GP 4001 Lecture Series Dealing with undifferentiated problems in primary care I

Learning Outcomes – three principal domains Dealing with undifferentiated problems presented by patients (udp)Dealing with undifferentiated problems presented by patients (udp) Management of chronic ill health (cdm)Management of chronic ill health (cdm) Communication (comm)Communication (comm)

Mary had a little cough It would not go away She went to see her GP To see what she would say The doctor looks and listens And weighs up different chances And sorts out Mary’s cough Without the aid of medical advances

Mary’s cough Mary is 36 years old and has had her cough for 3 days. What is the most likely cause of her cough?

Likelihood of different causes of cough of 3 days duration

Mary’s cough Mary is 36 years old and has had her cough for 3 weeks. What is the most likely cause of her cough?

Likelihood of different causes of cough of 3 weeks duration

Mary’s cough Mary is 36 years old and has had her cough for 3 months. What is the most likely cause of her cough?

Likelihood of different causes of cough of 3 months duration

Mary’s cough Mary is 36 months (i.e. 3 years) old and has had her cough for 3 days. What is the most likely cause of her cough?

Likelihood of different causes of cough in a 3 year old child

Mary’s cough Mary is 63 years old and has had her cough for 3 weeks. What is the most likely cause of her cough?

Likelihood of different causes of cough in a 63 year old

Mary’s cough Mary is 36 years old. She works in a chicken factory and has had her cough for 3 weeks. What is the most likely cause of her cough?

Mary’s cough Mary is 36 years old and has had her cough for 3 days. Last week she had stomach pains. The week before she had headaches. She comes to see her doctor at least once a week. What is the most likely cause of her cough?

What’s going on here – the diagnostic process Cues & CluesCues & Clues Main symptomMain symptom AgeAge GenderGender DurationDuration OccupationOccupation Patient’s general behaviour and demeanourPatient’s general behaviour and demeanour Generating diagnostic ideasGenerating diagnostic ideas Testing them using the information to handTesting them using the information to hand

Hypothetico-deductive reasoning Cues Information already known to doctor Presenting information from patient Provisional diagnoses Selective history and examination Re-evaluate diagnoses Seek confirmation by further history, examination or investigations Diagnosis not confirmed Diagnosis confirmed Other cues

Bayes’ Theorem the probability of a hypothesis being true (called the ‘posterior probability’) is a function of the probability you would have assigned to the hypothesis prior to making the observations (the ‘prior probability’) and the probabilities of the observations occurring if the hypothesis were true and the probability of the observations occurring if the hypothesis were false.

Put simply …. The chances of a diagnosis being right after we do a ‘test’ are related to the chances of the diagnosis being right before we do the ‘test’ and the chances of the ‘test’ being right and the chances of the test being wrong. N.B. All history, examination, investigation and information about a patient functions effectively as a ‘test’ in this context.

Shifting probabilities and Mary’s cough – Bayes’ Theorem in action Information Likelihood of lung cancer Mary has a cough Very low Mary is 63 years old Still quite low She has smoked 20 a day all her adult life A bit higher She has coughed up blood and has lost weight Quite high (ought to refer) (ought to refer)

Here comes the science bit!

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 diseaseSensitivity 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 diseaseSpecificity of a test is the proportion of the patients who test negative for the disease who actually do not have the disease

Sensitivity and Specificity TARGET DISORDER PRESENTABSENT DIAG- NOSTIC TEST RESULT +ab a + b -cd c + d a + c b + d a + b + c + d Sensitivity = a/(a+c) Specificity = (d/b+d)