Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-19T06:00:49.707Z Has data issue: false hasContentIssue false
  • English
  • Français

Risk stratification for adverse outcome in cardiac surgery

Published online by Cambridge University Press:  11 July 2005

J. H. Heijmans ,
J. G. Maessen  and
P. M. H. J. Roekaerts
J. H. Heijmans
Affiliation:
University Hospital Maastricht, Department of Anesthesiology, Maastricht, The Netherlands University Hospital Maastricht, Department of Cardiovascular Research Institute of Maastricht (CARIM), Maastricht, The Netherlands
J. G. Maessen
Affiliation:
University Hospital Maastricht, Department of Cardiothoracic Surgery, Maastricht, The Netherlands University Hospital Maastricht, Department of Cardiovascular Research Institute of Maastricht (CARIM), Maastricht, The Netherlands
P. M. H. J. Roekaerts
Affiliation:
University Hospital Maastricht, Department of Anesthesiology, Maastricht, The Netherlands University Hospital Maastricht, Department of Cardiovascular Research Institute of Maastricht (CARIM), Maastricht, The Netherlands
Get access

Extract

Summary

Risk-adjusted outcome prediction is mainly important in two separate fields. The first is quality monitoring: measuring actual versus predicted mortality in an institution allows assessment of the clinical surgical and anaesthesia performance while adjusting for the risk profile of the patients. Without risk stratification, surgeons and hospitals treating high-risk patients will appear to have worse results than others. This may prejudice referral patterns, affect the allocation of resources and even discourage the treatment of high-risk patients. The second field is that of informed consent and clinical decision-making. Risk-adjusted predicted mortality should form an important part of patient and surgeon decisions on whether or not to proceed with surgery. Clearly, no ‘perfect’ model can be produced as some aspects of mortality will always be related to risk factors not included in the model (e.g. the quality of the distal coronary artery vessels in coronary artery surgery) or due to chance happenings not related to preoperative patient characteristics (such as surgical error). An individual patient will either survive or die after cardiac surgery. Clearly, no scoring system will predict the specific outcome for every patient. However, risk stratification will inform patients and clinicians of the likely risk of death for a group of patients with a similar risk profile undergoing the proposed operation. This information is useful and should form part of the basis on which the patient and surgeon decide whether to proceed.

Keywords

ANAESTHESIACARDIOVASCULAR SURGICAL PROCEDURESRISK ASSESSMENTTREATMENT OUTCOME
Type
Review
Information
European Journal of Anaesthesiology , Volume 20 , Issue 7 , September 2003 , pp. 515 - 527
Copyright
© 2003 European Society of Anaesthesiology

