Despite this lack of evidence, she does not simply dismiss her
initial diagnosis. Although she faces conflicting evidence, she
maintains her clinical suspicion of myocardial injury caused by ACS. She
orders the tests repeated in two hours and has the patient monitored
closely for any signs of worsening condition. Because of persistent pain
he receives additional morphine. Meanwhile the clinician considers
alternative diagnoses which could mimic the symptoms of ACS.
Commentary: Why does the clinician maintain her initial
suspicion? While the negative results are sufficient to make her hold
off treatment based on the initial diagnosis, she wants to avoid
prematurely turning away from the commonest disease, especially since
not treating it could have potentially very negative effects for the
health of the patient. The case does not fit the textbook picture of
cardiac injury caused by ischemia. However, she knows both from her own
experience and epidemiological studies that atypical disease
presentations are not uncommon: the pain may be referred to the jaw
instead of chest, the T-waves in ECG may not develop early, etc.
Due to this uncertainty, she initiates two lines of action. Firstly,
although she does not have a single and simple hypothesis about what
could cause the atypical presentation, she decides to repeat the tests.
The fact that ACS is the most common cause of chest pain and the
life-threatening character of the diagnosis makes it reasonable to
repeat the tests. Secondly, her lowered confidence in the ACS diagnosis
is reflected in the fact that she decides to start systematically
generating alternative diagnoses in order to select a differential
diagnosis.
Generation and selection of differential diagnosis: The
clinician thinks of alternative diagnoses. She asks herself a number of
questions to guide her thinking: “what else could explain acute chest
pain?”, “which are the most common causes?”, “what would be the most
serious disease to miss?” “what could I test effectively and
quickly?”, “which conditions are effectively treatable?” She can
think of a wide range of possibilities. For instance, she briefly
considers Chagas disease, but decides this is too rare in the U.S.A. to
merit immediate consideration. Another possibility would be referred
pain e.g. from acute pancreatitis or another abdominal organ. In certain
clinical circumstances this can be confirmed by re-examination after
delaying an hour or more. In the present case, there is no time to
delay, as the pain seems to be continuous even with morphine analgesia.
She ultimately decides to focus on four alternative hypotheses in
addition to acute coronary syndrome:
- Acute pulmonary embolism (a blockage of a lung artery, e.g. by a
blood clot).
- Acute aortic syndromes, (different kinds of damage to the aorta,
the main artery leading blood out of the heart and into the body).
- Pericarditis (inflammation to the pericardium, the sac
surrounding the heart).
- Gastrointestinal reflux disease.
Commentary: At this stage, the clinician starts systematically
generating possible explanations. She probably has already thought of
alternative possibilities, but she now tries to explicitly elicit and
search her memory and clinical experience using a series of
interrogatives. The case presents a puzzling picture, so the clinician
decides initially to cast her net widely. She asks herself which other
diagnosis could explain the symptoms. Her first concern is to make sure
she has not forgotten to think of a potentially dangerous condition.
However, she needs to quickly limit herself to a short list of
actionable diagnoses. Trying to actively consider and rule out all
disease candidates is not practically possible, especially when the
patient is unstable. Thus, the physician asks herself further questions
to limit and focus her search, prioritizing conditions that (i) are
common for this type of patient, (ii) would be dangerous if left
untreated and (iii) allows of effective testing and treatment.
In this case the clinician chooses to generate hypotheses based on her
own experience and background knowledge. Sometimes the generation of
hypotheses happens almost automatically: the clinician recognizes a
known pattern and immediately recalls the most common and important
diagnoses. In more atypical cases, it can be necessary to employ a more
structured or directed form of thinking. Other possibilities would have
been to use one of the existing artificial intelligence programs
designed to assist medical diagnosis (e.g. Isabel, DXplain) or request a
colleague to review the data and offer a second opinion. All of these
would classify as different strategies for hypothesis generation.
Current computer programs are based on the prevalence and weighting of
signs and symptoms linked to diseases. They indicate which conditions
are most the common causes of the symptoms entered into the program and
red-flag potentially life-threatening diagnoses. The cardinal
arrangement of the diagnoses is based on the experience of the writers
of the program and the epidemiology of diseases commonly encountered in
the indicated age group, sex and geographical region. Using a computer
program can be helpful for reminding a clinician of rare but dangerous
conditions. Many experienced physicians however consider them of limited
usefulness.10
First, they tend to generate a fairly long list, which is not
particularly helpful in an emergency situation. Trying to work through
an extensive list of possibilities is not a feasible strategy especially
when the patient is unstable. It may also subject the patient to
unnecessary and potentially harmful over-testing. Second, physicians do
not know how the program assigns weights to the different signs and
symptoms and to the prevalence of the disease. The computer program is
based on geographically common epidemiologic findings in specific
diseases and populations, but this population level information cannot
be translated directly to the individual case. Experienced physicians
will be attuned to the concrete clinical setting (how stable is the
patient, what are the urgent problems), details of the case (e.g.
medical history, country of origin, foreign travel, use of drugs,
smoking) and the patient’s response to therapy (e.g. pain relief and
normalization of heart rate, breathing). These facts have to be
interpreted and the physician has to judge whether or not the findings
are properly perceived and integrated into the diagnostic picture. So,
physicians will in any case need to draw on their experience and insight
to interpret the results.
