GP 4001 Lecture Series GP 4001 Lecture Series 2006-20072006-2007

3. Dealing with 3. Dealing with undifferentiated problems in undifferentiated problems in

primary care II primary care II

Learning Outcomes for Learning Outcomes for this course - Ithis course - I

• Develop a rapport with patients such that Develop a rapport with patients such that patients are at ease in discussing their patients are at ease in discussing their health problem(s) health problem(s) (comm)(comm)

• Gather appropriate information on the Gather appropriate information on the patient’s health problem(s) including patient’s health problem(s) including information on the patient’s own information on the patient’s own perspective on the problem(s). perspective on the problem(s). (udp, (udp, comm)comm)

• Generate a reasonable range of diagnostic Generate a reasonable range of diagnostic possibilities for undifferentiated medical possibilities for undifferentiated medical problems presented by patients problems presented by patients (udp)(udp)

• Investigate these diagnostic possibilities Investigate these diagnostic possibilities using appropriately focused history taking using appropriately focused history taking and selective physical examination and selective physical examination (udp, (udp, comm)comm)

Learning Outcomes for Learning Outcomes for this course - IIthis course - II

• Construct a general model for the safe Construct a general model for the safe and effective management of patients and effective management of patients with multiple and long term health with multiple and long term health problems problems (cdm)(cdm)

• Adapt this model to the long term health Adapt this model to the long term health problems commonly encountered by problems commonly encountered by doctors doctors (cdm)(cdm)

• Construct an appropriate and feasible Construct an appropriate and feasible management plan to deal with the management plan to deal with the physical, psychological and social physical, psychological and social aspects of patient’s problem(s) aspects of patient’s problem(s) (udp, (udp, cdm)cdm)

• Negotiate this plan with the patient. Negotiate this plan with the patient. (comm)(comm)

Characteristics of tests – Characteristics of tests – Sensitivity and SpecificitySensitivity and Specificity

• Sensitivity of a test is the Sensitivity of a test is the proportion of patients who test proportion of patients who test positive for the disease who positive for the disease who actually have the diseaseactually have the disease

• Specificity of a test is the Specificity of a test is the proportion of the patients who test proportion of the patients who test negative for the disease who negative for the disease who actually do not have the diseaseactually do not have the disease

Sensitivity and Sensitivity and SpecificitySpecificity

 

TARGET DISORDER

 

PRESENT ABSENT

DIAG-NOSTIC

TEST RESULT

+ a b a + b

- c d c + d

  a + c b + d a + b + c + d

Sensitivity = a/(a+c) Specificity Sensitivity = a/(a+c) Specificity = (d/b+d)= (d/b+d)

Bayes’ Theorem and the Bayes’ Theorem and the characteristics of testscharacteristics of tests

• Let us suppose we have a ‘test’ for the Let us suppose we have a ‘test’ for the presence or absence of URTI in a population presence or absence of URTI in a population 35 year old women with 3 day history of 35 year old women with 3 day history of cough, temperature and green sputumcough, temperature and green sputum

• Let us suppose in this population the Let us suppose in this population the prevalence of URTI is 80% (4 in 5)prevalence of URTI is 80% (4 in 5)

• Let us suppose for this test the sensitivity is Let us suppose for this test the sensitivity is 90% and the specificity is 90%90% and the specificity is 90%

• Let us say ‘having a runny nose’ is the testLet us say ‘having a runny nose’ is the test

True and false positivesTrue and false positivesTrue and false negativesTrue and false negatives

• How many people will test positive who How many people will test positive who have the disease (true positives)have the disease (true positives)

• How many people will test positive who do How many people will test positive who do not have the disease (false positives)?not have the disease (false positives)?

• How many people will test negative who How many people will test negative who do not have the disease (true negatives)?do not have the disease (true negatives)?

• How many people will test negative who How many people will test negative who do have the disease (false negatives)?do have the disease (false negatives)?

