Copyright © 2003 by the European Society of Cardiology.
Time-based risk assessment after myocardial infarction. Implications for timing of discharge and applications to medical decision-making
a The Duke Clinical Research Institute, Durham, NC, USA
b University of Alberta, Edmonton, Canada
c Universitaire Ziekenhuizen Leuven, Leuven, Belgium
d Green Lane Hospital, Auckland, New Zealand
e Cleveland Clinic Foundation, Cleveland, Ohio, USA
revised April 16, 2002; accepted April 17, 2002 * Correspondence: L. Kristin Newby, MD, Duke Clinical Research Institute, P.O. Box 17969, Durham, NC 27715-7969, USA
Aims We evaluated timing of adverse cardiac events after thrombolysis to guide length of stay after ST-segment elevation myocardial infarction.
Methods and Results Kaplan–Meier survival curves described timing of major postinfarction complications in 41021 fibrinolytic-treated patients in GUSTO-I. Using model-fitting, these data were best explained by a mixed-exponential survival model: an acute curve describing most adverse events and a chronic curve describing a lower background rate. We replicated this strategy in 15059 fibrinolytic-treated patients in GUSTO-III. From the relation between time and events described by the model’s acute curve in GUSTO-III, we proposed times for hospital discharge. The acute curve explained 97% of deaths and 68%–96% of various event composites. Of complications within 10 days, 90% of deaths and 70% of acute curve death, stroke, shock, heart failure, or reinfarction occurred by 24 h. By 2.7 days, 95% of deaths, stroke, shock, heart failure, or reinfarction occurred. Most major ventricular arrhythmias occurred within 24 h, after which the hazard curve was flat.
Conclusions Mixed-exponential survival modelling describes timing of post-infarction complications and supports discharge 4 days after uncomplicated infarction. Such time-based risk assessment could guide decision-making in other settings in which randomized studies are impractical.
Key Words: Early discharge • acute myocardial infarction • survival modelling
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