Is There a Better Way to Predict Death Using Heart Rate Recovery?

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1 4693b-gorelik 8/14/6 11:15 AM Page 1 Clin. Cardiol. 29, (26) Is There a Better Way to Predict Death Using Heart Rate Recovery? DMITRY DAVID GORELIK, M.D., DAVID HADLEY, PH.D.,* JONATHAN MYERS, PH.D., VICTOR FROELICHER, M.D., FACC Stanford University VA Palo Alto Health Care System, Palo Alto, California; *Quinton Cardiology, Bothell, Washington, USA Summary Background: (HRR) during exercise testing is an independent predictor of prognosis. The relative predictive power of computational analysis of HRR as a function of resting and maximum heart rate (HR) compared with direct measurement of the drop in HR has not been determined. Hypothesis: We aimed to improve on the prognostic value of HRR by the use of mathematical representations of HRR kinetics. Methods: In all, 2,193 patients who underwent exercise testing, coronary angiography, and clinical evaluation were followed up for 1.2 ± 3.6 years. Mathematical functions were used to model HRR as a function of resting (HR Rest ), maximum HR (HR Peak ) and time (t): (a) (b) HRR = HR Rest + (HR Peak HR Rest ) e kt and HRR = HR Rest + (HR Peak HR Rest ) e kt2 Results: Equation (b) provided the best fit of the recovery HR curve. An abnormal HRR at 2 min was a better predictor of mortality than HRR at 1, 3, or 5 min. At 2 min, HRR also predicted mortality better than computational models of HRR, relating HRR as a function of maximum and resting HRs. After adjusting for univariately significant predictors of mortality, HRR, age, exercise capacity, and maximum HR were chosen in order as the best predictors of mortality. Conclusion: Even though the computational models of HRR and the determination of HRR at different time intervals were significant predictors of mortality, the simple discrete measure of HRR at 2 min was the best predictor of mortality. At 2 min, HRR outperformed age, METs, and maximum exercise HR in predicting all-cause mortality. Key words: heart rate recovery, autonomic nervous system, exercise testing, exercise physiology, mathematical models, heart rate reserve, exercise capacity Introduction In recent studies, the drop in heart rate (HR) during recovery from exercise (termed heart rate recovery, or HRR) has been validated as a marker of risk for mortality. Despite differences in study populations and methodology, the direct measurement of the drop in HR has consistently been predictive of death. has been shown to be prognostic at 1, 2, and 5 minutes after exercise in different populations referred for exercise testing. 1 6 However, the drop in HR as a function of the resting and maximum HR or as a more complex mathematical relationship to time has not been explored for its prognostic value. Since exercise testing, along with the recovery period, is a continuous process, it seems logical that continuous mathematical functions of HRR would be superior to measurements based on single time points. In this study, the prognostic values of HRR at individual minutes versus HRR as a function of resting and maximum HR during exercise in a clinically referred population were compared. We aimed first to define the model that best fit HRR, and then to demonstrate the predictive value of HRR kinetics compared with HRR at individual minutes from the end of peak exercise and the slope of the drop in HR during recovery. Dr. Hadley is Vice President of Research for Cardiac Science, manufacturer of the stress-test systems used in this study. He receives his salary (significant) from Cardiac Science and holds stock and stock options (significant) in that company. Address for reprints: V. Froelicher, M.D. 381 Miranda Ave. Cardiology Section, 111-C VA Palo Alto Health Care System Palo Alto, CA 9434, USA dmitrygorelik@yahoo.com Received: November 3, 26 Accepted with revision: March 31, 26 Methods Population A total of 8, male patients underwent treadmill testing at two Veteran Affairs Medical Centers between 1987 and Of these, 3,454 underwent coronary angiography within 3 months of treadmill testing. Patients with previous cardiac surgery or angiography, valvular heart disease, left bundle-branch block, paced rhythms, or Wolff-Parkinson-White syndrome on the resting electrocardiogram (ECG) were excluded from the study. The remaining 2,193 patients were analyzed with respect to angiographic disease, ECG changes, medication use, and clinical status.