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Parsonnet V, Dean D, Bernstein AD. A method of uniform stratification of risk for evaluating the results of surgery in acquired adult heart disease. Circulation 1989: 79: I3–I12.Google Scholar
Diamond GA. Future imperfect: the limitation of clinical prediction models and the limits of clinical prediction. J Am Coll Cardiol 1989; 14: 12–22.Google Scholar
Glantz SA. Primer of Biostatistics. New York, USA: McGraw Hill Book Company, 2001.
Donabedian A. The definition of quality and approaches to its assessment. Explorations in quality assessment and monitoring. Vol 1. Ann Arbor, MI, USA:Health Administration Press, 1980.
Daley J. Criteria by which to evaluate risk-adjusted outcomes programs in cardiac surgery. Ann Thorac Surg 1994; 58: 1827–1835.Google Scholar
Loop FD, Berrettoni JN, Pichard A, Siegel W, Razavi M, Effler DB. Selection of the candidate for myocardial revascularization; a profile of high risk based on multivariate analysis. J Thorac Cardiovasc Surg 1975; 69: 40–51.Google Scholar
Kennedy JW, Kaiser GC, Fisher LD, et al. Multivariate discriminant analysis of the clinical and angiographic predictors of operative mortality from the Collaborative Study in Coronary Artery Surgery (CASS). J Thorac Cardiovasc Surg 1980; 80: 876–887.Google Scholar
Wright JG, Pifarre R, Sullivan HJ, et al. Multivariate discriminant analysis of risk factors for operative mortality following isolated coronary artery bypass graft. Loyola University Medical Center experience, 1970 to 1984. Chest 1987; 91: 394–399.Google Scholar
Edwards FH, Albus RA, Zajtchuk R, et al. Use of a Bayesian statistical model for risk assessment in coronary artery surgery. Ann Thorac Surg 1988; 45: 437–440.Google Scholar
Hannan EL, Kilburn H Jr, O'Donnell JF, Lukacik G, Shields EP. Adult open heart surgery in New York State. An analysis of risk factors and hospital mortality rates. JAMA 1990; 264: 2768–2774.Google Scholar
O'Connor GT, Plume SK, Olmstead EM, et al. Multivariate prediction of in-hospital mortality associated with coronary artery bypass graft surgery. Northern New England Cardiovascular Disease Study Group. Circulation 1992; 85: 2110–2118.Google Scholar
Higgins TL, Estafanous FG, Loop FD, Beck GJ, Blum JM, Paranandi L. Stratification of morbidity and mortality outcome by preoperative risk factors in coronary artery bypass patients. A clinical severity score. JAMA 1992; 267: 2344–2348.Google Scholar
Tuman KJ, McCarthy RJ, March RJ, Najafi H, Ivankovich AD. Morbidity and duration of ICU stay after cardiac surgery. A model for preoperative risk assessment. Chest 1992; 102: 36–44.Google Scholar
Tremblay NA, Hardy JF, Perrault J, Carrier M. A simple classification of the risk in cardiac surgery: the first decade. Can J Anaesth 1993; 40: 103–111.Google Scholar
Edwards FH, Clark RE, Schwartz M. Coronary artery bypass grafting: the Society of Thoracic Surgeons National Database experience. Ann Thorac Surg 1994; 57: 12–19.Google Scholar
Tu JV, Jaglal SB, Naylor CD. Multicenter validation of a risk index for mortality, intensive care unit stay, and overall hospital length of stay after cardiac surgery. Steering Committee of the Provincial Adult Cardiac Care Network of Ontario. Circulation 1995; 91: 677–684.Google Scholar
Bridgewater B, Neve H, Moat N, Hooper T, Jones M. Predicting operative risk for coronary artery surgery in the United Kingdom: a comparison of various risk prediction algorithms. Heart 1998; 79: 350–355.Google Scholar
Wynne-Jones K, Jackson M, Grotte G, Bridgewater B. Limitations of the Parsonnet score for measuring risk stratified mortality in the north west of England. The North West Regional Cardiac Surgery Audit Steering Group. Heart 2000; 84: 71–78.Google Scholar
Roques F, Gabrielle F, Michel P, De Vincentiis C, David M, Baudet E. Quality of care in adult heart surgery: proposal for a self-assessment approach based on a French multicenter study. Eur J Cardiothorac Surg 1995; 9: 433–439.Google Scholar
Nashef SA, Roques F, Michel P, Gauducheau E, Lemeshow S, Salamon R. European system for cardiac operative risk evaluation (EuroSCORE). Eur J Cardiothorac Surg 1999; 16: 9–13.Google Scholar
Weightman WM, Gibbs NM, Sheminant MR, Thackray NM, Newman MA. Risk prediction in coronary artery surgery: a comparison of four risk scores. Med J Aust 1997; 166: 408–411.Google Scholar
Pliam MB, Shaw RE, Zapolanski A. Comparative analysis of coronary surgery risk stratification models. J Invas Cardiol 1997; 9: 203–222.Google Scholar
Pinna-Pintor P, Bobbio M, Colangelo S, et al. Inaccuracy of four coronary surgery risk-adjusted models to predict mortality in individual patients. Eur J Cardiothorac Surg 2002; 2: 199–204.Google Scholar
Lawrence DR, Valencia O, Smith EE, Murday A, Treasure T. Parsonnet score is a good predictor of the duration of intensive care unit stay following cardiac surgery. Heart 2000; 83: 429–432.Google Scholar
Doering LV, Esmailian F, Laks H. Perioperative predictors of ICU and hospital costs in coronary artery bypass graft surgery. Chest 2000; 118: 736–743.Google Scholar
Nashef SA, Carey F, Charman S. The relationship between predicted and actual cardiac surgical mortality: impact of risk grouping and individual surgeons. Eur J Cardiothorac Surg 2001; 19: 817–820.Google Scholar
Pinna Pintor P, Bobbio M, Sandrelli L, et al. Risk stratification for open heart operations: comparison of centers regardless of the influence of the surgical team. Ann Thorac Surg 1997; 64: 410–413.Google Scholar
Zenati M, Cohen HA, Holubkov R, et al. Preoperative risk models for minimally invasive coronary bypass: a preliminary study. J Thorac Cardiovasc Surg 1998; 116: 584–589.Google Scholar
Christakis GT, Ivanov J, Weisel RD, Birnbaum PL, David TE, Salerno TA. The changing pattern of coronary artery bypass surgery. Circulation 1989; 80: I151–I161.Google Scholar
Weintraub WS, Jones EL, Craver J, Guyton R, Cohen C. Determinants of prolonged length of hospital stay after coronary bypass surgery. Circulation 1989; 80: 276–284.Google Scholar
Michalopoulos A, Tzelepis G, Pavlides G, Kriaras J, Dafni U, Geroulanos S. Determinants of duration of ICU stay after coronary artery bypass graft surgery. Br J Anaesth 1996; 77: 208–212.Google Scholar
Zaroff J, Aronson S, Lee BK, Feinstein SB, Walker R, Wiencek JG. The relationship between immediate outcome after cardiac surgery, homogeneous cardioplegia delivery, and ejection fraction. Chest 1994; 106: 38–45.Google Scholar
Doering LV, Esmailian F, Imperial-Perez F, Monsein S. Determinants of intensive care unit length of stay after coronary artery bypass graft surgery. Heart Lung 2001; 30: 9–17.Google Scholar