Evidence-Based Diagnosis in Physical Therapy
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Evidence-Based Diagnosis in Physical Therapy
Julie M. Fritz, PhD, PT, ATCDepartment of Physical
TherapyUniversity of Pittsburgh
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What is Diagnosis?
“The anatomic, biochemical, physiologic, or psychologic
derangement”
DIAGNOSIS
Labeling Pathology
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What is Diagnosis?
“Diagnosis is the term which names the primary dysfunction toward which the physical therapist directs treatment” (Sahrmann, 1989)
DIAGNOSIS
Planning Treatment
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What is Diagnosis?• Medical Diagnosis:
• Herniated Disc
• CVA
• Physical Therapy Diagnosis:• Right-sided radiculopathy centralizing with
repeated extension
• Left-sided hemiplegia - Brunnstrom Stage III: all movements in synergy with marked spasticity
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Three Strategies of Clinical Diagnosis
• Pattern recognition
• Complete history and physical examination
• Hypothetico-deductive strategy
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Pattern Recognition
• Instantaneous realization that the patient conforms to a previously learned pattern of disease
• Usually reflexive, not reflective
• Usually cannot be explained to others
• Argued to be “learned” on patients and not “taught” in lecture halls
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Complete History and Physical (Exhaustion)
• The pain-staking search for (but paying no immediate attention to) all the facts about a patient.
• Method of a novice
• Impractical and inefficient
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Hypothetico-Deductive Method
• The formulation, from the earliest clues of a “short list” of potential diagnoses.
• Subsequent tests are performed which will most likely reduce the length of the list.
• Requires an understanding of probability (zebras versus horses).
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Exhaustive vs. Hypothesis-Driven Approach
• Exhaustion
• empty the mind of all preconceived notions
• watch “nature in action”
• draw conclusions after all the facts are in
• Hypothesis-Driven• bold hypotheses are
proposed, then exposed to severe criticism
• requires understanding of confirmatory/discon-firmatory tests
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Gathering Diagnostic Data for a Hypothesis-Driven Approach
• Complete versus exhaustive data gathering
• Must know what is good data
• The importance of confirmatory and disconfirmatory data
• Rarely is one test sufficient
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Appraising the Literature Regarding Diagnostic Tests
• The effectiveness of a hypothesis-driven approach hinges on appropriate selection and interpretation of diagnostic tests.
• The clinician must be able to appraise the literature regarding diagnostic tests.
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Appraising the Literature Regarding Diagnostic Tests
Condition PresentCondition Absent
Test Positive
Test Negative
True Positive
True Negative
False Negative
False Positive
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Appraising the Literature Regarding Diagnostic Tests
• Characteristics of Good Studies:
• Independent Gold Standard
• Operational Definitions
• Representative Subjects
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Condition Present Condition Absent
Test Positive
Test Negative
True Positive A
True Negative D
False Negative C
False Positive
B
SENSITIVITY
A/(A+C)
SPECIFICITY
D/(B+D)
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Sensitivity (True Positive Rate)• Proportion of patients with the condition who
have a positive test result
• Tests with high sensitivity have few false negatives, therefore a negative result rules out the condition. (SnNout)
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Specificity (True Negative Rate)• Proportion of patients without the condition who
have a negative test result
• Tests with high specificity have few false positives, therefore a positive result rules in the condition. (SpPin)
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Appraising the Literature Regarding Diagnostic Tests
• Likelihood ratios combine the information contained in sensitivity and specificity values.
• Permits comparisons among competing tests.
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Appraising the Literature Regarding Diagnostic Tests
• Positive Likelihood Ratio: Expresses the change in odds favoring the disorder given a positive test.
(Sensitivity/(1-Specificity))
• Negative Likelihood Ratio: Expresses the change in odds favoring the disorder given a negative test.
((1-Sensitivity) /Specificity)
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Appraising the Literature Regarding Diagnostic Tests
• What characterizes a good test?
• Large +LR (>5.0)
• change the odds favoring the diagnosis given a + test
• helpful for ruling in the condition.
• Small -LR (<0.30)
• reduce the odds favoring the diagnosis given a - test
• . helpful for ruling out the condition.
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Pre-Test Likelihood Post-Test Probability Ratio Probability
X =
50% (1:1) X 5.0 = 83% (5:1)
50% (1:1) X 0.30 = 23% (.3:1)
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An Example from the Literature
• Rubenstein et al. The accuracy of the clinical examination of posterior cruciate ligament injuries. Am J Sports Med.1995.
• Performed multiple clinical tests for PCL laxity in 39 patients (78 knees), 19 with a torn PCL.
• gold standard = MRI.
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Test Sens. Spec. + LR - LR__
Posterior Drawer 90% 99% 90.0 0.10
Posterior Sag Sign 79% 100% ~79.0 0.21
Qd. Active Drawer 54% 97% 18.0 0.47
Reverse Pvt Shift 26% 95% 5.2 0.78
KT-1000 86% 94% 14.3 0.15
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An Example from the Literature
• All tests had higher specificity than sensitivity, therefore each is better as a rule in test.
• The posterior drawer test has a high +LR, and small -LR, making it an excellent diagnostic test
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Pre-Test Likelihood Post-TestProbability Ratio ProbabilityX =
25% (.33:1) X 0.10 = 3% (.03:1)
25% (.33:1) X 0.78 = 20% (.26:1)
Your patient is a 23 year-old male s/p MVA whose knee hit the dashboard, you think he may have injured his PCL (25% probability). You perform a diagnostic test to r/o the PCL injury. The result is negative.
Posterior Drawer Test:
Reverse Pivot Shift Test:
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Another Example
• 69 patients with acute, work-related LBP
• Waddell’s signs and symptoms assessed prior to treatment
• Gold standard = return to work within four weeks
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Test Sens. Spec. + LR - LR
Signs (2+) 41% 79% 1.9 0.75
Symptoms (3+) 50% 81% 2.6 0.62
Signs+Symptoms (3+) 64% 62% 1.7 0.59
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Another Example
• None of the tests demonstrated good LRs
• None of the tests would function well as a screening tool
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Pre-Test Likelihood Post-TestProbability Ratio ProbabilityX =
20% (.25:1) X 0.75 = 16% (.19:1)
20% (.25:1) X 0.59 = 13% (.15:1)
You have a patient with acute, work-related LBP. You know approximately 20% of such patients go on to long-term problems. You use Waddell’s tests as a screen to see if this patient is at risk. The results are negative.
Waddell’s Signs (<2):
Waddell’s Signs+Symptoms (<3):
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Integrating Diagnostic Information into Practice
If Data Exists
If Data Does Not Exist
FIND IT!!
COLLECT IT!!
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Integrating Diagnostic Information into Practice
• What You Need To Do:
• Decide what you are diagnosing
• List all possible variables
• Decide on the “gold standard”
• Measure Everyone !!
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An Example
You are in charge of screening residents of a long-term care facility for those who need therapy due to increased risk of falling.
What are you diagnosing - Risk of falling
What are the possible predictors?
What will be the gold standard of fall risk?
Follow-up everyone
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THANK YOU
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