2 4693b-gorelik 8/14/6 11:15 AM Page 4 4 Clin. Cardiol. Vol. 29, September 26 Exercise Testing Statistical Analysis Patients underwent symptom-limited treadmill testing using the U.S. Air Force School of Aerospace Medicine 7 or an individualized ramp treadmill protocol 8 based on a pretest symptom questionnaire. 9 Patients did not perform a cool-down walk, but were placed supine as soon as possible after exercise. Standard criteria for termination were employed. 1 The Borg 6-2 Perceived Exertion Scale was used to quantify degree of effort. Visual ST-segment depression was measured at the J junction and corrected for preexercise ST-segment depression. Blood pressure was taken manually, and metabolic equivalents (METs) were estimated from treadmill speed and grade. Agepredicted METs, based on a nomogram for Veteran patients, was calculated as METs ( age). The percentage of age-predicted METs achieved was calculated as achieved METs divided by age-predicted METs. Age-adjusted predicted maximum heart rate, based on a nomogram for Veteran patients, was calculated as age. 11 Heart rate was measured supine, standing, during each minute of exercise, at maximum exercise, and in recovery at 1, 2, 3, and 5 minutes. was defined as peak heart rate minus the heart rate at specified time periods after exercise. Double product (HR systolic blood pressure [SBP]) and delta values (maximal rest) were calculated. The METs achieved by each patient exercising on the individualized ramp protocol were converted to exercise time per the Bruce protocol and inputted into the Duke Treadmill Score equation. Heart Rate Kinetics Mathematical functions were used to model HRR as a function of resting (HR Rest ), maximum HR (HR Peak ), and time (t). The slope of the HRR curve was also analyzed. For instance, the slope of the recovery curve at 2 min was computed as the difference between the HR at 2 min minus the HR at 1 min. Coronary Angiography Coronary artery narrowing was visually estimated and expressed as percent lumen diameter stenosis. Patients with a 5% narrowing were considered to have significant angiographic coronary artery disease (CAD). Severe disease was considered to be double-vessel disease if the proximal left anterior descending was involved; otherwise, triple-vessel or left main disease were considered severe. In addition, the Duke coronary artery jeopardy score was calculated. 12 Ejection fraction was estimated from biplane left ventricular angiograms. A low ejection fraction was defined as < 5%. Follow-Up The social security number was used to match patients names to the Social Security Death Index. Death status was determined as of December 24 and was 1% complete. No other information regarding hospitalizations, cardiac interventions, or cause of death during the follow-up was available. NCSS software (NCSS Statistical Software, Kaysville, Utah, USA) was used for all statistical analyses. Unpaired t-tests were used to compare continuous variables and chisquare tests to compare dichotomous variables. All-cause mortality was used as the endpoint for follow-up, and all survival analyses were age-adjusted. Survival analysis was performed using univariate Cox proportional hazards analysis to demonstrate which variables were independently and significantly associated with death. These variables were further analyzed to determine the best determinants of death in multiple Cox proportional hazards analysis. Results Demographics This male population had a mean age of 59 ± 1 years, and a mean body mass index of 28 ± 9 kg/m 2 (Table I). A total of 68 deaths occurred during a mean follow-up of 1 years, and the average annual mortality was 2%. Patients who died were older and more likely to have had preexisting cardiac disease (Q wave on ECG or history of previous myocardial infarction [MI]), history of typical angina, heart failure (HF), history of stroke, chronic obstructive pulmonary disease (COPD), a lower ejection fraction, more ECG