Download presentation

Presentation is loading. Please wait.

Published byRose Stewart Modified over 3 years ago

1
Critically Evaluating the Evidence: diagnosis, prognosis, and screening Elizabeth Crabtree, MPH, PhD (c) Director of Evidence-Based Practice, Quality Management Assistant Professor, Library and Informatics

2
2/3 legal claims against GPs in UK 40,000-80,000 US hospital deaths from misdiagnosis per year Adverse events, negligence cases, and serious disability are more likely to be related to misdiagnosis than drug errors Diagnosis uses <5% of hospital costs, but influences 60% of decision making

3
What are tests used for?

4
Increase certainty about presence/absence of disease Disease severity (triage) Monitor clinical course Assess prognosis – risk/stage within diagnosis Plan treatment Screening

5
Appraising articles on diagnosis in 3 easy steps: Are the results important? Can the results be applied to my patient?

6
Appraising articles on diagnosis in 3 easy steps: Are the results important? Can the results be applied to my patient? Appropriate spectrum of patients? Does everyone get the gold standard? Is there an independent, blind or objective comparison with the gold standard?

7
Appropriate spectrum of patients? An example: Prospective Validation of the Pediatric Appendicitis Score, Goldman et al., 2008 Who are the patients being screened with the PAS? (Abstract, Methods pg. 279) - Study includes the full spectrum of manifestation of the illness (ex. early and late) - Study includes patients with illnesses commonly included in the differential

8
Reference standard applied (does everyone get the gold standard)? An example: Prospective Validation of the Pediatric Appendicitis Score, Goldman et al., 2008 Did all patients receive CT? (Methods Section, pg. 279) Investigators often forgo the reference standard when the diagnostic test is negative (may require a period of follow-up with criteria for need for treatment)

9
Is there an independent, blind or objective comparison to gold standard? An example: Screening for Urinary Tract Infections in Infants in the Emergency Department: Which Test Is Best, Shaw et al., 1998 What is the diagnostic test and what is the reference (gold) standard? (Abstract objectives, Methods, and Table 1) – Subjects should have both the diagnostic test in question and the reference standard – Be vigilant to the reference standard The results of one test should not be known (or bias) the other

10
Appraising articles on diagnosis in 3 easy steps: Are the results important? Can the results be applied to my patient? Sensitivity, Specificity Predictive Values ROC Curves Likelihood Ratios

11
Sensitivity and Specificity Sensitivity is the proportion of true positives that are correctly identified by a test or measure (e.g., percent of sick people correctly identified as having the condition) Ex: If 100 patients known to have a disease were tested, and 43 test positive, then the test has 43% sensitivity. Specificity is the proportion of true negatives that are correctly identified by the test (e.g., percent of healthy people correctly identified as not having the condition) Ex: If 100 patients with no disease are tested and 96 return a negative result, then the test has 96% specificity.

12
ROC Curves -Shows the tradeoff between sensitivity and specificity -The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. - The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test - The area under the curve is a measure of text accuracy

13
Area Under ROC Curve (AUC) Overall measure of test performance Comparisons between two tests based on differences between (estimated) AUC

14
Best Test: Worst test: True Positive Rate 0%0% 100% False Positive Rate 0%0% 100 % True Positive Rate 0%0% 100% False Positive Rate 0%0% 100 % The distributions don’t overlap at all The distributions overlap completely ROC Curve Extremes

15
True Positive Rate 0%0% 100% False Positive Rate 0%0% 100 % True Positive Rate 0%0% 100% False Positive Rate 0%0% 100 % True Positive Rate 0%0% 100% False Positive Rate 0%0% 100 % AUC = 50% AUC = 90% AUC = 65% AUC = 100% True Positive Rate 0%0% 100% False Positive Rate 0%0% 100 % AUC for ROC Curves

16
An example: Prospective Validation of the Pediatric Appendicitis Score, Goldman et al., 2008 What is the total area under the ROC curve for the PAS? (Results pg. 279, Figure 1)

17
Patients and clinicians have a different question… Positive and Negative Predictive Values Positive predictive value is the probability that a patient with a positive test result really does have the condition for which the test was conducted. Negative predictive value is the probability that a patient with a negative test result really is free of the condition for which the test was conducted Predictive values give a direct assessment of the usefulness of the test in practice – influenced by the prevalence of disease in the population that is being tested

18
Pre- and Post-Test Probability a solution to the deficiencies of sensitivity/specificity and predictive values?

19
Positive Likelihood Ratio probability of an individual with the condition having a positive test divided by the probability of an individual without the condition having a positive test A helpful test will have a large LR positive

20
Negative Likelihood Ratio probability of an individual with the condition having a negative test divided by the probability of an individual without the condition having a negative test A helpful test will have a small LR negative

