• Cases & Commentaries
  • Published January 2004

Crushing Chest Pain: A Missed Opportunity

  • Spotlight Case

Case Objectives

  • Appreciate the challenges of diagnosing aortic dissection
  • Describe a Bayesian approach to diagnosis
  • Understand the benefits and limitations of heuristic thinking
  • List the cardinal dimensions of clinical decision-making

Case & Commentary: Part 1

A 62-year-old female presented with 12 hours of crushing chest pain. Her physical exam revealed a blood pressure of 140/90, a heart rate of 110, and a respiratory rate of 16. An electrocardiogram revealed left ventricular hypertrophy with strain. Review of the chest x-ray in the emergency department (ED) revealed no abnormalities. The patient was treated for an acute coronary syndrome (ACS) with heparin, aspirin, morphine, and a nitroglycerin drip. Cardiac enzymes were drawn. She was admitted to the cardiac care unit (CCU).

Seven hours after admission, the patient became hypotensive, with a systolic blood pressure in the 80s and a heart rate in the 120s. A repeat electrocardiogram revealed no significant changes. Right-sided leads showed no evidence of right ventricular infarct. The first set of cardiac enzymes was equivocal, and a CPK-MB was minimally elevated.

Chest pain is a common complaint in the ED, increasingly so as patients heed advice to find the closest hospital for evaluation. Correct diagnosis is critical in this setting: the patient's survival may hinge on making a timely and accurate diagnosis.

In a 62-year-old female with crushing chest pain, most physicians would choose an acute coronary syndrome as the most likely diagnosis, as in this case. They would arrive at this diagnosis knowing that coronary artery disease is common in the aging population and that unrelieved "crushing" pain often indicates myocardial ischemia. In the lingo of medical decision-making, this diagnosis emerged subconsciously from the 'availability' or the 'representativeness' heuristics.(1) Availability implies that the diagnosis springs to mind, likely because ED physicians often see patients whose chest pain is due to ACS. Representativeness implies a mental match between the patient's symptoms and the characteristic symptoms of ACS stored in the clinician's memory. In the absence of expert skills, heuristics are remarkably effective in helping us reach a correct diagnosis rapidly, accurately, and with little conscious effort. Unfortunately, they also lead to occasional diagnostic errors, for example, when the correct diagnosis is not considered.

Ideally, the ED clinicians would have used a Bayesian approach, such as 'expected value' decision-making, to derive the most probable diagnosis.(2) A Bayesian approach cannot guarantee success, but in theory has the highest likelihood of selecting the correct diagnosis. This approach begins by listing all the diagnostic possibilities and the likelihood of each. In one study, the most common cause of chest or back pain was acute coronary syndrome, present in 24.4% (Table 1).(3) The second step in the Bayesian approach is to adjust the initial probabilities using Bayesian calculations to incorporate any information uncovered in data gathering.(2) This patient described her chest pain as "crushing." Although this description has long been considered a hallmark of myocardial infarction (MI), studies suggest it is a weak predictor, with a positive likelihood ratio less than 2.(4) In addition, this patient lacked physical findings that increase the likelihood of MI (diaphoresis, a 3rd heart sound, hypotension, or rales). Finally, her ECG had no features suggestive of MI, a feature that modifies the possibility of ACS by a likelihood ratio of 0.1 to 0.3. Combining these likelihood ratios with the pre-test probability of 24.4% yields an overall likelihood of cardiac ischemia of less than 17%. These calculations can be performed simply by using the 'odds' form of Bayes theorem (Table 2) or a simple nomogram.

An alternative approach is to use an algorithm that simulates expert thinking. Many such aides are available, and these improve the sensitivity and specificity of diagnosing cardiac ischemia compared with 'clinical judgment.'(4-8) For example, one study used a formula based on seven clinical variables to predict cardiac ischemia,(8) and another derived a prediction rule using four clinical variables: past history of MI, presence of diaphoresis with chest pain, ST elevation, and the presence of a Q wave.(5) By these formulae, this patient's likelihood of having a myocardial infarction is less than 7%, or less than 2%, respectively.

The ED team correctly decided that the patient would benefit from observation in the CCU. Their initial diagnosis of ACS was reasonable in view of the history, and the fact that this diagnosis, in terms of base rates (Table 1), is orders of magnitude more likely than alternatives, such as acute aortic dissection (AD) (80 times less likely) or pulmonary embolism (60 times less likely). However, had the team incorporated all available data, they might have realized that the likelihood of cardiac ischemia was substantially less than initially assumed. This might have prompted a search for an alternative diagnosis.

Case & Commentary: Part 2

The team re-reviewed the chest x-ray and discovered an abnormality in the aorta: a 1-cm separation between the intimal calcification and the adventitial outline of the descending aorta (Figure 1).

