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European Heart Journal Advance Access published online on January 11, 2006
European Heart Journal, doi:10.1093/eurheartj/ehi700
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European Heart Journal © The European Society of Cardiology 2005; all rights reserved
Received July 25, 2005
Revised October 31, 2005
Accepted December 1, 2005

Clinical research

Forecasting mortality: dynamic assessment of risk in ST-segment elevation acute myocardial infarction

Wei-Ching Chang 1, Padma Kaul 1, Yuling Fu 1, Cynthia M. Westerhout 1, Christopher B. Granger 2, Kenneth W. Mahaffey 2, Lars Wallentin 3, Frans Van de Werf 4, Paul W. Armstrong 1 *, and for the ASSENT-3 Investigators

1 Department of Medicine, University of Alberta, 2-51 Medical Sciences Building, Edmonton, Alberta, Canada T6G 2H7
2 Duke Clinical Research Institute, Durham, NC, USA
3 Department of Cardiology, University Hospital of Uppsala, Uppsala, Sweden
4 Center for Thrombosis and Vascular Research, Katholieke Universiteit, Leuven, Belgium

* To whom correspondence should be addressed.
Paul W. Armstrong, E-mail: paul.armstrong{at}ualberta.ca


  Abstract

Aims To demonstrate the feasibility and clinical utility of developing dynamic risk assessment models for ST-segment elevation myocardial infarction (STEMI) patients.

Methods and results In 6066 STEMI patients enrolled in the Assessment of the Safety and Efficacy of a New Thrombolytic-3 (ASSENT-3) trial with complete electrocardiographic data, we assessed the probability of 30-day mortality over the following forecasting periods beginning at day 0 (baseline), 3 h, day 2, and day 5 using multiple-logistic regression. These models were validated and simplified in independent samples of 1622 similar fibrinolytic-treated patients from the ASSENT-3 PLUS trial and in 814 STEMI patients undergoing primary percutaneous coronary intervention in the COMplement inhibition in Myocardial infarction treated with Angioplasty (COMMA) trial. The discriminatory power of these predictive models, from baseline to day 5, was excellent (c-statistics 0.80 to 0.87); and their predictive ability was supported by strong gradients in mortality outcomes as the risk score increased. Dynamic modelling also provided information on the change in prognosis over time which may be used to advise more appropriate therapeutic decisions, e.g. the identification of high-risk patients for possible co-interventions.

Conclusion Dynamic modelling for STEMI patients enhances the risk assessment and stratification and should provide valuable ongoing guidance for their management.

Keywords: Prognosis; Dynamic modelling; Simplified risk scores; ST-segment elevation myocardial infarction.
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