CON: Monitoring heart rate variability adds a significant incremental value in cardiovascular surgery
Jay Horrow, MD & Dmitri Chamchad, MD
Drexel University, Philadelphia, PA
Monitoring of traditional vital signs - systemic blood pressure, heart rate, respiratory rate, and temperature - provide value only insofar as they predict short- or long-term outcomes. We miss potentially valuable predictive information when limiting monitors to those four. The added value of SpO2, EEG, cardiac output, and SSEPs demonstrates this limitation. Likewise, delving beneath the surface of raw physiologic data extracts better predictors. Witness the advantage of spectral analysis of the EEG, and of ST segment analysis of ECG signals.
We thus can understand the attempt to predict outcome from how heart rate varies from beat to beat. A large volume of studies have attempted to associate heart rate variability (HRV) with diverse outcomes, including sudden cardiac death 1-5, cardiac arrhythmias 6-13, myocardial infarction 14-16 , diabetes 17,18, heart failure 19-24, COPD 25 and sleep abnormalities 26-30. Just how successfully do analyses of heart rate variability predict outcomes? The answer depends on the type of analysis and on data acquisition techniques.
Differences in signal sampling rate and in durations of recording plague attempts to compare analytic techniques across studies. In the absence of standardized signal acquisition criteria, comparisons of analytic techniques are limited to reports applying several analyses to the same data, acquired appropriately for all applied analyses.
Simple analyses of the raw ECG signal collect statistics on the R-R intervals and are termed time domain measurements. They perform relatively poorly as predictors of clinical outcomes 31. Malliani et al. 32 reviewed different human pathophysiologic conditions, demonstrating the superior predictive ability of spectral analysis, i.e., frequency domain analyses, compared to time domain summary statistics. Lombardi 33 tried to identify patients with increased cardiac mortality after myocardial infarction using time domain measures. Sensitivity and specificity were poor; positive predictive value was < 30 percent. The time domain measures results depended on the point in the recording period, leading Lombardi to speculate whether non-linear analyses could describe better this complex behavior. Other investigators also found time domain analyses inferior to more sophisticated ones. 9,10,34-37. In a few cases, discrepancies in data acquisition techniques might explain the differences.
A more sophisticated analysis transforms the time domain data to the frequency domain, in the following way. Any repetitive signal can be expressed as a series of sine waves of various frequencies, with the amplitude of each wave reflective of its relative contribution. We display the amplitude squared (power) in a power spectrum analysis. Theoretical considerations lead one to assume that the higher frequency portion has a major vagal contribution and the lower frequency component influences both sympathetic and vagal autonomic traffic 32,38-43. Such theoretical associations cannot substitute for robust outcome data. Indeed, those associations face stiff challenges 44-46. The limitation may arise from the fact that the Fourier transformation converting data from the time domain to the frequency domain assumes "stationarity" of the system generating the signal. Heart rate and other similar biological phenomena display non-stationarity. 31
Indeed, outcome studies using frequency domain analyses show only modest predictive value for a variety of outcome measures, or results that cannot be validated upon repeat study 31. Although Frost et al. 47 reported differences in power spectrum analysis measurements in patients who developed atrial fibrillation after coronary revascularization, Halaka et al. 9 could not reproduce that finding, finding similar results (p>0.10 for atrial fibrillation v. others) for time domain and power spectrum analyses. Chamchad et al. 48 likewise found no significant predictive utility of power spectra, although significance levels were between 0.05 and 0.08. Hogue et al. 10 concluded that frequency domain measures did not predict reliably the occurrence of ventricular dysrhythmias, whereas more sophisticated, nonlinear analytic measures did.
Laitio et al. studied predictors of >48 hour stay in the intensive care unit following coronary revascularization. Multivariate logistic regression identified only 2 independent predictors: APACHE II score and a nonlinear HRV measure called detrended fluctuation analysis parameter alpha-1, with alpha-1 being the more significant factor at p=0.039 (odds ratio 0.103 per 0.2 units). Time and frequency domain HRV parameters did not predict prolonged stay 49. Even in studies where frequency domain results correlated well with outcome, they proved inferior to results using more sophisticated, non-linear measures. For example, using 24 hour recordings, the nonlinear measure "approximate entropy" predicted the onset of atrial fibrillation better than more traditional HRV methods 50. What are these newer methods?
Spawned from chaos theory and fractals, non-linear measures identify high level components of information. The names of the calculated parameters reflect their abstract properties, quite divorced from readily identifiable physiologic phenomena: approximate entropy, for example, measures the degree of unpredictability in a set of data 10,36,50-54. Poincaré plot analysis uses hundreds or thousands of ordered pairs consisting of an RR interval and the RR interval of an immediately adjoining beat. The shape of the ensuing data cloud provides inferences on the stationarity of the system, with promising results 55-59. Point correlation dimension (PD2) measures the number of independent generators of information within a given system 34,60-64. In a pilot study of 22 parturients receiving spinal anesthesia for Cesaerean section, the 11 patients who developed hypotension after spinal anesthesia had the 11 lowest PD2 values (100% sensitivity and specificity); the 2 patients with bradycardia were among those same 11 64. Low and high frequency components (frequency domain measures) and their ratio did not predict hypotension. Likewise, in a multivariate logistic regression seeking associations of many HRV parameters with atrial fibrillation following coronary revascularization, PD2 (odds ratio 3.03, p=0.025) along with age and body weight, but not time domain or frequency domain measures, associated well (overall c-statistic 0.88) 48.
Current evidence suggests that these nonlinear metrics fare better than time and frequency domain analyses. However, it is too early to promote these metrics as predictors of clinical outcome. We need further work. First, we need acquisition techniques with efficient algorithms that permit real-time calculation. We also need validation of preliminary work in prospective cohorts. Most convincing of all will be demonstration of a clinical outcome advantage of substantial proportions following prospective calculation of a HRV parameter leading to intervention. Will HRV analysis ever display the same clinical impact achieved in the classic paradigm of hypotension and tachycardia --> fluid administration --> resolution of hypotension and tachycardia? Stay tuned.
[The authors acknowledge helpful advice and explanations from Dr J.E. Skinner.]
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