Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- In your own words, discuss some of ... by KirikOis 3 views
- UG AND SLA by Ammara Shaukat 493 views
- The role of corrective feedback in ... by ahfameri 836 views
- Chomsky's theories of-language-acqu... by ottymcruz 60125 views
- First language acquisition by Valeria Roldán 30845 views
- Evidence-based diagnosis by Hesham Gaber 559 views

577 views

Published on

No Downloads

Total views

577

On SlideShare

0

From Embeds

0

Number of Embeds

1

Shares

0

Downloads

17

Comments

0

Likes

9

No embeds

No notes for slide

- 1. Evidence Based Diagnosis
- 2. When a Patient Has a Problem The doctor reaches a diagnosis by: • Clinical data • Diagnostic tools
- 3. Increasing use of Diagnostic tests: - Availability. - The urge to make use of new technology.
- 4. The evaluation of diagnostic techniques is less advanced than that of treatments (NO phase I, II, III, IV). New Diagnostic tests
- 5. Relevance • First, the test should be one that is feasible for you in your community • Example: brain biopsy is an accurate test for diagnosing dementia, it’s not practical for my (living) patients! • Can I apply the test to my patients? (Availability, Cost) e.g MRI
- 6. Validity The degree to which the results of a study are likely to be true and free from bias. • It should be compared to a gold reference standard
- 7. Caution • reference standard used should be acceptable (e.g HSG vs DL) • Both reference standard and test should be applied to all patients
- 8. Independent • the decision to perform the reference standard should ideally be independent of the results of the test being studied.
- 9. Ask yourself • the patient sample should include an appropriate spectrum of patients to whom the diagnostic test will be applied in clinical practice
- 10. Rule of Thumb • at least 100 participants to ensure an appropriate "spectrum" of disease
- 11. 2 x 2 table comparing the results of a diagnostic test with a reference standard reference standard disease no disease test abnormal true pos. [a] false pos. [b] test normal false neg. [c] true neg. [d]
- 12. sensitivity • probability of a positive test among patients with disease • i.e Ability to diagnose
- 13. specificity • probability of a negative test among patients without disease • i.e Ability to exclude
- 14. 2 X 2 Table b (false positive) a (true positive) d (true negative) c (false negative)
- 15. Keep in Mind • sensitivity and specificity by themselves are only useful when either is very high (over typically, 95% or higher).
- 16. 1000 individual 10% disease prevalenceS E N S I T I V I T Y S P E C I F I C I T Y +VE PREDICTIVE VALUE -VE PREDICTIVE VALUE = a/a+c 90/100 = 90% = d/b+d 720/900 =80% = a/a+b 90/720= 33% = d/c+d 720/730 = 99.6% +ve -ve disease No disease 90 10 180 720 100 900 270 730 a b c d
- 17. Who wants what ? main interest Methodologist sensitivity specificity Doctor accuracy Patient Probability
- 18. Likelihood Ratio The "positive likelihood ratio" (LR+) tells us how much to increase the probability of disease if the test is positive The "negative likelihood ratio" (LR-) tells us how much to decrease it if the test is negative
- 19. Likelihood Ratio LR+= probability of a +ve test in those who have the disease___ probability of a +ve test in those who do not have the disease sensitivity= 1-specificity LR-= ve test in those who have the disease___-probability of a probability of a -ve test in those who do not have the disease sensitivity-1= specificity
- 20. InterpretationLR Large and often conclusive increase in the likelihood of disease > 10 Moderate increase in the likelihood of disease5 - 10 Small increase in the likelihood of disease2 - 5 Minimal increase in the likelihood of disease1 - 2 No change in the likelihood of disease1 Minimal decrease in the likelihood of disease0.5 - 1.0 Small decrease in the likelihood of disease0.2 - 0.5 Moderate decrease in the likelihood of disease0.1 - 0.2 Large and often conclusive decrease in the likelihood of disease < 0.1
- 21. Why LR • The LR+ corresponds to the clinical concept of "ruling-in disease" • The LR- corresponds to the clinical concept of "ruling-out disease“
- 22. Patient oriented !!!!!!! • Your 45 year old patient has a mammogram. The study is interpreted as "suspicious for malignancy" by your radiologist. • Your patient asks you: "Does this mean I have cancer?", and you (correctly) answer "No, we have to do further testing."
- 23. • Your patient then asks, "OK, I understand that the mammogram isn't the final answer, but given what we know now, what are the chances that I have breast cancer?".
- 24. Is it Easy!!! • Assume that the overall risk of breast cancer in any 45 year old woman, regardless of mammogram result, is 1%. Assume also that mammography is 90% sensitive and 95% specific. Then, select your answer below: 1% 15% 60% 85% 95%
- 25. If you know that the risk of breast cancer in any 45 year old woman is 1% and that mammography is 90% sensitive and 95% specific. What do you think your patient’s probability of having breast cancer is? LR+=Sens/100-Spec =90/5=18
- 26. Disease ruled IN Disease ruled OUT Disease not ruled in or out Above this point, treat Below this point, no further testing Determined by: Complications of untreated disease Risks of therapy Complications of tests Cost
- 27. ROC curve is simply a graph of sensitivity vs (1-specificity)
- 28. Score Systematic Collaboration of Ovarian Reserve Evaluation systematic reviews of Diagnostic tests
- 29. THANK YOU

No public clipboards found for this slide

Be the first to comment