Lessons in clinical reasoning – pitfalls, myths, and pearls: a case of crushing, substernal chest pain

Denslow Allerton Trumbull; Erica L. Braschi; Ankur Jain; Frederick S. Southwick; Andrew S. Parsons; Nila S. Radhakrishnan

doi:10.1515/dx-2022-0017

Article

Lessons in clinical reasoning – pitfalls, myths, and pearls: a case of crushing, substernal chest pain

Denslow Allerton Trumbull , Erica L. Braschi , Ankur Jain , Frederick S. Southwick , Andrew S. Parsons and Nila S. Radhakrishnan

Published/Copyright: July 14, 2023

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Diagnosis Volume 10 Issue 3

Abstract

Objectives

Diagnostic error is not uncommon and diagnostic accuracy can be improved with the use of problem representation, pre-test probability, and Bayesian analysis for improved clinical reasoning.

Case presentation

A 48-year-old female presented as a transfer from another Emergency Department (ED) to our ED with crushing, substernal pain associated with dyspnea, diaphoresis, nausea, and a tingling sensation down both arms with radiation to the back and neck. Troponins were elevated along with an abnormal electrocardiogram. A negative myocardial perfusion scan led to the patient’s discharge. The patient presented to the ED 10 days later with an anterior ST-elevation myocardial infarction.

Conclusions

An overemphasis on a single testing modality led to diagnostic error and a severe event. The use of pre-test probabilities guided by history-taking can lead to improved interpretation of test results, ultimately improving diagnostic accuracy and preventing serious medical errors.

Keywords: Bayes theorem; diagnostic error; myocardial perfusion scan

Corresponding author: Nila S. Radhakrishnan, MD, SFHM, Associate Professor, Division of Hospital Medicine, University of Florida College of Medicine, Gainesville, FL, USA, Phone: 352 733 1234, E-mail: Radhakrishnan@ufl.edu

Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Informed consent: Informed consent was obtained from all individuals included in this study.
Ethical approval: As this is a case presentation, the local Institutional Review Board deemed the study exempt from review. https://irb.ufl.edu/index/irb-policies-guidelines-and-guidances.html.

References

1. Whelton, PK, Carey, RM, Aronow, WS, Casey, DE, Collins, KJ, Himmelfarb, CD, et al.. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American college of cardiology/American heart association task force on clinical practice guidelines. J Am Coll Cardiol 2018;71:e127–248, https://doi.org/10.1016/j.jacc.2017.11.006.Search in Google Scholar PubMed

2. Goff, DC, Lloyd-Jones, DM, Bennett, G, Coady, S, D’Agostino, RB, Gibbons, R, et al.. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of cardiology/American heart association task force on practice guidelines. Circulation 2014;129:S49–73, https://doi.org/10.1161/01.cir.0000437741.48606.98.Search in Google Scholar PubMed

3. D’Agostino, R, Vasan, R, Pencina, MJ, Wolf, P, Cobain, M, Kannel, W, et al. Calculator: cardiovascular risk assessment (10-year, general cardiovascular disease, women: framingham, 2008 paper) [Online]. Available from: https://www.uptodate.com/contents/calculator-cardiovascular-risk-assessment-10-year-general-cardiovascular-disease-women-framingham-2008-paper?search=cardiovascular%20risk%20calculator&source=search_result&selectedTitle=2∼150&usage_type=default&display_rank=2 [Accessed 10 Sept 2020].Search in Google Scholar

4. Six, AJ, Backus, BE, Kelder, JC. Chest pain in the emergency room: value of the HEART score. Neth Heart J 2008;16:191–6. https://doi.org/10.1007/bf03086144.Search in Google Scholar

5. Miner, B, Hart, EH. Wellens syndrome. In: StatPearls. Treasure island (FL): StatPearls Publishing; 2019.[Online]. http://www.ncbi.nlm.nih.gov/books/NBK482490/ [Accessed 20 Nov 2019].Search in Google Scholar