abnormalities, and were more likely to be receiving digoxin. There were no differences in the extent of disease in the individual vessels, but the Duke Jeopardy Score, along with the number of diseased vessels and the severity of overall coronary disease, was greater in those who died (p <.1). Exercise Test Responses In univariate analysis, patients who died achieved a lower peak exercise capacity (5.8 vs. 7.5 METs; p <.1) (Table II). They demonstrated a lower maximum SBP and had a lesser change in HR and double product. There were no differences in maximum heart rate achieved, percentage of age predicted maximum HR achieved, percent of age-predicted maximum HR, peak perceived exertion, ST depression, or silent ischemia among patients who survived versus those who died. After adjusting for age, those patients who achieved a lower exercise capacity had an increased risk of death (adjusted HR per MET.85; p <.1). In addition, the change in HR from rest to peak exercise was predictive of death. The double products at rest and exercise, along with maximum SBP, were also statistically significant predictors of death but overlapped so greatly that they were of little clinical significance. After adjusting for age, the Duke Treadmill Score was not a significant predictor of mortality. Heart Rate Recovery and Recovery Slope There was a significant difference in the drop in HR from peak exercise at 1-, 2-, 3-, and 5-min recovery among surviv-

3 4693b-gorelik 8/14/6 11:15 AM Page 41 D. D. Gorelik, et al.: Predicting death using heart rate recovery 41 TABLE I Baseline clinical characteristics of 2,193 patients classified by mortality Total patients Survivors Nonsurvivors n = 2,193 n = 1,585, 72% n = 6 8, 28% p Value Demographic data Age 59.4 ± ± 1.1 (1,58) 63.3 ± 8.7 (66) <.1 BMI 28. ± ± ± Previous MI (%) 81 (36.5) 531 (33.5) 27 (44.4) <.1 History of typical angina (%) 853 (39.1) 574 (36.5) 279 (46.) <.1 Heart failure (%) 19 (5.) 55 (3.5) 54 (8.9) <.1 COPD (%) 135 (6.2) 71 (4.5) 64 (1.5) <.1 Stroke (%) 86 (3.9) 42 (2.7) 44 (7.2) <.1 Hypertension (%) 1151 (52.6) 825 (52.1) 326 (53.6).53 Claudication (%) 192 (8.8) 126 (8.) 66 (1.9).3 Smoking (%) 752 (32.3) 532 (33.6) 22 (36.2).26 Diabetes (%) 337 (15.4) 233 (14.7) 14 (17.1) <.17 Angiographic data LAD disease (%) 729 (33.3) 548 (34.6) 181 (29.8).31 LCx disease (%) 66 (27.6) 453 (28.6) 153 (25.2).11 RCA disease (%) 696 (31.8) 59 (32.1) 187 (3.8).53 Any CAD (%) 1,537 (7.2) 1,52 (66.5) 485 (79.8) <.1 Severe CAD (%) 65 (29.7) 42 (25.4) 248 (4.8) <.1 Duke Jeopardy Score 4.4 ± ± ± 4.1 <.1 Low ejection fraction (%) 274 (14.3) 156 (11.4) 118 (21.6) <.1 Mean ejection fraction 62 ± ± 12 6 ± 16 <.1 Medications Digoxin (%) 96 (3.1%) 45 (2.8%) 51 (8.4) <.1 Beta blocker (%) 743 (33.9) 549 (34.7) 194 (31.9).22 ECG data LVH (%) 65 (3.) 27 (1.7) 38 (6.3) <.1 Q wave (%) 441 (24.3) 28 (17.7) 161 (26.5) <.1 Resting ST depression (%) 391 (17.8) 252 (15.9) 139 (22.9) <.1 Right bundle (%) 69 (3.1) 37 (2.3) 32 (5.3) <.1 Abbreviations: BMI = body mass index, MI = myocardial infarction, COPD = chronic obstructive pulmonary disease, LAD = left anterior descending, LCx = left circumflex, RCA = right coronary artery, CAD = coronary artery disease, ECG = electrocardiographic, LVH = left ventricular hypertrophy. ing versus non surviving patients (Table III and Figure 1). The slope of HRR during the second minute of recovery was greater among those who survived versus those who died (21.3 vs beats/min; p <.1). The slope did not differ among surviving and nonsurviving patients during the 3 min of recovery and the slope of HRR from 5 to 3 min. After adjusting for age, HRR at 2 min was predictive of mortality (hazard ratio.96, CI 95%,.96.97, p <.1); HRR at 1 min, 3 min, and 5 min were also predictors of mortality. Only the decrease in HRR during the second minute of recovery or the slope at 2-min recovery was predictive of mortality (hazard ratio.98, CI 95%,.97.99, p <.1) compared with the drop in HRR at other