beyond blame. University of California.
MERLOT. Retrieved from http://tlcprojects.org/NEAT/CultureSafety_P1.swf
Folsom, A., et al. (2006). An assessment of incremental coronary risk prediction using C-reactive
protein and other novel risk markers: The atherosclerosis risk in communities study. Archives of
Internal Medicine, 166, 1368–1373.
Guyatt, G., Rennie, D., Meade, M., & Cook, D. (2014). Users’ guides to the medical literature:
Essentials of evidence-based clinical practice, 3rd edition. New York: McGraw-Hill.
Harris, G. (2011, Oct. 6). U.S. panel says no to prostate screening for healthy men. New York Times.
Retrieved from http://www.nytimes.com/2011/10/07/health/07prostate.html
Kolata, G. (2009, Nov. 22). Behind cancer guidelines, quest for data. New York Times. Retrieved from
http://www.nytimes.com/2009/11/23/health/23cancer.html
Lafata, J., Simpkins, J., Lamerato, L., Poisson, L., Divine, G., & Johnson, C. (2004). The economic
impact of false-positive cancer screens. Cancer, Epidemiology, Biomarkers, & Prevention, 13,
2126–2132.
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13. Evidence-Based Practice
References – Lecture d Continued
Nelson, H., Tyne, K., Naik, A., Bougatsos, C., Chan, B., & Humphrey, L. (2009). Screening for breast
cancer: An update for the U.S. Preventive Services Task Force. Annals of Internal Medicine, 151,
727–737.
Righini, M., et als. (2013). Predicting deep venous thrombosis in pregnancy: External validation of the
LEFT clinical prediction rule. Haematologica, 98(4), 545-548
Schwartz, L., Woloshin, S., Fowler, F., & Welch, H. (2004). Enthusiasm for cancer screening in the
United States. Journal of the American Medical Association, 291, 71–78.
Siontis, G. (2012). Comparisons of established risk prediction models for cardiovascular disease:
systematic review BMJ ;344:e3318. Retrieved from
http://www.bmj.com/content/344/bmj.e3318.full.pdf+html
Wells, P., et al. (2000). Derivation of a simple clinical model to categorize patients probability of
pulmonary embolism: Increasing the models utility with the SimpliRED D-dimer. Thrombosis and
Haemostasis, 83, 416–420.
Wells, P., Owen, C., Doucette, S., Fergusson, D., & Tran, H. (2006). Does this patient have deep vein
thrombosis? Journal of the American Medical Association, 295, 199–207.
Charts, Tables, Figures
5.4 Figure: Adapted from Guyatt, G., Rennie, D., Meade, M., & Cook, D. (2008). Users’ guides to the
medical literature: Essentials of evidence-based clinical practice. New York: McGraw-Hill.
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14. The Culture of Health Care
Evidence-Based Practice
Lecture d
This material was developed by Oregon Health &
Science University, funded by the Department of
Health and Human Services, Office of the National
Coordinator for Health Information Technology
under Award Number IU24OC000015. This
material was updated in 2016 by Bellevue College
under Award Number 90WT0002.
14