The European Journal of Health Economics

, Volume 8, Issue 4, pp 339–346 | Cite as

The incidence and cost of cardiac surgery adverse events in Australian (Victorian) hospitals 2003–2004

  • Jonathon Pouya Ehsani
  • Stephen J. Duckett
  • Terri Jackson
Original Paper


The aim of this study was to estimate the incidence of adverse events in acute surgical admissions for cardiac disease in admitted episodes in the year 2003–2004 and to estimate the cost of these complications to the Victorian health system. Cardiac surgery adverse events are among the most frequent and significant contributors to the morbidity, mortality and cost associated with hospitalisation. Patient-level costing data set for major Victorian public hospitals in 2003–2004 was analysed for adverse events using C-prefixed markers, denoting complications that arose during the course of hospital treatment for cardiac surgery diagnosis related groups (DRGs). The cost of adverse events was estimated by linear regression modelling, adjusted for age and co-morbidity. A total of 16,766 multi-day cardiac disease cases were identified, of whom 6,181 (36.85%) had at least one adverse event. Patients with adverse events stayed approximately 7 days longer and had four times the case fatality rate than those without. After adjustment for age and co-morbidity, the presence of an adverse event adds AUS$5,751. The sum of the total cost of adverse events for each DRG was AUS$42.855 million, representing 21.6% of total expenditure on cardiac surgery and adding 27.5% in broad terms to the cardiac surgery budget.


Health economics Costs Adverse events Patient safety 

JEL Classification

C13 D78 H51 



The authors would like to acknowledge the assistance of Peter McNair, Steve Gillett, Daniel Borovnicar and Jane Fewings, from the Department of Human Services, who provided ongoing support with the Victorian costing dataset, and Associate Professor Damien Jolley, for his advice on approaches to data analysis.