AnswersAnswersOut of Out of 100100

Total number of casesTotal number of cases 8080

Total number of non-casesTotal number of non-cases 2020

Total positives Total positives

Total negativesTotal negatives

True positivesTrue positives

False positivesFalse positives

True negativesTrue negatives

False negativesFalse negatives

74

26

72

2

18

8

ConclusionsConclusions

• ‘‘Having a runny nose’ is a pretty good Having a runny nose’ is a pretty good predictor of having an URTI (in a predictor of having an URTI (in a patients with other relevant features)patients with other relevant features)

• Not having a runny nose, however, is Not having a runny nose, however, is not such a reliable indicator of not not such a reliable indicator of not having an URTIhaving an URTI

• In the jargon of clinical epidemiologyIn the jargon of clinical epidemiology• This ‘test’ has a good positive predictive This ‘test’ has a good positive predictive

valuevalue• But has poor negative predictive valueBut has poor negative predictive value

Another example to show the Another example to show the impact of prevalence on impact of prevalence on

predictive valuepredictive value• Let us suppose we have a ‘test’ for the presence Let us suppose we have a ‘test’ for the presence

or absence of pneumonia in a population 35 year or absence of pneumonia in a population 35 year old women with 3 week history of cough, old women with 3 week history of cough, temperature and green sputumtemperature and green sputum

• Let us suppose in this population the prevalence Let us suppose in this population the prevalence of pneumonia is 20% (1 in 5)of pneumonia is 20% (1 in 5)

• Let us suppose for this test the sensitivity is 90% Let us suppose for this test the sensitivity is 90% and the specificity is 90%and the specificity is 90%

• Let us suppose the presence or absence of basal Let us suppose the presence or absence of basal crepitations on lung auscultation is the ‘test’crepitations on lung auscultation is the ‘test’

True and false positivesTrue and false positivesTrue and false negativesTrue and false negatives

• How many people will test positive who How many people will test positive who have the disease (true positives)have the disease (true positives)

• How many people will test positive who do How many people will test positive who do not have the disease (false positives)?not have the disease (false positives)?

• How many people will test negative who How many people will test negative who do not have the disease (true negatives)?do not have the disease (true negatives)?

• How many people will test negative who How many people will test negative who do have the disease (false negatives)?do have the disease (false negatives)?

AnswersAnswersOut of Out of 100100

Total number of casesTotal number of cases 2020

Total number of non-casesTotal number of non-cases 8080

Total positives Total positives

Total negativesTotal negatives

True positivesTrue positives

False positivesFalse positives

True negativesTrue negatives

False negativesFalse negatives

26

74

18

8

72

2

ConclusionsConclusions• For a test with a fairly typical sensitivity For a test with a fairly typical sensitivity

and specificity (i.e. 90% and 90%) we can and specificity (i.e. 90% and 90%) we can say:-say:-• When the disease has a high prevalence we When the disease has a high prevalence we

tend to have more false negatives and fewer tend to have more false negatives and fewer false positivesfalse positives

• When the disease is less prevalent we tend to When the disease is less prevalent we tend to have more false positives and fewer false have more false positives and fewer false negativesnegatives

• The ‘performance’ of a test (in terms of its The ‘performance’ of a test (in terms of its ability to tell cases from non-cases) is ability to tell cases from non-cases) is critically dependent on the prevalence of critically dependent on the prevalence of the condition in the population (even if it the condition in the population (even if it has good sensitivity and specificity)has good sensitivity and specificity)

Predictive values of Predictive values of teststests

• The The positive predictive valuepositive predictive value (PPV) is the proportion of patients (PPV) is the proportion of patients with positive test results who are with positive test results who are correctly diagnosed. correctly diagnosed.

• The The negative predictive valuenegative predictive value (NPV) is the proportion of patients (NPV) is the proportion of patients with negative test results who are with negative test results who are correctly diagnosed correctly diagnosed

Positive and negative Positive and negative predictive valuespredictive values

  

TARGET DISORDERTARGET DISORDER

  PRESENTPRESENT ABSENTABSENT

DIAG-DIAG-NOSTIC NOSTIC

TEST TEST RESULTRESULT

++ aa bb a + ba + b

-- cc dd c + dc + d

   a + ca + c b + db + d a + b + c + da + b + c + d

PPV = a/(a+b) PPV = a/(a+b) NPV = NPV = d/(c+d)d/(c+d)