The mortality rate of undetected AD approaches 1% per hour, and the diagnosis is missed in 25%-50% of patients.(9,10) Death from a missed AD is preventable, because AD can be easily and definitively identified by appropriate imaging (CT scan, MRI, or trans-esophageal echography), and survival exceeds 90% with prompt diagnosis and management.(11) However, the challenge is to consider the diagnosis in the first place.

The classical presentation is chest or back pain of acute onset, severe from the outset, tearing or ripping in quality.(10,12) Unfortunately, atypical presentations are common, because symptoms will vary depending on the exact anatomic location of the dissection and the secondary vasculature involved. Pain may not be present at all; in one series, 15% of patients with AD reported no pain.(9) Physical findings that may support the presence of a dissection include pulse deficits (greater than 20 mm differential), new aortic regurgitation, signs of pericardial tamponade, and focal neurological deficits (Table 3). The majority of patients, however, will have no specific physical findings.

In 90% of patients with AD, the admission chest x-ray is abnormal.(10) Abnormal aortic contour (sensitivity = 71%) and a widened mediastinum (sensitivity = 64%) are the most common findings. As in this case, the x-ray may show the 'calcium sign'—a separation by greater than 1 cm between the intimal calcification and the outer adventitial border. Problems degrading the test characteristics of the chest x-ray in this setting include vagaries in the test itself and problems with interpretation. For example, the apparent width of the mediastinum can be increased by a poor inspiration or supine positioning of the patient. Substantial inter-observer variability is encountered in the reading of chest x-rays.(13) In summary, a normal chest x-ray should not be used to exclude the possibility of AD.

A reliable blood test would be ideal to simplify the diagnosis of AD. Currently the use of D-dimer is being evaluated for this purpose. A recent report found a 100% sensitivity for D-dimer in detecting AD in a small series of patients.(15) Although this test lacks specificity (it may also be elevated in pulmonary embolism, cancer, DIC, sepsis, etc.), a negative result may help physicians reliably rule out AD. The specificity problem may be solved by measuring myosin heavy chain, released from vascular smooth muscle.(16) However, the utility of either test in the evaluation of AD has yet to be validated.

Case & Commentary: Part 3

A transesophageal echocardiogram revealed an ascending aortic dissection (Figure 2). Anticoagulation therapy was discontinued, beta-blocker therapy was initiated, and cardiothoracic surgery was called. The patient was transported to the operating room. Upon arrival in the operating room, the patient became progressively hypotensive, coded, and died. Post-mortem autopsy revealed hemorrhage into the pericardium.

What led to the fatal diagnostic error in this case? Delays in diagnosis of AD have been associated with incomplete historical questioning and atypical presentations.(14) This patient's death is the result of errors in each of the cardinal dimensions of clinical decision-making: data gathering, hypothesis generation (synthesis), and verification.

Data Gathering: The most critical error in this case was interpreting the chest x-ray as normal. This is a cognitive error, which may be knowledge-based (if staff were never trained to recognize abnormalities of the aorta) or skill-based (if staff were knowledgeable of the x-ray changes of AD but did not appreciate these on the films). In addition, the misread chest x-ray reflects a widespread "systems" flaw, where ED staff read chest x-rays instead of expert radiologists. This practice contributes to an untold number of diagnostic errors in medicine and illustrates the challenge medicine will face in reducing diagnostic error in the future. The error can be prevented, but at the substantial cost of having expertise available when needed. Teleradiology may be a solution for some institutions, but credentialing radiologists in another city, state, or country, has yet to be standardized.(17)

Additional errors in data gathering may have contributed if the history was incomplete. Rosman and colleagues found that clinicians correctly identified AD in more than 90% of patients if the history included three essential questions regarding the chest pain: the quality of the chest pain, its severity, and its location.(18) In contrast, the diagnosis was correct in less than half if all three questions were not asked.

Synthesis: The ED physicians assigned a diagnosis of ACS in this patient, without seriously excluding other possibilities. If the initial presentation does not "trigger" the correct diagnosis, it is unlikely to ever be considered.(19,20) The Bayesian approach would guarantee that the diagnosis of AD would have been considered.

Verification: Once a diagnosis is reached, clinicians have a tendency to stop thinking. An existing diagnosis has almost infinite inertia. This phenomenon of "premature closure" is possibly the most common cognitive error in internal medicine.(21) The CCU team in this case made another cognitive error when they accepted the ED diagnosis without re-examining the facts and independently re-thinking the case. Related cognitive biases include "framing" (we are overly biased by the way in which a case is presented; we tend to blindly accept a previous diagnosis established by others, or even ourselves), and "anchoring" (fixating inappropriately on an early diagnosis). To their credit, the CCU team eventually did re-think the case when the patient's condition changed. If a patient's course takes an unexpected turn, it is important to re-think initial assumptions and consider recruiting a colleague or consultant to evaluate the case anew.