intervals. Best Fit to Recovery Curve In examining the shape of the recovery curve it was found that function (a) HRR = HR Rest + (HR Peak HR Rest ) e kt systematically failed to capture the actual character of the data. However, the functional form (b) HRR = HR Rest + (HR Peak HR Rest ) e kt2, where time (t) is squared, provided an excellent fit to the average recovery character, correctly capturing the behavior at both short and longer times during recovery. As with function (a), the derived constant k characterizes the rapidity of recovery, larger values relating to faster recovery. These models are plotted in Figure 2 along with the actual average values at each time point during recovery. The curve of model (b) closely fits the averages at each point of recovery. Figure 3 compares the actual data points of the whole population and the two subgroups (dead/alive) with the derived model (b) for the same subset populations. The actual data values are fit nearly perfectly by this function for both survivors and nonsurvivors. The HRR kinetic equations describing HRR as a function of maximum heart rate and resting heart rate were able to separate surviving and nonsurviving patients (p <.1). After

4 4693b-gorelik 8/14/6 11:15 AM Page Clin. Cardiol. Vol. 29, September 26 TABLE II Exercise testing responses comparing survivors and nonsurvivors Total patients Survivors Nonsurvivors n = 2,193 n = 1,585, 72% n =6 8, 28% p Value Rest Standing systolic BP 125 ± ± 2 127± Standing HR 77 ± 15 76± 15 78± 15.2 Double product rest 9,623 ± 2,424 9,527 ± 2,392 9,873 ± 2,492.3 Peak exercise METs 7. ± ± ± 2.5 <.1 % Age predicted METs (%) <.1 Max HR 127 ± ± ± 2.52 % Age predicted max HR (%) Maximal systolic BP 164 ± ± ± 3.2 Double product exercise 21,14 ± 6,251 21,44 ± 6,334 19,97 ± 5,894 <.1 Angina occurred (%) 589 (26.9) 432 (27.3) 157 (25.8).48 Angina reason for stopping (%) 39 (14.1) 23 (14.5) 79 (13.).86 Silent ischemia (%) 128 (5.8) 86 (5.4) 42 (6.9).19 Exercise-induced ST depression ( 1mm) (%) 674 (3.7) 472 (29.8) 22 (33.2).67 Duke Treadmill Score 1.4 ± ± ± 7.35 <.1 Borg scale 16.6 ± ± ± Delta HR 49.7 ± ± ± 19. <.1 Abbreviations: BP = blood pressure, HR = heart rate, MET = metabolic equivalent. TABLE III and recovery kinetics in surviving versus nonsurviving patients Total Survivors Nonsurvivors n = 2,193 n = 1,585, 72% n = 68, 28% p Value 1 min 12.5 ± ± 7.8 (158) 1.4 ± 7. (68) <.1 2 min 33. ± ± 12.5 (1583) 27.8 ± 12.4 (68) <.1 3 min 41.5 ± ± 14.1 (1583) 36.9 ± 14.5 (68) <.1 5 min 44. ± ± 14.8 (1583) 39.8 ± 15. (68) <.1 slope 2 min 1 min 2.5 ± ± 1.5 (1583) 18.3 ± 9.5 (68) <.1 3 min 2 min 8.5 ± ± 8.2 (1583) 9.1 ± 8.5 (68).3 5 min 3 min 2.4 ± ± 5.7 (1583) 2.8 ± 4.9 (68).54 kinetics e_ktt.41 ± ± ±.23 <.1 HRR ± ± ± 1.8 <.1 HRR ± ± ± 9.6 <.1 Abbreviations: HRR = heart rate recovery, e_ktt = least squares derived value of the decay coefficient k, HRR1 = HR rest + ( HR peak HR rest) e kt2, HRR2 = HR rest + ( HR peak HR floating rest) e kt2, floating rest = derived post-test estimate of the resting heart rate. Delta heart rate from peak exercise Min of recovery FIG. 1 Comparison of the actual average of the data points for survivors and nonsurvivors (with the data points connected by a simple plot-smoothing routine). = Dead, = survived, = total. adjusting for age, both functional equations describing the recovery period were predictive of mortality. A low value for the functional form (b) showed an increased risk of death (hazard ratio.98, CI 95%,.98.99, p <.1). Clinical Determinants of Mortality In a multivariate model adjusting for clinical variables determined to be prognostic markers by univariate analysis, age emerged as the strongest predictor of death (adjusted relative risk [RR]1.5; p <.1). Other significant clinical determinants were a previous history of stroke, ECG evidence of right bundle-branch block or left ventricular hypertrophy, a low ejection fraction, a lower Duke Jeopardy Score, and a history of MI or chronic obstructive pulmonary disease.

