Cardiovascular Diseases Clinical Trial
To investigate whether impaired heart-rate recovery after exercise is a powerful and independent predictor of mortality.
BACKGROUND:
Although there has been considerable attention paid to the prognostic significance of the
heart rate rise during exercise, only recently has it been noted that the heart rate fall
after exercise, or "heart-rate recovery," may be an even more powerful predictor of outcome.
Heart-rate recovery after exercise is a consequence of central reactivation of vagal tone.
As impaired parasympathetic function has been associated with increased risk of death, the
study tests the hypothesis that an impaired heart-rate recovery is a powerful and
independent predictor of mortality.
DESIGN NARRATIVE:
The overall aim of this project was to use heart-rate recovery to substantially improve the
prognostic value of the exercise test. The specific aims of this project were: 1) Derive
biologically meaningful mathematical models of heart-rate recovery. Data from over 20,000
patients who had undergone exercise testing at Cleveland Clinic Foundation between 1990 and
1998 were used; all of these patients had had their tests performed on exercise workstations
which recorded heart rates every 10 seconds during and after exercise. Heart-rate recovery
measures were the difference between heart rate at peak exercise and heart rate at different
points during recovery. Modeling was based on exponential families, using stepwise
selection, bootstrapping, and information theory approaches. Correlates of different
patterns of heart rate recovery were determined. 2) Using the results of modeling of
heart-recovery derived from the work in Specific Aim 1, determined a prognostically defined
optimal definition of abnormal heart rate recovery and demonstrated that an abnormal heart
rate recovery was a powerful and independent predictor of mortality in diverse patient
groups. Data from exercise tolerance tests of over 40,000 patients studied at the Cleveland
Clinic Foundation between 1990 and 1999 were analyzed. Statistical methods used included the
nonparametric Kaplan-Meier product limit method and the Cox proportional hazards model with
bootstrap validation, which included use of the random forest technique. 3) Using completely
parametric techniques, developed predictive survival models in which heart-rate recovery was
included along with clinical data and other exercise findings, including exercise capacity
and heart rate changes during exercise. The advantages of the parametric technique included:
a) it allowed for modeling of nonproportional hazards that might permit differential
strength of effect at different follow-up times for different sets of risk factors; b) it
generated absolute risk, not just relative risk; and c) it permitted patient-specific
prediction.
The study completion date listed in this record was obtained from the "End Date" entered in
the Protocol Registration and Results System (PRS) record.
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