Heart Rate Recovery and Mortality
To investigate whether impaired heart-rate recovery after exercise is a powerful and independent predictor of mortality.
|Study Design:||Observational Model: Defined Population
Observational Model: Natural History
|Study Start Date:||March 2001|
|Estimated Study Completion Date:||February 2004|
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.
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.
|Investigator:||Michael Lauer||The Cleveland Clinic|