5 4693b-gorelik 8/14/6 11:15 AM Page 43 D. D. Gorelik, et al.: Predicting death using heart rate recovery 43 Delta heart rate from peak exercise Exercise Testing Determinants of Mortality In a multivariate model with exercise and HRR variables, independent predictors of mortality were an abnormal HRR at 2 min (hazard ratio.97; 95% CI,.96.98; p <.1), age (for every 1 additional years, RR 1.3; 95% CI, ; p <.1), and the level of exercise achieved (for each additional MET, RR.85; 95% CI,.82.89; p <.1). (Table IV). Even after adjusting for clinical determinants of mortality, HRR at 2 min remained the best exercise-related predictor of mortality. Discussion models (a) versus (b) Min of recovery FIG. 2 Comparison of the two models to the actual average of the data points. Note that model 2 exactly fits the averages at each time point of recovery. = Data, --- = time (t), = t 2. Most studies have focused on the HR at specific times during recovery and have not considered information potentially contained in the entire recovery time history. Other investigators have theorized that the transition process from sympathetic control of the heart at peak exercise to vagally mediated HR at rest controls the shape of the HRR curve and provides insights into vagal tone and risk. This study examined these and other diverse metrics to assess their ability to predict mortality. The HRR curve continuously changes slope, from an initially steep or rapid decline to a gradual and lower value as the HR approaches the resting rate. Slope, defined as the difference in HR between two points in time, was examined as an alternative way to characterize HRR. Only the slope of HRR during the second minute of recovery predicted mortality in univariate analysis. However, after adjusting for other exercise test markers and HRR variables, determination of HRR slope at 2 min failed to predict an increased risk of death. Delta heart rate from peak exercise FIG. 3 Comparison of the average heart rate recovery values and the recovery curves associated with the function (b) time squared model. Notice that the actual data point average values is fit nearly perfectly by the function for the total population and the subgroups of nonsurvivors and survivors. = Dead, = survived, = total, ---- = dead t 2, = survived t 2, = total t 2. Conceptually, a functional curve fit to the entire HRR time history offers the possibility of both minimizing the error associated with a single estimate and leveraging information that may be contained in the overall shape of the curve. Previous investigators have proposed an exponential curve of the general form: (a) Min of recovery HRR = HR Rest + (HR Peak HR Rest ) e kt to characterize the recovery process. 13 Larger values of k, the decay coefficient, result in more rapid return to the resting HR. This functional form can be fit to the recovery data utilizing a value of k that minimizes the error of the fit in a leastsquares sense. The derived parameter k provides a characterization of the recovery process that may be statistically assessed as a discriminate for risk assessment. Unfortunately, this study has found no significant separation of the population with regard to mortality based upon the derived value of k. In examining the shape of the recovery curve, it was found that function (a) systematically fails to capture the actual character of the data. The average recovery curve has more of a z shape than the simple exponential decline curve. These observations led to a search for a better functional form that captures the shape of the average recovery curve. We have discovered that the functional form (b) HRR = HR Rest + (HR Peak HR Rest ) e kt2, TABLE IV Multivariate predictors of mortality among exercise testing variables and recovery markers Predictors of mortality Hazard ratio CI (95%) p Value Wald X 2 HRR at 2 min < Age <.1 25 METs <.1 39 Abbreviations: HRR = heart rate recovery, CI = confidence interval.