6. Kobayashi, A, Misumida, N, Aoi, S, Kanei, Y. Prevalence and clinical implication of wellens’ sign in patients with non-st-segment elevation myocardial infarction. Cardiol Res 2019;10:135–41. https://doi.org/10.14740/cr856.Search in Google Scholar PubMed PubMed Central

7. Haines, DE, Raabe, DS, Gundel, WD, Wackers, FJ. Anatomic and prognostic significance of new T-wave inversion in unstable angina. Am J Cardiol 1983;52:14–8. https://doi.org/10.1016/0002-9149(83)90061-9.Search in Google Scholar PubMed

8. Radhakrishnan, NS, Singh, H, Southwick, FS. Teaching novice clinicians how to reduce diagnostic waste and errors by applying the toyota production system. Diagnosis 2019;6:179–85. https://doi.org/10.1515/dx-2018-0081.Search in Google Scholar PubMed

9. Singh, H, Sittig, DF. Advancing the science of measurement of diagnostic errors in healthcare: the safer dx framework. BMJ Qual Saf 2015;24:103–10. https://doi.org/10.1136/bmjqs-2014-003675.Search in Google Scholar PubMed PubMed Central

10. Ngo, A, Gandhi, P, Miller, WG. Frequency that laboratory tests influence medical decisions. J Appl Lab Med 2017;1:410–4. https://doi.org/10.1373/jalm.2016.021634.Search in Google Scholar PubMed

11. Freedman, DB. Towards better test utilization – strategies to improve physician ordering and their impact on patient outcomes. EJIFCC 2015;26:15–30.Search in Google Scholar

12. Al Badarin, FJ, Malhotra, S. Diagnosis and prognosis of coronary artery disease with spect and pet. Curr Cardiol Rep 2019;21:57. https://doi.org/10.1007/s11886-019-1146-4.Search in Google Scholar PubMed

13. Hasbek, Z, Ertürk, SA, Çakmakçılar, A, Gül, İ, Yılmaz, A. Evaluation of myocardial perfusion imaging spect parameters and pharmacologic stress test with adenosine versus coronary angiography findings: are they diagnostically concordant? Mol Imaging Radionucl Ther 2019;28:53–61. https://doi.org/10.4274/mirt.2019.47450.Search in Google Scholar

14. Kumar, B, Kanna, B, Kumar, S. The pitfalls of premature closure: clinical decision-making in a case of aortic dissection. BMJ Case Rep 2011;2011:bcr0820114594. https://doi.org/10.1136/bcr.08.2011.4594.Search in Google Scholar PubMed PubMed Central

15. Khafaji, HAH, Suwaidi, JMA. Atypical presentation of acute and chronic coronary artery disease in diabetics. World J Cardiol 2014;6:802–13. https://doi.org/10.4330/wjc.v6.i8.802.Search in Google Scholar PubMed PubMed Central

16. Graber, ML. The incidence of diagnostic error in medicine. BMJ Qual Saf 2013;22:ii21–7. https://doi.org/10.1136/bmjqs-2012-001615.Search in Google Scholar PubMed PubMed Central

17. Singh, H, Meyer, AN, Thomas, EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf 2014;23:727–31. https://doi.org/10.1136/bmjqs-2013-002627.Search in Google Scholar PubMed PubMed Central

18. Singh, H, Giardina, TD, Meyer, AN, Forjuoh, SN, Reis, MD, Thomas, EJ. Types and origins of diagnostic errors in primary care settings. JAMA Intern Med 2013;173:418–25. https://doi.org/10.1001/jamainternmed.2013.2777.Search in Google Scholar PubMed PubMed Central

Received: 2022-02-16

Accepted: 2023-04-25

Published Online: 2023-07-14

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/dx-2022-0017

Keywords for this article

Bayes theorem; diagnostic error; myocardial perfusion scan