Evidence-Based
Diagnosis Part I:
Introduction to
diagnosis
Mark H. Ebell MD, MS
Associate Professor
Dept of Epidemiology and Biostatistics
Co-Director, Institute for EvidenceBased Health Professions Education
College of Public Health
University of Georgia
Disclosure
 Editor-in-Chief, Essential Evidence Plus,
www.essentialevidence.com (Wiley-Blackwell)
 Deputy Editor, American Family Physician
(American Academy of Family Physicians)
 Co-Editor, Essentials of Family Medicine
(Lippincott Williams and Wilkins)
 Member, United States Preventive Services
Task Force
Objectives
Key tasks when teaching diagnosis
 What is the differential diagnosis?
 What is the pretest probability?
 How do I make best use of the history and physical?
 How do I select and interpret diagnostic tests?
My background in
technology
 Formal education
 Age 15, Fortran IV class at Delta Community College (punch
cards)
 Informal education
 Basic, Pascal, Visual Basic, NewtonScriptand computer
languages
 FamilyMD shareware program (1991-3)
 One of first Web sites for medical journal (1993)
 First medical application for Apple Newton (1996)
 Founded InfoPOEMs 1998, developed InfoRetriever, purchased
by Wiley Blackwell 2006, now Essential Evidence
 Programmed Newton, Windows CE, and desktop versions of
software
 Currently editor and software architect for Essential Evidence
What is a diagnostic test?
 A question about a symptom: "Have you
had a fever?" or "Is your chest pain worse
with exercise?"
 A physical sign such as swollen
glands or crackles in the lungs
 A blood, urine, or stool study
 An imaging study such as
ultrasound, CT, MRI, or x-ray
 An invasive study such as
colonoscopy or catheterization
 Combinations of the above called "clinical
decision rules" or "clinical prediction rules"
What are the steps in the
diagnostic process?
1. Determine the differential diagnosis
2. Use the history and physical examination to modify the
likelihood of each diagnosis
3. Use office-based diagnostic tests to modify the likelihood
of each diagnosis
4. Use other diagnostic tests if needed to rule in or rule out
important diagnoses
For example, consider diagnosis of influenza:
Pretest
probability
History and
physical
Diagnostic
tests
Influenza
ruled in or
out
Ruling in and ruling out
disease: "Threshold Model"
 The "Threshold Model" was developed by Stephen Pauker
and Jerome Kassirer in the 1980's
 It provides a framework for thinking about diagnosis:
 When can I stop ordering tests, and "rule out" a diagnosis?
 When should I stop ordering tests, and begin treatment?
 A challenge of evidence-based practice is to move from
implicit to explicit decision-making
Test
threshold
Treatment
threshold
0%
Do nothing
100%
More information needed
Treat
Example: Rapid test for
influenza
 Let's say that if we are more than 60% sure a patient has
the flu, we would make the diagnosis and begin treatment.
 On the other hand, if the probability was less than 10%, we
would no longer worry about it, especially since it is typically
a self-limited condition.
 That situation would look like this:
Test
threshold
Treatment
threshold
0%
Flu
ruled 10%
out
100%
Need more info
60%
Treat for flu
Example: Rapid test for
influenza
 During the middle of flu season, a patient comes in possible
flu-like symptoms
 The overall chance that they actually have flu before you
learn anything more about them is the "pretest probability"
and is about 30%
 What can we learn from the rapid flu test?
Test
threshold
30%
Treatment
threshold
0%
Flu
ruled 10%
out
100%
Need more info
60%
Treat for flu
Example: Rapid test for
influenza
 Given a pretest probability of 30% (typical in flu season):
 If the test is positive, the probability of flu increases to 84%
 If the test is negative, the probability of flu decreases to 8%
 These values are "post-test probabilities" and depend on
three things: the pretest probability, and the sensitivity and
specificity of the test. More on that later!
Test
threshold
0%
30%
Treatment
threshold
8%
Flu
ruled 10%
out
84%
Need more info
60%
Treat for flu
100%
Example: Rapid test for
influenza
 What if the patient has fever, cough, acute onset, and body
aches, increasing their pretest probability to 56%?
 Because the starting point has changed, the new post-test
probabilities are:
 If the test is positive, the probability of flu increases to 95%
 If the test is negative, the probability of flu decreases to 25%
 Now, a negative test does not help you!
Test
threshold
0%
Flu
ruled 10%
out
Treatment
threshold
25%
Need more info
100%
56%
95%
60%
Treat for flu
Example: Rapid test for
influenza
 Finally, what if someone comes in with fever and cough but it
isn't flu season. Their pretest probability is only about 5%.
 In this situation, you wouldn't order a test either, since they
are beginning below the test threshold.
 So, we've learned that pretest probability is important, and
that we have to interpret tests and perhaps even act
differently in different scenarios. One size does not fit all!
5%
Test
threshold
Treatment
threshold
0%
Flu
ruled 10%
out
100%
Need more info
60%
Treat for flu
Increasing bias if flaw present
Not just any answer,
but the right answer
Source: JAMA 1999; 282(11): 1064
Essential Evidence Plus (EE+)
 www.essentialevidence.com or www.eeplus.mobi/m
 Content
 780 disease or symptom topics (ie “Chest pain”, “Strep
throat”, “Tinea versicolor”)
 Plus underlying databases
 4000+ POEMs
 4000+ Cochrane abstract
 2000 H&P calculators
 2000 Diagnostic test calculators
 350 decision support tools
 Visual derm expert system
Differential diagnosis
 Lots of books and Web sites with lists
 Helpful to provide more than just list
Examples:
 Patient with chest pain – what are the
possibilities?
 Patient with cough – what are the
possibilities?
Differential Diagnosis (EE+)
Pretest probability
 No great sources
 Best is Dutch database
 Begin with pretest probability among
patients in study that looked at patients
like those in your practice
 Important area for research
History and Physical:
Demo with EE+
 In a patient with chest pain, are they having a
myocardial infarction?
 Chest pain  myocardial infarction
 In patient with sore throat, do they have Group
A beta-hemolytic strep?
 Sore throat  Group A strep pharyngitis
Diagnosis of skin lesions
Diagnosis of skin lesions
Diagnosis of skin lesions
Diagnosis of skin lesions:
Demo of EE+
Clinical decision rules
 Combine several elements of the history and physical
exam, perhaps including office-based tests
 Can stratify patients into low, moderate and high risk
 Good fit for our threshold model
Test
threshold
Treatment
threshold
0%
Do nothing
100%
More information needed
Treat
Clinical decision rules
 Strep score
 Prostate cancer prognosis (“Probability
that prostate cancer is indolent”)
 Many others
Diagnostic tests
 What is the best test to diagnose blood clot in the
lung?
 How accurate is troponin as a test for acute
myocardial infarction?