Cognitive errors are all too common in medical decision-making. Although they can never be eliminated, they can be reduced by learning optimal decision-making strategies, understanding the intrinsic biases of using heuristics, and improving metacognition (the ability to monitor the accuracy of our own thought processes).(22-24) For example, the tendency to premature closure can be offset by conscious efforts to keep an open mind. Clinicians should routinely generate a complete differential diagnosis in every case. Consider applying the "crystal ball experience"—after reaching a diagnosis, pretend you can look into the future with your crystal ball and see that your initial diagnosis is wrong. What alternatives should be considered?

Take-Home Points

  • "The first priority in differential diagnosis is to think about the diseases you can't afford to miss."(2) It is always appropriate in ED settings to rule out the "worst case scenario."
  • Keys to detecting acute aortic dissection are a complete history (identifying the quality, severity, and location of the chest pain) and attention to the chest x-ray.
  • Always strive to achieve expert skills through learning. Non-experts can supplement diagnostic skills using expected value decision-making or established algorithms.
  • It is often wise to consider tests that might help "rule in" an alternative diagnosis than to pursue tests for a diagnosis already in doubt. Be prepared to re-think a case if an inconsistent finding or unexpected turn of events arises.
  • Learn to use cognitive forcing strategies to reduce diagnostic error (23); understand the common cognitive biases and use metacognition to avoid these traps. Keep an open mind.

Mark Graber, MD
Chief, Medical Service, VA Medical Center, Northport, NY
Professor and Vice-Chair, Department of Medicine, SUNY Stony Brook

Faculty Disclosure: Dr. Graber has declared that neither he, nor any immediate member of his family, has a financial arrangement or other relationship with the manufacturers of any commercial products discussed in this continuing medical education activity. In addition, his commentary does not include information regarding investigational or off-label use of pharmaceutical products or medical devices.

References


1. Elstein AS. Heuristics and biases: selected errors in clinical reasoning. Acad Med. 1999;74:791-4.[ go to PubMed ]

2. Sox HC Jr, Blatt MA, Higgins MC, Marton KI. Medical decision making. Stoneham, MA: Butterworth-Heinemann; 1988.

3. von Kodolitsch Y, Schwartz AG, Nienaber CA. Clinical prediction of acute aortic dissection. Arch Intern Med. 2000;160:2977-82.[ go to PubMed ]

4. Panju AA, Hemmelgarn BR, Guyatt GH, Simel DL. The rational clinical examination. Is this patient having a myocardial infarction? JAMA. 1998;280:1256-63.[ go to PubMed ]

5. Tierney WM, Roth BJ, Psaty B. Predictors of myocardial infarction in emergency room patients. Crit Care Med. 1985;13:526-31.[ go to PubMed ]

6. Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med. 1979;300:1350-58.[ go to PubMed ]

7. Goldman L, Cook E, Brand DA. A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med. 1988;318:797-803.[ go to PubMed ]

8. Pozen MW, D'Agostino RB, Selker HP, Sytkowki PA, Hood WB Jr. A predictive instrument to improve coronary-care-unit admission practices in acute ischemic heart disease. A prospective multicenter clinical trial. N Engl J Med. 1984;310:1273-8.[ go to PubMed ]

9. Spittell PC, Spittell JA Jr, Joyce JW, et al. Clinical features and differential diagnosis of aortic dissection: experience with 226 cases (1980 through 1990). Mayo Clin Proc. 1993;68:642-51.[ go to PubMed ]

10. Klompas M. Does this patient have an acute thoracic aortic dissection? JAMA. 2002;287:2262-72.[ go to PubMed ]

11. Nienaber CA, von Kodolitsch Y, Nicolas V. The diagnosis of thoracic aortic aneurysm by noninvasive imaging procedures. N Engl J Med. 1993;328:1-9.[ go to PubMed ]

12. Hagan PG, Nienaber CA, Isselbacher EM, et al. The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease. JAMA. 2000;283:897-903.[ go to PubMed ]

13. Gregorio MD, Baumgartner FJ, Omari BO. The presenting chest roentgenogram in acute type A aortic dissection: a multidisciplinary study. Am Surg. 2002;68:6-10.[ go to PubMed ]

14. Alsous F, Islan A, Ezeldin A, Zarich S. Potential pitfalls in the diagnosis of aortic dissection. Conn Med. 2003;67:131-4.[ go to PubMed ]