21
LR < 0.1 = strong negative test result LR =1 = no diagnostic value LR >10 = strong positive test result

22
Pre test 5% Post test 20% ? Appendicitis: McBurney tenderness LR+ = 3.4 Likelihood Nomogram

23
Likelihood Ratio Approximate Change in Probability (%) * Values between 0 and 1 decrease the probability of disease 0.1 − 45 0.2 − 30 0.3 − 25 0.4 − 20 0.5 − 15 10 Values greater than 1 increase the probability of disease 2+15 3+20 4+25 5+30 6+35 7 8+40 9 10+45 From: J Gen Intern Med. 2002 August; 17(8): 647–650. doi: 10.1046/j.1525- 1497.2002.10750.x

24
Appraising articles on diagnosis in 3 easy steps: Are the results important? Can the results be applied to my patient? Can I do the test in my setting? Do results apply to the mix of patients I see? Will the result change my management? Costs to patient/health service?

25
Why is understanding prognosis important?

26
Appraising articles on prognosis in 3 easy steps: Are the results important? Can the results be applied to my patient? Sample of patients assembled at a common point in the course of their disease? Follow-up sufficient and complete? Outcome criteria objective? Adjustment for important prognostic factors?

27
Sample defined at common point? An example: Risk of epilepsy after febrile convulsions: a national cohort study, Verity & Golding, 1991 What kind of study is this? Was a defined, representative sample of patients assembled at a common point in the course of their disease? (Abstract) Cohort studies: best design by studying patients with the disease over time Case control: limited value by strength of inference

28
Follow-up sufficient and complete? An example: Risk of epilepsy after febrile convulsions: a national cohort study, Verity & Golding, 1991 How long were patients followed? Was this long enough to determine the outcome in question? (Abstract)

29
Outcome criteria objective? An example: Risk of epilepsy after febrile convulsions: a national cohort study, Verity & Golding, 1991 What were the criteria applied for ascertaining the outcome? (Abstract)

30
Adjustment for important prognostic factors? An example: Risk of epilepsy after febrile convulsions: a national cohort study, Verity & Golding, 1991 Were there subgroups to follow? (Abstract & Results)

31
Appraising articles on prognosis in 3 easy steps: Are the results important? Can the results be applied to my patient? What is the risk of the outcome over time? How precise are the estimates?

32
What is the risk of the outcome over time? Relative Risk Odds Ratios Survival Curves

33
HOMEHELPFEEDBACKSUBSCRIPTIONS Click on image to view larger version. Proc. Am. Thorac. Soc.Am. J. Respir. Cell Mol. Biol. Copyright © 2009 American Thoracic Society Return to a r t i c l e Return to a r t i c l e Post Transplant Survival for Patients with CF at Two Time Periods Liou et al. Am J Respir Crit Care Med 2005 Survival Curves

34
How precise are the estimates? Confidence Intervals Around a rate – Gives the reader a sense of precision – It represents the range that the test statistic would be expected to fall in if the study were repeated 100 number of times Ex. A 95% CI means that 95 out of 100 times the test statistic would fall within that range

35
Appraising articles on prognosis in 3 easy steps: Are the results important? Can the results be applied to my patient? Is my patient so different to those in the study that the results cannot apply? Will this evidence make a clinically important impact on my conclusions about what to offer my patients?

36
Basically, the process of deciding whether to screen follows the following format: 1) Is the prevalance of the disease high enough in the target population to warrant the time and expense of screening? 2) Does a therapy exist which will significantly reduce the risk of disease? 3) If not, will early screening effect the duration/severity of the disease? 4) Is the screening test itself sufficiently sensitive to catch the disease so that treatment may progress? Screening

37
Feeling confident in making the diagnosis and understanding prognosis helps determine whether to proceed with therapy When appraising articles, always consider validity, importance, and applicability Knowledge translation in this setting is the interpretation and integration of appraised and accepted evidence into clinical practice recommendations

Similar presentations

OK

Screening and Diagnostic Testing Sue Lindsay, Ph.D., MSW, MPH Division of Epidemiology and Biostatistics Institute for Public Health San Diego State University.

Screening and Diagnostic Testing Sue Lindsay, Ph.D., MSW, MPH Division of Epidemiology and Biostatistics Institute for Public Health San Diego State University.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To ensure the functioning of the site, we use **cookies**. We share information about your activities on the site with our partners and Google partners: social networks and companies engaged in advertising and web analytics. For more information, see the Privacy Policy and Google Privacy & Terms.
Your consent to our cookies if you continue to use this website.

Ads by Google

Ppt on programmable logic array application Ppt on panel discussion Download ppt on human evolution Ppt on writing english skills Training ppt on communication skills Ppt on traumatic brain injury Ppt on astronomy and astrophysics journals Download ppt on wildlife conservation in india Ppt on models of atoms Ppt on superstition in indian culture