URTI ExampleURTI Example

  URTIURTI

  PRESENTPRESENT ABSENTABSENT

Runny noseRunny nose++ 7272 22 7474

-- 88 1818 2626

   8080 2020 100100

PPV = 72/74 =.97 PPV = 72/74 =.97 NPV = NPV = 18/26=.69 18/26=.69

Pneumonia ExamplePneumonia Example

  PneumoniaPneumonia

  PRESENTPRESENT ABSENTABSENT

Basal Basal crepitationcrepitation

ss

++ 1818 88 2626

-- 22 7272 7474

   2020 8080 100100

PPV = 18/26=.69 PPV = 18/26=.69 NPV = NPV = 72/74 =.9772/74 =.97

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

Prevalence Prevalence (%)(%)

Sensitivity and specificity (%)Sensitivity and specificity (%)

9999 9595 9090 8080

2020 96.196.1 82.182.1 69.269.2 50.050.0

1010 91.791.7 67.967.9 50.050.0 30.830.8

55 83.983.9 50.050.0 32.132.1 17.417.4

11 50.050.0 16.116.1 8.38.3 3.93.9

0.10.1 9.09.0 8.78.7 4.34.3 2.02.0

What does this all mean What does this all mean for making diagnoses in for making diagnoses in

generalgeneral• Knowing the prevalence of disease in a Knowing the prevalence of disease in a population is a very important population is a very important consideration in making good diagnosesconsideration in making good diagnoses

• The prevalence is essentially the same The prevalence is essentially the same as ‘prior probability’ in the Bayesian as ‘prior probability’ in the Bayesian modelmodel

• Every bit of additional information we Every bit of additional information we get about a patient functions as if it get about a patient functions as if it were a ‘test’ and has:- were a ‘test’ and has:- • SensitivitySensitivity• SpecificitySpecificity• Positive and negative predictive valuesPositive and negative predictive values

Implications for general Implications for general practicepractice

• Where prevalence (the prior probability) is Where prevalence (the prior probability) is high ‘tests’ have good positive predictive high ‘tests’ have good positive predictive value but poor negative predictive valuevalue but poor negative predictive value

• Where prevalence (the prior probability) is Where prevalence (the prior probability) is low (the typical situation in general low (the typical situation in general practice) tests generally have poor practice) tests generally have poor positive predictive value but better positive predictive value but better negative predictive valuenegative predictive value

• In general practice we tend to be better In general practice we tend to be better able to rule diseases ‘out’able to rule diseases ‘out’

• In hospital we tend to be better able to In hospital we tend to be better able to rule diseases ‘in’rule diseases ‘in’

Ruling in and ruling outRuling in and ruling out

• Hospital doctors focus on ruling Hospital doctors focus on ruling disease in – i.e. establishing the disease in – i.e. establishing the presence of disease/ confirming a presence of disease/ confirming a diagnosisdiagnosis

• GPs focus on ruling disease out – i.e. GPs focus on ruling disease out – i.e. establishing the absence of disease/ establishing the absence of disease/ refuting a diagnosisrefuting a diagnosis

Types of errorTypes of error• Type 1 – accepting the null hypothesis Type 1 – accepting the null hypothesis

when it ought to have been rejectedwhen it ought to have been rejected

i.e. missing a disease in a patient who i.e. missing a disease in a patient who has onehas one

• Type 2 – rejecting the null hypothesis Type 2 – rejecting the null hypothesis when it ought to have been acceptedwhen it ought to have been accepted

i.e. diagnosing disease in a patient i.e. diagnosing disease in a patient who does not have onewho does not have one

Who makes what kind of Who makes what kind of error?error?

• Type 1 – accepting the null hypothesis Type 1 – accepting the null hypothesis when it ought to have been rejectedwhen it ought to have been rejected

i.e. missing a disease in a patient who i.e. missing a disease in a patient who has onehas one

• Type 2 – rejecting the null hypothesis Type 2 – rejecting the null hypothesis when it ought to have been acceptedwhen it ought to have been accepted

i.e. diagnosing a disease in a patient i.e. diagnosing a disease in a patient who does not have onewho does not have one

GPsGPs

Hospital doctorsHospital doctors

Mary had a little coughMary had a little coughMary 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

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