Download - Evidence-Based Diagnosis in Physical Therapy

Evidence-Based Diagnosis in Physical Therapy

Julie M. Fritz, PhD, PT, ATCDepartment of Physical

TherapyUniversity of Pittsburgh

What is Diagnosis?

“The anatomic, biochemical, physiologic, or psychologic

derangement”

DIAGNOSIS

Labeling Pathology

What is Diagnosis?

“Diagnosis is the term which names the primary dysfunction toward which the physical therapist directs treatment” (Sahrmann, 1989)

DIAGNOSIS

Planning Treatment

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

Three Strategies of Clinical Diagnosis

• Pattern recognition

• Complete history and physical examination

• Hypothetico-deductive strategy

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

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

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).

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

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

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.


Condition PresentCondition Absent

Test Positive

Test Negative

True Positive

True Negative

False Negative

False Positive


• Characteristics of Good Studies:

• Independent Gold Standard

• Operational Definitions

• Representative Subjects

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)

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)

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)


• Likelihood ratios combine the information contained in sensitivity and specificity values.

• Permits comparisons among competing 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)


• 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.

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)

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.

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

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

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:

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

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

Another Example

• None of the tests demonstrated good LRs

• None of the tests would function well as a screening tool

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):

Integrating Diagnostic Information into Practice

If Data Exists

If Data Does Not Exist

FIND IT!!

COLLECT IT!!

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 !!

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

THANK YOU

Review this lecture