Depression is a common psychiatric disorder in patients with coronary heart disease (CHD). Whereas the lifetime prevalence of major depression in the United States is estimated to be about 16%,1 with an annual rate of about 7%, approximately 20% of patients with CHD have major depression at any point in time.2–5 About the same proportion have minor depression.3 During the 12 months following an acute coronary event, as many as 30% of patients may develop major depression;6 the prevalence of minor depression during this 12-month period has not been reported but is also estimated to be about 30%. Thus, up to 60% of patients with an acute coronary event experience symptoms of depression within the 12 months following the event.
In addition to being highly comorbid with CHD, depression is also a significant risk factor for cardiac morbidity and mortality in patients with CHD. This risk is present from the time of initial diagnosis of CHD by cardiac catheterization and angiography7,8 as well as after an acute myocardial infarction (MI),6,9–11 an episode of unstable angina,12 or coronary artery bypass graft surgery.13–15 A recent meta-analysis of more than 20 studies of depression following acute MI found that major depression more than doubles the risk of mortality in the months following the acute event.16 Another meta-analysis found that just having symptoms of depression at various times in the course of CHD doubles the risk of death, and that clinical depression is associated with an even higher risk.17
Depression has been associated with many behavioral and biological abnormalities that could help explain the increased mortality risk in depressed patients with cardiac disease, including reduced adherence to treatment regimens, increased prevalence of smoking and diabetes, platelet dysfunction and coagulant processes, inflammatory processes, and alterations in hypothalamic-pituitary-adrenal axis and autonomic nervous system (ANS) function.18,19 Any or all of these might contribute to the increased risk for cardiac morbidity and mortality in depressed patients. Of all these possibilities, however, ANS dysfunction probably has received the most attention.20 Excessive sympathetic or reduced parasympathetic nervous system activity in patients with CHD may promote myocardial ischemia, ventricular tachycardia, ventricular fibrillation, and even sudden cardiac death.21–23
Studies dating back to the 1960s have found plasma and urinary catecholamine levels and resting heart rate (HR) to be elevated in medically well psychiatric patients with major depression compared with nondepressed controls.24–30 Studies of patients with CHD have also found elevated resting and 24-hour HRs in depressed compared with nondepressed patients.31,32 Additional evidence of ANS dysfunction in depressed CHD patients includes increased HR response to orthostatic challenge;32 increased QT interval variability, reflecting abnormal ventricular repolarization;33 abnormal HR response to ventricular arrhythmias (turbulence);34 and an increased incidence of ventricular tachycardia.35 All of these factors have been related to ANS dysfunction, and all are predictors of mortality in cardiac patients.
Many, though not all, studies of medically well depressed psychiatric patients have also found reduced HR variability (HRV), reflecting abnormal ANS modulation of HR. Low HRV is an excellent predictor of cardiac-related mortality36–39 and thus may further help to explain the relationship of depression to increased risk of mortality.
MEASUREMENT OF HEART RATE VARIABILITY
Analysis of HRV is a widely used method for studying cardiac autonomic modulation of HR.36 Low HRV generally reflects excessive sympathetic and/or inadequate parasympathetic modulation of HR36 and is a strong predictor of mortality in patients with CHD.37–39
Three methods of deriving HRV
In large prognostic or epidemiologic studies, HRV is usually measured over a 24-hour period and is derived from electrocardiographic (ECG) data by one of three methods: time domain analysis, frequency domain analysis, and nonlinear statistical models.
Time domain indices are based on descriptive statistical analyses of the HR time series. These include the standard deviation of all normal-to-normal intervals (SDNN) and the root mean square of successive N-N differences (rMSSD).
Frequency domain indices. Fast Fourier transforms and spectral analyses of ECG data are used to characterize HRV in the frequency domain. Frequency domain indices are defined by specific frequency ranges:
- Ultra low frequency (ULF;
- Very low frequency (VLF; 0.0033 to 0.04 Hz)
- Low frequency (LF; 0.04 to 0.15 Hz)
- High frequency (HF; 0.15 to 0.4 Hz).
These indices are usually log-transformed to produce approximately normal distributions. Efferent vagal activity is largely responsible for the HF component, whereas LF power seems to reflect both sympathetic and parasympathetic activity.36 There is less certainly about the contributions to ULF.36 While not completely understood, VLF power is known to be unaffected by beta-blockade but nearly abolished by atropine, suggesting that the parasympathetic nervous system is the predominant determinant of VLF.40
Nonlinear statistical models. HRV has also been characterized by nonlinear mathematical models, such as those based on chaos theory and fractals. Nonlinear methods quantify the structure of the HR time series, including its regularity and self-similarity. These indices include the short-term fractal scaling exponent and approximate entropy.