CC BY 4.0 · Aorta (Stamford) 2015; 03(03): 108-117
DOI: 10.12945/j.aorta.2015.14.060
State-of-the-Art Review
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Dissecting the Dissection

Towards More Comprehensive Decision-Making Methodology for Thoracic Aortic Disease
Hisham M.F. Sherif
1   Department of Cardiac Surgery, Christiana Hospital, Christiana Care Health System, Newark, Delaware, USA
› Author Affiliations
Further Information

Publication History

30 September 2014

27 March 2015

Publication Date:
24 September 2018 (online)

Abstract

Aortic dissection remains one of the most devastating diseases. Current practice guidelines provide diagnostic and therapeutic interventions based primarily on the aortic diameter. The level of evidence supporting these recommendations is Level C or “Expert Opinion” Since aortic dissection is a catastrophic structural failure, its investigation along the guidelines of accident investigation may offer a useful alternative, utilizing process mapping and root-cause analysis methodology. Since the objective of practice guidelines is to address the risk of serious events, on the utilization of a probabilistic predictive modeling methodology, using bioinformatics tools, may offer a more comprehensive risk assessment.

 
  • References

  • 1 Lemaire S, Russell L. Epidemiology of thoracic aortic dissection. Nat Rev Cardiol 2011; 8: 103-113 . DOI: 10.1038/nrcardio.2010.187
  • 2 Hiratzka LF, Bakris GL, Beckman JA, Bersin RM, Carr VF, Casey Jr DE. , et al. 2010 ACCF/ AHA/AATS/ACR/ASA/SCA/SCAI/SIR/STS/ SVM guidelines for the diagnosis and management of patients with Thoracic Aortic Disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, American Association for Thoracic Surgery, American College of Radiology, American Stroke Association, Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, Society of Interventional Radiology, Society of Thoracic Surgeons, and Society for Vascular Medicine. Circulation 2010; 121: e266-369 , DOI: 10.1161/CIR.0b013e3181d4739e
  • 3 Hagan P, Russman C, Isselbacher E, Bruckman D, Karavite D, Russman P. , et al. The International Registry of aortic dissection (IRAD): New insights into an old disease. JAMA 2000; 283: 897-903 . DOI: 10.1001/jama.283.7.897
  • 4 Criado FJ. Aortic dissection. A 250 year perspective. Tex Heart Inst J 2011; 38: 694-700 . PMID: 22199439.
  • 5 Leonard J. Thomas Bevill Peacock and the early history of dissecting aneurysm. Br Med J 1979; 2: 260-262 . DOI: 10.1136/bmj.2.6184.260
  • 6 Sherif HMF. Heterogeneity in the segmental development of the aortic tree: Impact on management of genetically triggered aortic aneurysms. Aorta (Stamford) 2014; 2: 186-195 . DOI: 10.12945/j.aorta.2014.14-032
  • 7 Wolinsky H, Glagov S. Structural basis for the static mechanical properties of the aortic media. Circ Res 1964; 14: 400-413 . DOI: 10.1161/01.RES.14.5.400
  • 8 Milewicz D. Perturbation of the VSMC contractile unit and aortic aneurysm: Clinical presentation, incidence and mechanism. Proceedings, the Aortic Disease Summit. Baltimore, Maryland. September 22–23, 2009.
  • 9 Sherif H. In search of a new therapeutic target for the treatment of genetically triggered thoracic aortic aneurysms and cardiovascular conditions: Insights from human and animal lathyrism. Interact Cardiovasc Thorac Surg 2010; 11: 271-276 . DOI: 10.1510/icvts.2010.239681
  • 10 Bonventre M. Sweet peas, copper, pregnancy and pills: Effects on the aorta. N Y State J Med 1974; 74: 633-637 . PMID: 4523429
  • 11 Hahn C, Schwartz M. Mechanotransduction in vascular physiology and atherogenesis. Nat Rev Mol Cell Biol 2009; 10: 53-62 . DOI: 10.1038/nrm2596
  • 12 Chung J, Lachapalle K, Wener E, Cartier R, De Varennes B, Fraser R. , et al. Energy loss, a novel biomechanical parameter, correlates with aortic aneurysm size and histopathology. J Thorac Cardiovasc Surg 2014; 148: 1082-1089 . DOI: 10.1016/j.jtcvs.2014.06.021
  • 13 Hsieh H, Liu C, Huang B, Tseng A, Wang D. Shear-induced endothelial mechanotransduction: The interplay between reactive oxygen species (ROS) and nitric oxide (NO) and the pathophysiological implications. J Biomed Sci 2014; 21: 3 . DOI: 10.1186/1423-0127-21-3
  • 14 Lu D, Kassab G. Role of shear stress and stretch in vascular mechanobiology. J R Soc Interface 2011; 8: 1379-1385 . DOI: 10.1098/rsif.2011.0177
  • 15 Ando J, Yamamoto K. Vascular mechanobiology. Endothelial cell responses to fluid shear stress. Circ J 2009; 73: 1983-1992 . DOI: 10.1253/circj.CJ-09-0583
  • 16 Cunningham A, Gotlieb A. The role of shear stress in the pathogenesis of atherosclerosis. Lab Invest 2005; 85: 9-23 . DOI: 10.1038/labinvest.3700215