15. Weber T, Hogler S, Auer J, et al. D-dimer in acute aortic dissection. Chest. 2003;123:1375-8.[ go to PubMed ]

16. Suzuki T, Katoh H, Nagai R. Biochemical diagnosis of aortic dissection: from bench to bedside. Jpn Heart J. 1999;40:527-34.[ go to PubMed ]

17. Pollack A. Who's reading your x-ray? New York Times. Nov 16, 2003: BU 1, BU 9.

18. Rosman HS, Patel S, Borzak S, Paone G, Retter K. Quality of history taking in patients with aortic dissection. Chest. 1998;114:793-5.[ go to PubMed ]

19. Elstein AS. Clinical reasoning in medicine. In: Higgs JM, Jones M, eds. Clinical reasoning in the health professions. Oxford, England: Butterworth-Heinemann Ltd; 1995: 49-59.

20. Kassirer JP, Kopelman RI. Cognitive errors in diagnosis: instantiation, classification, and consequences. Am J Med. 1989;86:433-41.[ go to PubMed ]

21. Graber M, Franklin N, Gordon R. Diagnostic error in internal medicine. Paper presented at: 24th Annual Meeting of the Society for Medical Decision Making. October 20, 2002; Baltimore, MD. 2002.

22. Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med. 2002;9:1184-204.[ go to PubMed ]

23. Croskerry P. Cognitive forcing strategies in clinical decision making. Ann Emerg Med. 2003;41:110-120.[ go to PubMed ]

24. Graber M, Gordon Ruthanna, Franklin N. Reducing diagnostic error in medicine: what's the goal? Acad Med. 2002;77:981-92.[ go to PubMed ]


Tables


Table 1. Diagnosis of ED Patients with Chest Pain (3)

ED Diagnosis

Percentage (%)

Acute coronary syndromes

24.4

Neuro-radicular pain

17.1

Pulmonary disease

15

Cardiac arrhythmia

6

Vasovagal

6.5

Congestive heart failure

5

Hyperventilation

3.3

Hypertensive crisis

2.2

Chest wall syndrome

1.8

Gastrointestinal disease

1.2

Pneumothorax

0.6

Pulmonary embolism

0.4

Aortic dissection

0.3

Pleuritis

0.2

Pericarditis

0.1

No clear diagnosis

5.9

Other

10.1


Table 2. Using Bayes Theorem to Adjust Pre-test Probabilities for New Data (2)

Step

Process

Specific Calculations

1

Identify a likely diagnostic possibility.

Does this patient have an acute coronary syndrome?

2

Estimate the pre-test probability of this disease using known prevalence rates, a clinical prediction rule, or your local experience.

Pre-test probability = 24.4%;
Probability = 24.4% = Odds/(1+odds)
Odds = approximately 1/3 (one chance in three; one person will have an acute coronary syndrome for every 3 that have some other explanation).

3

Gather additional data or perform a test that will help support or refute your initial hypothesis.

Crushing chest pain: Likelihood ratio < 2
Lack of other characteristic findings: Likelihood ratio = 0.1 to 0.3

4

Uses Bayes' theorem to adjust your pre-test probability, incorporating this new information. The post-test probability is calculated most easily using published nomograms. Alternatively, the post-test probability can be calculated by multiplying the pre-test odds of disease by the relevant likelihood ratios.

Post-test odds = Pre-test odds x Likelihood ratio
Post-test odds = 1/3 X (<2) x (.1 to .3)
Post-test odds = < 1/15 to < 1/5 = < 6% to <17%

5

Keep gathering new data until you have reached your threshold for going to the next step, or dropping the disease from consideration.

At some point, the likelihood of an acute coronary syndrome becomes low enough that other diagnostic possibilities are more seriously considered.

6

Repeat this process for any other diagnostic possibilities.

 


Table 3. Sensitivity of the Physical Exam and Chest X-ray in the Diagnosis of Acute Thoracic Aortic Dissection (10)

Finding

Sensitivity (%)

95 CI* (%)

Elevated blood pressure

49

41-57

Diastolic murmur

28

21-36

Pulse deficit

31

24-39

Pericardial rub

6

3-13

Congestive heart failure

15

4-33

Focal neurological deficit

17

12-23

Shock

19

15-26

New MI by ECG

7

4-14

Chest x-ray findings

 

 

   Abnormal aortic contour

71

56-84

   Wide mediastinum

64

44-80

   Pleural effusion

16

12-21

   Displaced intimal calcification

9

6-13


*CI indicates confidence interval.

Figures


Figure 1. Chest X-ray with Calcium Sign (arrow).

Chest X-ray image.

Figure 2. Transesophageal Echocardiography of Aortic Dissection. Short Axis View of Proximal Aorta.

Echocardiography image.
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