6 4693b-gorelik 8/14/6 11:15 AM Page Clin. Cardiol. Vol. 29, September 26 where time t is squared, provides an excellent fit to the average recovery character, correctly capturing the behavior at both short and longer times during recovery. One complexity arises in fitting a functional form to the recovery data: normal HR variability assures that the heart does not return to the same resting value at the end of a vigorous exercise protocol as was observed prior to the start of the test. This can introduce bias into the curve fitting process if the curve is expected to return to the preexercise value of the resting HR. The curve fitting for function (b) allowed both k and the resting HR to be variables in the inversion (overdetermined with two unknowns and five data points at peak, 1, 2, 3, and 5 min). In Table III, the parameter e_ktt is the least squares derived value of k for function (b). Equation (b) contains the term (HR Peak HR Rest ), often referred to as the HR reserve. Failure to achieve 8% of the expected HR reserve, termed chronotropic incompetence by Obenza et al., 14 has been shown to be predictive of increased mortality, larger values indicating lower risk. This suggests that an ad hoc parameter, combining both the derived k from function (b) with the measured HR reserve, may provide a useful combined integrated discriminate. As risk decreases with increasing k and increasing HR reserve, we propose the simple product of these two terms: (HR Peak HR Rest ) k. The parameter HRR1 in Table III is this product, where the resting HR is the actual pretest resting HR. A second parameter in Table III, HRR2, uses the derived post-test estimate of the resting HR. These mathematical models defining HRR as a function of peak HR and resting HR were able to predict mortality in univariate analysis. However, after adjusting for other exercise testing markers and HRR variables, HRR at discrete intervals outperformed these complex mathematical measurements of HRR. The simple determination of HRR at 2 min was the best predictor of mortality. In addition, HRR at 2 min outperformed other clinical and exercise testing markers to predict the risk of death, including previously established determinants of mortality, age, and exercise capacity. Despite HRR at 2 min outperforming mathematical models, the concept of HRR at discrete intervals must be reexamined. Our institution has consistently found HRR at 2 min to be the best predictor with the ramp protocol in our Veteran population, while the Cleveland Clinic group has established HRR at 1 min to predict mortality utilizing the Bruce protocol. The aim of this study was not to examine the difference between these two populations or protocols. is a continuous process spanning the entire postexercise period. In spite of clinical studies demonstrating the benefit of obtaining information at distinct intervals, researchers as well as clinicians need to reconsider their view of HRR. Exercise and the recovery period, rather than discrete intervals, must be examined as a continuous process to obtain the most useful information from exercise testing in the future. Conclusion We developed a mathematical model to best fit the decline in HRR based on the shape and slope of the recovery curve. Even though the model was able to predict mortality in univariate analysis, the simple discrete measure of HRR at 2 min was the best predictor of mortality. Despite continuing efforts to model HRR as a continuous process, the simple determination of HRR at discrete intervals should be measured during exercise testing to aid in assessing prognosis. References 1. Kannel WB, Kannel C, Paffenbarger RS Jr, Cupples LA: Heart rate and cardiovascular mortality: The Framingham Study. 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