Upon arrival, a review of the inflight recordings and TTE change
the diagnosis to dissecting thoracic aortic aneurysm. Patient is taken
directly to surgery where ascending (type A) aortic dissection was
repaired. He was discharged home after one week.
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Conclusions
Extant discussion of diagnosis selection tends to represent the
reasoning probabilistically (e.g. Gøtzsche 2008, Ch. 4; Richardson &
Wilson 2015): the physician is supposed to estimate the prior
probability of the hypothesis and update this in light of information
from incoming tests. This model may fit some aspects of our case study.
For instance, when the physician was considering the implications of the
negative tests, she may be taken to rely (implicitly) on something like
a Bayesian estimation of the probability given test results. Given
appropriate assumptions about prior probability, sensitivity, and
specificity of the test, a positive test would have given a high
posterior probability of the disease being there, with negative tests
only moderately reducing it and thus failing to conclusively rule out
the diagnosis. (Insert here the Bayesian graph parts or not?)
However, often the physician will not be able to give anything like a
precise numerical estimate of their confidence on the hypothesis.
Instead she will have to rely on less precise judgments about whether
the clinical picture ‘fits’ the candidate hypothesis, judgments which
have to rely on clinical acumen and experience. In these cases she will
at best be able to state rough qualitative estimates, e.g. that a given
diagnosis is “the most likely” or “unlikely, but still possible”. In
these cases, it might be better to represent the physician’s reasoning
analogous to a fitting together of clues in a crossword puzzle, rather
than as vaguely approximating the mathematical machinery of the
probability calculus (see Haack 2003).
Even when probabilistic considerations can be applied, they are not
sufficient for diagnosis selection. As highlighted throughout, the
physician must also bear in mind the seriousness of the potential
diagnoses and test possibility of testing. Furthermore, if probabilistic
calculations are isolated from careful observation of the particular
facts of the clinical case and if assigned prior probabilities disregard
clinical experience and insight, exclusive consideration of
probabilities may lead to misdiagnosis. In this case, in order not to
miss a very plausible diagnosis and to prevent further muscle damage,
the cardiologist persists in the diagnosis of myocardial injury;
therefore, she recommends thrombolytic therapy, even in the face of an
atypical clinical picture.
Judicious attention to relevant facts that complicate this picture is
also relevant to perspicuous diagnosis. In the review, the cardiologist
returns to the bedside, listens for the holosystolic murmur reported at
initial examination, notes the non-stress induced pain; the slightly
widened aortic valve shadow on the CT, and the revises the diagnosis:
it must be a dissection . Abduction introduces an
hypothesis that provides more plausible explanation for the positive and
negative findings. The consideration of all relevant facts eliminates
several possible diagnoses from the diagnostic space—for example, the
negative CT scan ruled out pulmonary embolism. Several possible
diagnoses are conjectured. Consequences are deduced from them. For some
of the diagnoses, the predicted consequences are not observed, and the
diagnoses are discarded. Meanwhile, other diagnoses are consistent with
the observed facts, even though the salient clue (soft holosystolic
murmur) is soft, it and other clues, fit into an explanatory pattern.
All of the observations, history, testing, and physical examination must
be fitted together as clues in a crossword puzzle.
In general, the foregoing case illustrates: (1) the generation of
plausible diagnoses by abduction, and (2) their selection on the basis
of a combination of strategies. These strategies include the evaluation
of diagnoses through Bayesian statistical criteria, where the experience
and acumen of the clinicians provide informed prior probabilities.
However, they also involve careful observation and the comparison of
predicted signs and symptoms given a conjectured disease to the observed
bedside facts. The review of clinical findings and testing is crucial to
resolving atypical or unusual presentations. Experienced clinicians do
not rely exclusively on prior and posterior probabilities. They also
hold onto clinical history, the laboratory findings while considering
other conditions in order to refine their impressions and form
reasonable patterns that result in correct diagnoses.