GP 4001 Lecture Series 2006-2007

1. GP 4001 Lecture Series 2006-2007 3. Dealing with undifferentiated problems in primary care II

2. Learning Outcomes for this course - I • Develop a rapport with patients such that patients are at ease in discussing their health problem(s) (comm) • Gather appropriate information on the patient’s health problem(s) including information on the patient’s own perspective on the problem(s). (udp, comm) • Generate a reasonable range of diagnostic possibilities for undifferentiated medical problems presented by patients (udp) • Investigate these diagnostic possibilities using appropriately focused history taking and selective physical examination (udp, comm)

3. Learning Outcomes for this course - II • Construct a general model for the safe and effective management of patients with multiple and long term health problems (cdm) • Adapt this model to the long term health problems commonly encountered by doctors (cdm) • Construct an appropriate and feasible management plan to deal with the physical, psychological and social aspects of patient’s problem(s) (udp, cdm) • Negotiate this plan with the patient. (comm)

4. Characteristics of tests – Sensitivity and Specificity • Sensitivity of a test is the proportion of patients who test positive for the disease who actually have the disease • Specificity of a test is the proportion of the patients who test negative for the disease who actually do not have the disease

5. Sensitivity and Specificity Sensitivity = a/(a+c) Specificity = (d/b+d)

6. Bayes’ Theorem and the characteristics of tests • Let us suppose we have a ‘test’ for the presence or absence of URTI in a population 35 year old women with 3 day history of cough, temperature and green sputum • Let us suppose in this population the prevalence of URTI is 80% (4 in 5) • Let us suppose for this test the sensitivity is 90% and the specificity is 90% • Let us say ‘having a runny nose’ is the test

7. True and false positivesTrue and false negatives • How many people will test positive who have the disease (true positives) • How many people will test positive who do not have the disease (false positives)? • How many people will test negative who do not have the disease (true negatives)? • How many people will test negative who do have the disease (false negatives)?

8. Answers 74 26 72 2 18 8

9. Conclusions • ‘Having a runny nose’ is a pretty good predictor of having an URTI (in a patients with other relevant features) • Not having a runny nose, however, is not such a reliable indicator of not having an URTI • In the jargon of clinical epidemiology • This ‘test’ has a good positive predictive value • But has poor negative predictive value

10. Another example to show the impact of prevalence on predictive value • Let us suppose we have a ‘test’ for the presence or absence of pneumonia in a population 35 year old women with 3 week history of cough, temperature and green sputum • Let us suppose in this population the prevalence of pneumonia is 20% (1 in 5) • Let us suppose for this test the sensitivity is 90% and the specificity is 90% • Let us suppose the presence or absence of basal crepitations on lung auscultation is the ‘test’

11. True and false positivesTrue and false negatives • How many people will test positive who have the disease (true positives) • How many people will test positive who do not have the disease (false positives)? • How many people will test negative who do not have the disease (true negatives)? • How many people will test negative who do have the disease (false negatives)?

12. Answers 26 74 18 8 72 2

13. Conclusions • For a test with a fairly typical sensitivity and specificity (i.e. 90% and 90%) we can say:- • When the disease has a high prevalence we tend to have more false negatives and fewer false positives • When the disease is less prevalent we tend to have more false positives and fewer false negatives • The ‘performance’ of a test (in terms of its ability to tell cases from non-cases) is critically dependent on the prevalence of the condition in the population (even if it has good sensitivity and specificity)

14. Predictive values of tests • The positive predictive value (PPV) is the proportion of patients with positive test results who are correctly diagnosed. • The negative predictive value (NPV) is the proportion of patients with negative test results who are correctly diagnosed

15. Positive and negative predictive values PPV = a/(a+b) NPV = d/(c+d)

16. URTI Example PPV = 72/74 =.97 NPV = 18/26=.69

17. Pneumonia Example PPV = 18/26=.69 NPV = 72/74 =.97

18. The positive predictive value for some values of prevalence, sensitivity and specificity

19. What does this all mean for making diagnoses in general • Knowing the prevalence of disease in a population is a very important consideration in making good diagnoses • The prevalence is essentially the same as ‘prior probability’ in the Bayesian model • Every bit of additional information we get about a patient functions as if it were a ‘test’ and has:- • Sensitivity • Specificity • Positive and negative predictive values

20. Implications for general practice • Where prevalence (the prior probability) is high ‘tests’ have good positive predictive value but poor negative predictive value • Where prevalence (the prior probability) is low (the typical situation in general practice) tests generally have poor positive predictive value but better negative predictive value • In general practice we tend to be better able to rule diseases ‘out’ • In hospital we tend to be better able to rule diseases ‘in’

21. Ruling in and ruling out • Hospital doctors focus on ruling disease in – i.e. establishing the presence of disease/ confirming a diagnosis • GPs focus on ruling disease out – i.e. establishing the absence of disease/ refuting a diagnosis

22. Types of error • Type 1 – accepting the null hypothesis when it ought to have been rejected i.e. missing a disease in a patient who has one • Type 2 – rejecting the null hypothesis when it ought to have been accepted i.e. diagnosing disease in a patient who does not have one

23. Who makes what kind of error? • Type 1 – accepting the null hypothesis when it ought to have been rejected i.e. missing a disease in a patient who has one • Type 2 – rejecting the null hypothesis when it ought to have been accepted i.e. diagnosing a disease in a patient who does not have one GPs Hospital doctors

24. Mary had a little cough Mary had a little cough There was a lot of it about Does she have pneumonia? That’s rare, so we can usually rule it out But what is the diagnosis? Whatever could it be? To say for sure, to rule it in That’s not the task of her GP

25. “It is very difficult to make an accurate prediction, especially about the future."