Missed and Erroneous Diagnoses Common in Primary Care Visits
Bimal Jain
bjain at PARTNERS.ORG
Mon Dec 23 19:23:30 UTC 2013
Your point about prior probability is well taken. The point I am trying to make is that with any presentation with any estimated prior probability, we have no evidence for presence or absence of a suspected disease in a given, individual patient. I would have no objection to assigning a prior probability of 0.5 to every patient with a suspected disease regardless of presentation. This would lead to a post test probability of disease of 90 percent in every patient in whom a test result with likelihood ratio of 10 is observed. This post test test probability will actually correspond to our experience as I discuss below.
It is well known from experience any given disease , acute myocardial infarction for example occurs in different patients with varying presentations and therefore varying prior probabilities. As a presentation is constituted by combination of a number of independent factors such as symptoms, age, sex, risk factors,etc. we can expect the prior probability to be distributed normally in patients with disease encountered by us. (In PIOPED study on diagnosis of pulmonary embolsism, 68 percent patients with pulmonary enbolsm had mid range prior probabilities JAMA 263: 1990, 2753-2759) The average prior probability in these patients will then be close to 0.5. Observation of a test result with likelihood ratio of 10 in these patients will lead to an average post test probability of 90 percent indicating the test result diagnoses diease correctly in 90 percent patients. This high accuracy was actually observed in a large series of patients in whom acute Q wave and ST elevatio EKG changes with likelihood ratio of 13 diagnosed acute MI correctly in 90 percent patients (Rude et al Am J Card 52: 1983, 936-942). The problem with the standard Bayesian approach in which a prior probability is estimated from presentation in a given patient is that it leads to a diagnosis sometimes which is wrong from a clinical standpoint. This is seen in the following case, discussed in a clinical problem solvig exercise(Pauker et al NEJM 326:1992, 688-691). A 40 year old healthy woman without any cardiac risk factor presents with highly atypical chest pain and is found to hav acute Q wave, ST elevation EKG changes.Her prior probability estimaterd to be 7 percent of MI is combined with LR of EKG changes of 13 to yield a post test probability of 50 percent. The Bayesian diagnosis of MI being indeterminate is in sharp contrast to near certain diagnosis of MI from EKG changes alone by discussing physician.
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