  1. 1.
    Frankum, B., et al.: The “Cam affair”: an isolated incident or destined to be repeated? Med. J. Aust. 180(7), 362–366 (2004)Google Scholar
  2. 2.
    Kennedy, I.: The Report of the Public Inquiry into Children’s Heart Surgery at the Bristol Royal Infirmary 1984–1995: Learning from Bristol. The Stationery Office Ltd, Norwich, UK (2001)Google Scholar
  3. 3.
    Barraclough, B.: Safety and quality in Australian healthcare: making progress. Med. J. Aust. 174, 616–617 (2001)Google Scholar
  4. 4.
    Wilson, R.M., et al.: The quality in Australian health care study. Med. J. Aust. 163(9), 458–471 (1995)Google Scholar
  5. 5.
    Hannan, E.L., et al.: Predictors of readmission for complications of coronary artery bypass graft surgery. JAMA 290(6), 773–780 (2003)CrossRefGoogle Scholar
  6. 6.
    Stiver, H.G., et al.: Pseudomonas sternotomy wound infection and sternal osteomyelitis. Complications after open heart surgery. JAMA 241(10), 1034–1036 (1979)Google Scholar
  7. 7.
    Ting, H.H., et al.: A total of 1,007 percutaneous coronary interventions without onsite cardiac surgery: acute and long-term outcomes. J. Am. Coll. Cardiol. 47(8), 1713–1721 (2006)CrossRefGoogle Scholar
  8. 8.
    Wynne, R., Botti, M.: Postoperative pulmonary dysfunction in adults after cardiac surgery with cardiopulmonary bypass: clinical significance and implications for practice. Am. J. Crit. Care 13(5):384–393 (2004)Google Scholar
  9. 9.
    Kiefe, C.: Predicting rehospitalization after bypass surgery: can we do it? Should we care? Med. Care 37(7), 621–624 (1999)CrossRefGoogle Scholar
  10. 10.
    Kimmel, S.E., Berlin, J.A., Laskey, W.K.: The relationship between coronary angioplasty procedure volume and major complications. JAMA 274(14):1137–1142 (1995)CrossRefGoogle Scholar
  11. 11.
    O’Connor, G., et al.: Multivariate prediction of in-hospital mortality associated with coronary artery bypass graft surgery. Northern New England Cardiovascular Disease Study Group. Circulation 85(6), 2110–2118 (1992)Google Scholar
  12. 12.
    Groom, R.C., Morton, J.R., Lefrak, E.A.: Outcomes analysis in cardiac surgery. Perfusion 12(4), 257–261 (1997)Google Scholar
  13. 13.
    Hammermeister, K.E., et al.: Identification of patients at greatest risk for developing major complications at cardiac surgery. Circulation 82(5 Suppl), 380–389 (1990)Google Scholar
  14. 14.
    Dacey, L.J., et al.: Perioperative stroke and long-term survival after coronary bypass graft surgery. Ann. Thorac. Surg. 79(2), 532–536 (2005)CrossRefGoogle Scholar
  15. 15.
    Burton, K.R., et al.: Hospital volume of throughput and periprocedural and medium-term adverse events after percutaneous coronary intervention: retrospective cohort study of all 17 417 procedures undertaken in Scotland, 1997–2003. Heart 92(11), 1667–1672 (2006)CrossRefGoogle Scholar
  16. 16.
    Wilson, R.M., et al.: An analysis of the causes of adverse events from the Quality in Australian Health Care Study. Med. J. Aust. 170, 411–415 (1999)Google Scholar
  17. 17.
    Rubin, H., Pronovost, P., Diette, G.: From a process of care to a measure: the development and testing of a quality indicator. Int. J. Qual. Health Care 13, 489–496 (2001)CrossRefGoogle Scholar
  18. 18.
    De la Costa, R., Muir, F., Harris, I.: Accuracy of mandatory surgeon recording of unplanned return to theatre. ANZ J. Surg. 74(5), 302–303 (2004)CrossRefGoogle Scholar
  19. 19.
    NCCH: The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM). National Centre for Classification in Health, Sydney (2002)Google Scholar
  20. 20.
    Jackson, T.: Using computerised patient level costing data for setting DRG weights: the Victorian (Australia) cost weight studies. Health Policy 56, 149–163 (2001)CrossRefGoogle Scholar
  21. 21.
    Jackson, T.: Cost estimates for hospital inpatient care in Australia: evaluation of alternative sources. Aust. N. Z. J. Public Health 24(3), 234–241 (2000)Google Scholar
  22. 22.
    Jackson, T.J., et al.: Data comparability in patient level clinical costing. Casemix Q. 1(1), 36–45 (1999)Google Scholar
  23. 23.
    Commonwealth of Australia: Australian Refined Diagnosis Related Groups Version 5, Definitions Manual, vol. 3. Department of Health and Aging, Canberra (2002)Google Scholar
  24. 24.
    Sundararajan, V., et al.: Epidemiology of sepsis in Victoria, Australia. Crit. Care Med. 33(1), 71–80 (2005)CrossRefGoogle Scholar
  25. 25.
    Sundararajan, V., et al.: New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J. Clin. Epidemiol. 57, 1288–1294 (2004)CrossRefGoogle Scholar
  26. 26.
    MacIntyre, C.R., Ackland, M.J., Chandraraj, E.J.: Accuracy of injury coding in Victorian hospital morbidity data. Aust. N. Z. J. Public Health 21(7), 779–783 (1997)Google Scholar
  27. 27.
