Spotlight Case January 2004 Crushing Chest Pain: A Missed Opportunity

2 Source and Credits This presentation is based on the Jan AHRQ WebM&M Spotlight Case See the full article at CME credit is available through the Web site –Commentary by: Mark Graber, MD, State University of New York at Stony Brook –Editor, AHRQ WebM&M: Robert Wachter, MD –Spotlight Editor: Tracy Minichiello, MD –Managing Editor: Erin Hartman, MS

3 Objectives At the conclusion of this educational activity, participants should be able to: Appreciate the challenges of diagnosing aortic dissection Describe the Bayesian approach to diagnosis Understand the benefits and limitations of heuristic thinking List the cardinal dimensions of clinical decision-making

4 Case: Crushing Chest Pain A 62-year-old female presented with 12 hours of crushing chest pain. Her blood pressure was 140/90, heart rate 110, and respiratory rate 16. An EKG revealed left ventricular hypertrophy with strain. Review of the chest x-ray in the emergency department (ED) revealed no abnormalities. She was treated for an acute coronary syndrome (ACS) with heparin, aspirin, morphine, and a nitroglycerin drip. Cardiac enzymes were drawn.

5 The patient was admitted to the cardiac care unit. 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 EKG 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. Case (cont.): Crushing Chest Pain

6 Chest Pain in the Emergency Dept. Chest pain is a common complaint in the ED Correct and timely diagnosis is critical and linked to morbidity and mortality in many diagnoses –Acute coronary syndrome –Pulmonary embolism –Aortic dissection.

7 Diagnosis of Chest Pain in the ED von Kodolitsch Y, et al. Arch Intern Med. 2000;160:

8 Three Different Approaches to Medical Decision-Making Use of heuristics Bayesian approach Application of algorithms Elstein AS. Acad Med. 1999;74:791-4.

9 Examples of Medical Decision-Making Using Heuristics Availability—Diagnosis springs to mind because clinician has seen such patients before Representativeness—Mental match between patients symptoms and characteristic symptoms of disease stored in clinicians memory Elstein AS. Acad Med. 1999;74:791-4.

10 Benefits and Risks of Using Heuristics Advantage—Can reach correct diagnosis rapidly Disadvantage—Can lead to diagnostic error when correct diagnosis not considered Elstein AS. Acad Med. 1999;74:791-4.

11 This Case Approached Using Heuristics Clinician knows: –Acute Coronary Syndrome is the most common cause of chest pain in the emergency room Clinician thinks: –Diagnosis must be ACS

12 Medical Decision-Making Using Bayesian Approach List all diagnostic possibilities Determine likelihood of each Gather pertinent clinical data Adjust initial probabilities based on clinical data using Bayesian calculations Sox HC Jr, et al. Medical decision making.1988.

13 Is this ACS? Bayesian Approach Nomogram

14 Medical Decision-Making Using Bayesian Approach After adjusting pretest probability by clinical data available in this case (lack of ECG findings, lack of rales, hypotension, etc.), the overall likelihood of ACS is less than 17% CONSIDER ALTERNATIVE DIAGNOSIS!

15 Medical Decision-Making Using Algorithmic Approach Use of algorithms can simulate expert thinking Multiple decision models available Algorithms improve sensitivity and specificity of diagnosing cardiac ischemia when compared with clinical judgment Panju AA, et al. JAMA. 1998;280: Goldman L, et al. N Engl J Med. 1988;318: Pozen MW, et al. N Engl J Med. 1984;310:

16 Medical Decision-Making Using Algorithmic Approach Use of a formula based on 7 clinical variables to predict cardiac ischemia results in a likelihood of ACS of 7% Use of a derived prediction rule using 4 clinical variables (hx MI, diaphoresis, ST elevation, q waves) results in a likelihood of 2% of ACS in this patient CONSIDER ALTERNATIVE DIAGNOSIS! Pozen MW, et al. N Engl J Med. 1984;310: Tierney WM, et al. Crit Care Med. 1985;13:

17 Case (cont.): Crushing Chest Pain 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 (the “calcium sign”), consistent with aortic dissection.

18 Chest X-ray with Calcium Sign (arrow)

19 Aortic Dissection Mortality rates approach 1% per hour Diagnosis is missed in 25%-50% of patients Survival exceeds 90% with prompt diagnosis and management Spittell PC, et al. Mayo Clin Proc. 1993;68: Klompas M. JAMA. 2002;287: Nienaber CA, et al. N Engl J Med. 1993;328:1-9.

20 Aortic Dissection Classic presentation includes acute-onset, severe chest/back pain described as “tearing” or “ripping” Atypical presentations are common –15% of patients report NO pain Supportive findings include pulse deficit, new aortic regurgitation, tamponade, and focal neurological deficits Majority of patients have no specific physical findings Spittell PC, et al. Mayo Clin Proc. 1993;68: Hagan PG, et al. JAMA. 2000;283:

21 Aortic Dissection: Physical Exam Findings Klompas M. JAMA. 2002;287:

22 Aortic Dissection 90% of patients with aortic dissection have an abnormal CXR Abnormal aortic contour and widened mediastinum are the most common findings A NORMAL CXR DOES NOT RULE OUT AORTIC DISSECTION! Spittell PC, et al. Mayo Clin Proc. 1993;68: Hagan PG, et al. JAMA. 2000;283:

23 Aortic Dissection: CXR Findings Klompas M. JAMA. 2002;287:

24 Case (cont.): Crushing Chest Pain A transesophageal echocardiogram revealed an ascending aortic dissection. 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.

25 Transesophageal Echocardiography of Aortic Dissection Video

26 What Went Wrong? The patient’s death may be result of errors in each of the cardinal dimensions of clinical decision-making –Data gathering –Hypothesis generation/synthesis –Verification

27 Errors in Clinical Decision-Making Data gathering –Staff not trained to recognize the calcium sign Synthesis –Diagnosis of ACS assigned despite low likelihood –An alternative diagnosis was not initially entertained Verification –Premature closure: CCU team accepted diagnosis of ACS without re-examining the facts –Framing: Team biased by how case was presented –Anchoring: Team fixated on an early diagnosis Rosman HS, et al. Chest. 1998;114: Elstein AS. In: Clinical reasoning in the health professions. 1995: Kassirer JP, Kopelman RI. Am J Med. 1989;86: Graber M, et al. Acad Med. 2002;77:

28 Avoiding Errors in Clinical Decision-Making Consider diseases you cannot afford to miss Supplement diagnostic skills using a bayesian approach or established algorithms Consider tests that will help rule in an alternative diagnosis rather than pursue a test for a diagnosis already in doubt Be aware of common cognitive biases—avoid “premature closure” by re-examining the facts Ask yourself, “What else could this be?” Rosman HS, et al. Chest. 1998;114: Elstein AS. In: Clinical reasoning in the health professions. 1995: Kassirer JP, Kopelman RI. Am J Med. 1989;86: Graber M, et al. Paper presented: October 20, 2002; Baltimore, MD.