An important part of this is the ability to recognize the important
signs and symptoms, i.e. to recognize that a certain fact, or
combination of facts, is important information which needs to be taken
into account. For instance, had a patient travelled to sub-Saharan
Africa, this might suggest anemia as a result of malaria; sedentary life
style would place coronary ischemic heart disease higher in the list of
diagnoses; family history of diabetes in mother and grandmother would
interpret weight gain, hypertension, and dyslipidemia into the spectrum
of metabolic syndromes. Of course, neither a computer nor a human
reasoner can take a given piece of evidence into account before it is
recognized as evidence. As with hypothesis generation, trying to
take into account every single piece of information is not feasible, nor
necessarily very efficient. Perspicacious observation is here an
intricate part of the reasoning process itself. As Peirce noted,
“abductive inference shades into perceptual judgment without any sharp
line of demarcation between them” (1932-1958, §5.181). These computer
programs are, at best, an aid to, rather than a replacement for,
clinical judgement and experience.
To have a colleague review the data, whose difference in experience,
training and background knowledge may lead him to think of other
possibilities, can also be a way to ensure that important diagnoses are
not overlooked. At other times, it grows out of the physician being
confused by the picture or even dissatisfaction with her own thought
process. Although this strategy for generating hypotheses cannot
guarantee to be as exhaustive as a computer program, drawing on the
judgement of a colleague has the advantage of being better attuned to
the concrete clinical situation, and so being more likely to generate
suggestions that are plausible and useful in context.
The experience and training of the clinician, or her colleagues, play a
crucial role in several respects in the selection of a differential
diagnosis. First, the clinician has to make a wise choice of which
strategy for generating new suggestions strikes the right balance
between expediency, exhaustiveness and quality in the concrete
situations. Although one can discuss general considerations of
advantages and disadvantages of different strategies, as above, the
choice ultimately has to rely on the judgement of the physician. Second,
it is the training, experience and background knowledge of the clinician
that allows her to recognize patterns and recall possible diagnoses.
Finally, in choosing which diagnostic hypothesis to focus on the
clinician needs to judge which diagnoses are most likely in the concrete
case and decide how to weigh this, e.g., against the seriousness and
urgency of the disease, its testability and its treatability.
Prioritize hypothesis for testing: Cardiac enzymes after
two hours are borderline elevated, with c-Troponin is 98.5% of the
normal range. The clinician ponders if perhaps the origin of the
troponin elevation is from the epicardium or the pleura, rather than the
heart muscle, reassessing the hypotheses of enzyme origin and cause of
chest pain. She requests a cardiologist consultation. Meanwhile, she is
at the bedside. She next considers pulmonary embolism and orders a
chest-computed tomogram (CT-scan) with contrast media to search for the
embolism (the blockage).
Commentary: The clinician now decides to request a second opinion
from a specialist colleague. Meanwhile she prioritizes the pulmonary
embolism hypothesis for testing. There are several good reasons for
this. First, she currently considers pulmonary embolism one of the most
likely diagnoses. Second, pulmonary embolism and acute aortic syndromes
are both emergency conditions and would require immediate treatment.
Pericarditis is also a very serious condition but less urgent, whereas
gastrointestinal reflux disease is not an immediate threat. Third, a
CT-scan is a highly reliable way to detect embolism. Fourth, the chest
CT-scan might also show a widened mediastinum (the area containing the
heart and the pericardium), a possible sign of pericarditis. It would
also show the thoracic aorta (the part of aorta situated in the
chest-region), a possible site of any aortic syndrome. This last point
is an example of how a test can have other epistemic consequences in
addition to testing hypotheses directly, in this case by potentially
providing clues for future hypothesis generation. In sum, the CT-scan
would be a reliable test of the most likely and serious condition while
potentially also providing information relevant for the two other most
serious conditions.
Further puzzling results: The results of the CT-scan
adjusted to an early phase of contrast injection are reported as
negative for pulmonary embolism, but the ascending aorta is reported to
be prominent, measuring 4.3 cm in diameter (normal ascendingaorta is 3.63 to 3.91 cm ). The patient still complains of
chest pain but feels relieved by increased dose of morphine, and
breathing is improved somewhat by continuous 100% oxygen therapy.