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In examining a diagnostic test, we make the assumption that the characteristics of the test – its sensitivity and specificity (or likelihood ratios, the way I prefer to think) – will stay constant across different populations, although the positive and negative predictive values will change * . This is sort of true, and sort of false.<\/p>\n
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Take an imagined example: using leucocyte urine dipsticks to diagnose UTIs. If the original study is undertaken in the acute assessment department of a paediatric hospital, then the sticks will be used to differentiate between children with similar alternative diagnoses and conditions (e.g. viral infections, UTIs and avoiding school) . This is the disease spectrum<\/em>. It might be that in the referred population of the assessment unit, 10% of febrile children have UTIs (that’s the prevalence<\/em>).<\/p>\n Now the sensitivity and specificity that his study describes are likely to be true when used in a paediatric ED, or when seeing kids in primary care. Why? Because although the prevalence<\/em> of UTI may vary (maybe 1% in primary care and 5% in ED’s), the other competing diagnoses are similar. The prevalence<\/em> of the condition has changed, but the spectrum<\/em> of illnesses seen hasn’t.<\/p>\n Now imagine using the sticks in the paediatric haematology-oncology ward, or outpatients. The proportion of kids with UTI may be similar to that in the ED, but here the spectrum<\/em> is not the same. The competing diagnoses (e.g. neutropenic sepsis, chemotherapy-induced cystitis) will be very different. In this case, the spectrum<\/em> change alters the expected sensitivity and specificity of the dipsticks.<\/p>\n So – take away sensitivity, specificity (or LRs) from diagnostic studies when the spectrum is similar regardless of the prevalence in your patients. If the spectrum differs – beware.<\/p>\n * Just a reminder: if the population has a lower prevalence of disease, then the positive predictive value will be lower (ie a positive test will be wrong more often) but the negative predictive value will be higher (ie a negative test will be right more often). The reverse is true if the prevalence of the disease is higher.<\/p>\n","protected":false},"excerpt":{"rendered":" In examining a diagnostic test, we make the assumption that the characteristics of the test – its sensitivity and specificity (or likelihood ratios, the way I prefer to think) – will stay constant across different populations, although the positive and negative predictive values will change * . This is sort of true, and sort of […]<\/p>\n