    Sundararajan, V., et al.: Linkage of the Victorian admitted episodes dataset. Symposium on health data linkage: its value for Australian health policy development and policy relevant research, pp. 212–215. Public Health Information Development Unit, University of Adelaide, Adelaide (2003)Google Scholar
  28. 28.
    MacIntyre, C.R., et al.: Accuracy of ICD-9-CM codes in hospital morbidity data, Victoria: implications for public health research. Aust. N. Z. J. Public Health 21(5), 477–482 (1997)CrossRefGoogle Scholar
  29. 29.
    Roughead, E.E., et al.: Coding drug-related admissions in medical records: is it adequate for monitoring the quality of medication use? Aust. J. Hosp. Pharm. 28, 7–12 (1998)Google Scholar
  30. 30.
    Jackson, T., et al.: Measurement of adverse events using ‘incidence flagged’ diagnosis codes. J. Health Serv. Res. Policy 11(1), 21–26 (2006)CrossRefGoogle Scholar
  31. 31.
    Charlson, M.E., et al.: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40(5), 373–383 (1987)CrossRefGoogle Scholar
  32. 32.
    Gabriel, S.E., Crowson, C.S., O’Fallon, W.M.: A comparison of two comorbidity instruments in arthritis. J. Clin. Epidemiol. 52, 1137–1142 (1999)CrossRefGoogle Scholar
  33. 33.
    Zhang, J.X., Iwashyna, T.J., Christakis, N.A.: The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims. Med. Care 37, 1128–1139 (1999)CrossRefGoogle Scholar
  34. 34.
    Stata Corporation: Stata version 8.0, Texas (2003)Google Scholar
  35. 35.
    Moje, C., Jackson, T.J., McNair, P.: Adverse events in Victorian admissions for elective surgery. Aust. Health Rev. 30(3), 333–343 (2006)CrossRefGoogle Scholar
  36. 36.
    McLachlan, J.: Audits of VAED Data. In: ICD Coding Newsletter November 2001. ICD Coding Committee, Victorian Department of Human Services (2001)Google Scholar
  37. 37.
    Vincent, C., Neale, G., Woloshynowych, M.: Adverse events in British hospitals: preliminary retrospective record review. BMJ 322(7285), 517–519 (2001)CrossRefGoogle Scholar
  38. 38.
    Bates, D.W., et al.: The costs of adverse drug events in hospitalized patients. Adverse Drug Events Prevention Study Group. JAMA 277(4), 307–311 (1997)Google Scholar
  39. 39.
    Weed, L.L.: New connections between medical knowledge and patient care. BMJ 315, 231–235 (1997)Google Scholar
  40. 40.
    Sellick, J.A., Jr., Stelmach, M., Mylotte, J.M.: Surveillance of surgical wound infections following open heart surgery. Infect. Control. Hosp. Epidemiol. 12(10), 591–596 (1991)CrossRefGoogle Scholar
  41. 41.
    Elahi, M., Hadjinikolaou, L., Galinanes, M.: Incidence and clinical consequences of atrial fibrillation within 1 year of first-time isolated coronary bypass surgery. Circulation 108(Suppl 1), 207–212 (2003)Google Scholar
  42. 42.
    Maniar, P.B., et al.: Intravenous versus oral beta-blockers for prevention of post-CABG atrial fibrillation in high-risk patients identified by signal-averaged ECG: lessons of a pilot study. Card. Electrophysiol. Rev. 7(2), 158–161 (2003)CrossRefGoogle Scholar
  43. 43.
    Krupski, W.C., Nehler, M.R.: How to avoid cardiac ischemic events associated with aortic surgery. Semin. Vasc. Surg. 14(4), 235–244 (2001)CrossRefGoogle Scholar
  44. 44.
    Bratzler, D.W., et al.: Use of antimicrobial prophylaxis for major surgery: baseline results from the National Surgical Infection Prevention Project. Arch. Surg. 140(2), 174–182 (2005)CrossRefGoogle Scholar
  45. 45.
    Tegnell, A., Aren, C., Ohman, L.: Wound infections after cardiac surgery—a wound scoring system may improve early detection. Scand. Cardiovasc. J. 36(1), 60–64 (2002)CrossRefGoogle Scholar
  46. 46.
    Gottesman, R.F., et al.: Watershed strokes after cardiac surgery: diagnosis, etiology, and outcome. Stroke 37(9), 2306–2311 (2006)CrossRefGoogle Scholar
  47. 47.
    Johnsson, P., et al.: Cerebral complications after cardiac surgery assessed by S-100 and NSE levels in blood. J. Cardiothorac. Vasc. Anesth. 9(6), 694–699 (1995)CrossRefGoogle Scholar
  48. 48.
    McKhann, G.M., et al.: Stroke and encephalopathy after cardiac surgery: an update. Stroke 37(2), 562–571 (2006)CrossRefGoogle Scholar
  49. 49.
    Arrowsmith, J., et al.: Central nervous system complications of cardiac surgery. Br. J. Anaesth. 84(3), 378–393 (2000)Google Scholar
  50. 50.
    Ahonen, J., Salmenpera, M.: Brain injury after adult cardiac surgery. Acta Anaesthesiol. Scand. 48(1), 4–19 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Jonathon Pouya Ehsani
    • 1
  • Stephen J. Duckett
    • 2
  • Terri Jackson
    • 1
    • 3
  1. 1.School of Public HealthLa Trobe UniversityMelbourneAustralia
  2. 2.Reform and Development UnitQueensland HealthBrisbaneAustralia
  3. 3.Australian Centre for Economics Research on HealthUniversity of QueenslandBrisbaneAustralia

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