  • 17 Parker K. A brief history of arterial wave mechanics. Med Biol Eng Comput 2009; 47: 111-118 . DOI: 10.1007/s11517-009-0440-5
  • 18 Cavalcante J, Lima J, Redheuil A, Al-Mallah M. Aortic stiffness. J Am Coll Cardiol 2011; 57: 1511-1522 . DOI: 10.1016/j.jacc.2010.12.017
  • 19 Sherif HMF. In search of a new therapeutic target for the treatment of genetically triggered thoracic aortic aneurysms and cardiovascular conditions: Insights from human and animal lathyrism. Interact Cardiovasc Thorac Surg 2010; 11: 271-276 . DOI: 10.1510/icvts.2010.239681
  • 20 Carper KL. Technical Council on Forensic Engineering: Twenty-year retrospective review. Foren Eng 2003; p. 280-296 . DOI: 10.1061/40692(241)29
  • 21 Cavalcante J, Lima J, Redheuil A, Al-Mallah M. Aortic stiffness. J Am Coll Cardiol 2011; 57: 1511-1522 . DOI: 10.1016/j.jacc.2010.12.017
  • 22 Carper KL. Technical Council on Forensic Engineering: Twenty-year retrospective review. Foren Eng 2003; . p. 280-296 . DOI: 10.1061/40692(241)29
  • 23 Reason J. Human error. Cambridge, UK: Cambridge University Press; 1999. . PMCID: PMC1070929
  • 24 Gerstein M. Flirting with disaster: Why accidents are rarely accidental. New York: Union Square Press; 2008
  • 25 Accident investigation. Washington DC: U.S. Department of Labor, Occupational Safety & Health Administration; 2002. [cited 2014 Sept 20]. Available at https://www.osha.gov/SLTC/accidentinvestigation/investigations.html
  • 26 Tague NR. Seven basic quality tools. In: The Quality Toolbox. Milwaukee, Wisconsin: American Society for Quality 2004 , p. 15
  • 27 Ishikawa K. Introduction to Quality Control. London: Chapman & Hall; 1990. , p. 448
  • 28 How to use the fishbone tool for root cause analysis. Center for Medicare and Medicaid Services. [cited 2014 Sept 20]. Available at http://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/QAPI/downloads/FishboneRevised.pdf
  • 29 Dhandapani D. Applying the Fishbone diagram and Pareto principle to Domino. IBM Lotus technical library. . June 2004 [cited 2014 Sept 20]. Available at http://www.ibm.com/developerworks/lotus/library/fishbone/
  • 30 Gunther D. Threat and error management. Emergency/Abnormal situations and security. San Jose, CA: NASA Ames Research Center; June 11–13, 2013 [cited 2014 Sept 20]. Available at http://humansystems.arc.nasa.gov/flightcognition/download/EAS_Symposium_Presentations/Security.pdf
  • 31 Browne L. Regulation of professions by the state. the right to regulate, reasons therefore, methods in use, and attitude of regulatory bodies and the courts, with relation thereto. Cal West Med 1935; 43: 119-123 . PMID: 18743337.
  • 32 Garoupa N. Regulation of professions in the US and Europe: A comparative analysis. Proceedings of the American Law and Economic Association Meetings. The Berkeley Electronic Press. 2004 [cited 2014 Sept 20]. Available at http://law.bepress.com/cgi/viewcontent.cgi?article=1053&context=alea
  • 33 Sherif H. Is practicing medicine virtually impossible? Employing virtual medicine technologies and techniques can help propel healthcare into the future. Healthc Inform 2006; 54-55 . PMID: 16948337.
  • 34 Rizzo J, Chen J, Fang H, Ziganshin BA, Elefteriades J. Statistical challenges in identifying risk factors for aortic disease. Aorta (Stamford) 2014; 2: 45-55 . DOI: 10.12945/j.aorta.2014.14-019
  • 35 North DW. A tutorial introduction to decision theory. IEEE Trans Syst Sci Cyber 1968; 4: 200-210 . DOI: 10.1109/TSSC.1968.300114
  • 36 Bayesian networks and decision making. University of Waterloo; Canada: [cited 2014 Sept 20]. Available at http://pami.uwaterloo.ca/~basir/ECE457/week10.pdf
  • 37 Lucas P. Bayesian networks in medicine: A model-based approach to medical decision making. . In: Adlassnig KP. Proceedings of the EUNITE workshop on Intelligent Systems in Patient Care, Vienna. Vienna: Austrian Computer Society; 2001. , p. 73-97
  • 38 Lucas P, Hommersom A. Modeling the interactions between discrete and continuous causal factors in Bayesian networks. . In: Myllymki P, Roos T, Jaakkola T. Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010). Helsinki: HIIT Publications; 2010. , p. 185-193
  • 39 Sherif H, Sadeghi S, Mogel G. Design and construction of a computer-based logical system for medical diagnosis. IOS Press 2001; 81: 459-464 . DOI: 10.3233/978-1-60750-925-7-459
  • 40 Sherif HMF, Johnson SS, Klair SA. Implementation of a computer-based decision support system for outcomes prediction and clinical triage: Initial results of two pilot studies. J Cardiol Ther 2013; 1: 71-77 . DOI: 10.12970/2311-052X.2013.01.02.6