Evidence about Diagnostic Tests

1. Evidence about Diagnostic Tests Min H. Huang, PT, PhD, NCS

2. Diagnostic Tests • Test threshold and treatment threshold • Help focus the exam in a particular body region or system. • Identify potential problems that require referral to other health care providers. • Assist in the diagnostic classification (i.e. a specific practice pattern). • Diagnostic tests MUST be reliable and valid.

3. Study Credibility • Appraisal of evidence begins with assessment of research validity. • Higher levels of validity indicate greater confidence that there is a lack of bias. • Lists of specific questions to ask (Table 10-1 in the textbook).

4. Specific Questions to ask • Can the research questions or hypotheses be tested with the research design • Did the investigators compare results from the diagnostic test to results from a “gold standard” diagnostic test • Were all subjects evaluated with the comparison diagnostic test • Were the individuals performing and interpreting each test’s result unaware of the other test’s results (i.e. were they masked, or blinded)

5. Specific Questions to ask • Did the investigators include subjects with all levels of stages of the condition being evaluated by the measure of interest • Did the investigators confirm their findings with a new set of subjects • Did the study use appropriate statistical analysis methods for reliability and validity • Correlation coefficients • Face, construct, criterion, concurrent validity • Were p values or C.I. significant?

6. Study Results • Sensitivity (SnNout) • Specificity (SpPin) • Positive predictive value (PPV) • Negative predictive value (NPV) • Likelihood ratios (LR): • reflect a diagnostic test’s ability to provide persuasive information • LR + = Sn/(1-Sp) • LR – = (1-Sn)/Sp • Receiver Operating Characteristic Curves (ROC) • a graphic way to evaluate different thresholds of a test

7. Loong et al. (2003).

8. Sn = 24/30 = 80%

9. Sp = 56/70 = 80%

10. PPV = 24/38 = 63%

11. NPP = 56/62 = 90%

12. Figure 10-7: A Receiver Operating Characteristic (ROC) Curve for an Imperfect but Useful Test

13. Likelihood Ratio Nomogram • Use a nomogram to calculate posttest probability, i.e. the probability that the patient/client has the condition after a test result is obtained. • LR+ = 1-2, LR- = 0.5-1.0  negligible change in pretest probability http://www.cebm.net/index.aspx?o=1043

14. Evidence about Clinical Measures Min H. Huang, PT, PhD, NCS

15. Clinical Measures • Are NOT used to label or classify a diagnosis or practice pattern • Quantify and/or describe a patient’s impairments in a standardized fashion • Distinguish among different levels of severity of a problem • Instruments must have reliability, validity, responsiveness

16. Study Credibility • SAME process as diagnostic tests • Refer to questions in Table 10-2 • Clinical measures MUST be validated in patient populations with different diagnoses

17. Study Results • Reliability and validity are confirmed by correlation coefficients. • Responsiveness is commonly assessed by • Minimal detectable change (MDC): the amount of change that just exceeds the standard error of measurement • Standardized response mean (SRM): the ratio between the mean change score and the standard deviation of the change scores; reflect the change over time

18. Considerations for Implementing the Evidence into Practice • Test or measure should be available, practical and safe in the setting • Test or measure should have demonstrated performance on similar patient/clients • Can pretest probabilities be estimated for the patient/client • Patient/client’s preferences and values

19. Review • Most useful diagnostic tests and clinical measures have demonstrated reliability and validity • Reliability is shown through statistical tests of relationships among repeated test results • Validity is demonstrated through statistical tests or comparison to the gold standard • Responsiveness is measured MDC or SRM

20. Impact of Pain Reported During Isometric Quadriceps Muscle Strength Testing in People With Knee Pain: Data From the Osteoarthritis InitiativeDaniel L. Riddle, Paul W. Stratford Min H. Huang, PT, PhD, NCS

21. Introduction • Common clinical assumption • Impairments in body structure or function (e.g. pain) can impact limitations in activities and participation (e.g. physical function) • Limitations of previous research • NO large scale studies available • Does pain affect muscle strength? • 1 study: Yes • 1 study: No

22. Relationships between Domains of the ICF Model

23. Purpose • Whether the relationship between maximal isometric quad strength (X1) and functional status (Y1,Y2,….Y5) was influenced by pain during isometric testing (X2) • The extent to which pain during testing (X1) affected quad strength (Y1), or other functional tests (Y2, Y3, Y4, Y5)

24. Purpose Model 1 (Initial): Physical Function (Y) = β1 Strength (X1) + covariates + ε Model 2 (Full): Physical Function (Y) = β1 Strength (X1) + β2 Pain (X2) + β3 Strength (X1) × Pain(X2) + covariates + ε X Y Y X

25. Purpose Model 3 (No interaction) Physical Function (Y) = β1 Strength (X1) + β2 Pain (X2) + covariates + ε X Model 2 Model 1 Y Y Y X X

26. Purpose – Class Discussion

27. Method • Participants (n=1,344) • Unilateral knee pain Verbal Numerical Rating (VNR) > 3 • WOMAC pain >1 • Outcome variables • WOMAC physical function • 20-m walk • 400-m walk • 5 times sit to stand • Independent variables • MAX Quad strength • Pain during Quad strength testing • Multiple regression models • Model 1 • Model 2 • Model 3 • 95% CI of β excludes 0

28. Method – Class Discussion

29. Results Pain did NOT modify or confound any of the outcome variables: 400-m walk, 20-m walk, chair stand, WOMAC – Physical Function.

30. Results Table 6. MODERATE or SEVER pain during testing was WEAKLY associated with reduced STRENTGH, but mild pain was not.

31. Results – Class Discussion

32. Discussion • Pain during maximal isometric Quad strength tests did not affect the construct validity of the tests • Isometric Quad muscle strength and functional status relationship is NOT affected by reports of pain during testing

33. Discussion – Class Discussion

34. Limitations • Were the samples representative of the population treated? • Measurement of pain? No psychometric properties reported. • Muscle strength measured by dynamometer – Is it applicable to clinical settings using MMT? • No Hypotheses; No power estimate – finishing expedition?