Alberto Porta

Beat-to-beat variability of the QT interval (QTV) has been used as a marker of repolarization lability and sympathetic activation. The aim of this study was to establish ECG sampling rate requirements for reliable QT interval variability measurement. We measured QTV in high resolution simulated (1000 Hz) and real ECG (1600 Hz; in the supine position during rest and during sympathetic activation upon standing), using time and frequency domain metrics as well as measures of symbolic dynamics for complexity assessment. We successively halved the sampling rate and investigated its effect on the QTV metrics. Reduction in sampling rate below 400 Hz and 500 Hz, respectively, resulted in a significant overestima-tion of QTV variability and also affected complexity measurement of QTV. QTV increased during standing compared to the supine measurement. At 100 Hz, the posture related change in QTV was completely masked by the measurement noise introduced by the low sampling rate. In conclusion, ECG sampling rates of 500 Hz yields a reliable QTV measurement, while sampling rates of 200 Hz and below should be avoided.

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocar-diograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.

Calibrated variability of muscle sympathetic nerve activity during graded head-up tilt in humans and its link with noradrenaline data and cardiovascular rhythms..—Muscle sympathetic nerve activity (MSNA) variability is traditionally computed through a low-pass filtering procedure that requires normalization. We proposed a new beat-to-beat MSNA variability computation that preserves dimensionality typical of an integrated neural discharge (i.e., bursts per unit of time). The calibrated MSNA (cMSNA) variability technique is contrasted with the traditional uncalibrated MSNA (ucMSNA) version. The powers of cMSNA and ucMSNA variabili-ties in the low-frequency (LF, from 0.04 to 0.15 Hz) band were computed with those of the heart period (HP) of systolic and diastolic arterial pressure (SAP and DAP, respectively) in seven healthy subjects (age, 20 –28 years; median, 22 years; 5 women) during a graded head-up tilt. Subjects were sequentially tilted at 0°, 20°, 30°, 40°, and 60° table inclinations. The LF powers of ucMSNA and HP variabil-ities were expressed in normalized units (LFnu), whereas all remaining spectral markers were expressed in absolute units. We found that 1) the LF power of cMSNA variability was positively correlated with tilt angle, whereas the LFnu power of the ucMSNA series was uncorrelated; 2) the LF power of cMSNA variability was correlated with LF powers of SAP and DAP, LFnu power of HP and noradren-aline concentration, whereas the relationship of the LFnu power of ucMSNA variability to LF powers of SAP and DAP was weaker and that to LFnu power of HP was absent; and 3) the stronger relationship of cMSNA variability to SAP and DAP spectral markers compared with the ucMSNA series was confirmed individually. The cMSNA variability appears to be more suitable in describing sympathetic control in humans than traditional ucMSNA variability. heart rate variability; arterial pressure variability; MSNA; cat-echolamines; autonomic nervous system; cardiovascular control SPECTRAL ANALYSIS OF SPONTANEOUS cardiovascular variability allows the indirect, noninvasive, inference of the autonomic function in humans (1, 20). The suitability of the spectral indexes derived from the beat-to-beat variations of the heart period (HP), of systolic and diastolic arterial pressure (SAP and DAP, respectively) has been validated by assessing their correlation with spectral indexes derived from direct, invasive, microneurographic recordings of muscle sympathetic nerve activity (MSNA) (5, 11, 28) during sympatho-inhibitory and/or sympatho-excitatory maneuvers (4, 10, 18, 21, 26). The link between MSNA and arterial pressure (AP) is widely recognized with MSNA burst rate increasing when AP falls and MSNA becoming silent when AP rises as a result of an operating baroreflex that inhibits sympathetic drive (29), even though central mechanisms such as the entrainment of a central oscillator by pulse-synchronous baroreceptor nerve activity cannot be excluded (2). Baroreflex-mediated modifications of the sympathetic drive are responsible for the changes in HP, and they partially explain the link between MSNA and HP variations (7). Traditionally, validation of the link between MSNA and modifications of HP and AP (mainly SAP and DAP in cardiovascular variability studies) was carried out after applying a suitable low-pass filtering procedure to the MSNA signal that retains the range of frequencies typical of cardio-vascular variability (i.e., from 0 to 0.5 Hz) (4, 10, 18, 21, 26). Because the power of the MSNA signal in the low-frequency (LF, from 0.04 to 0.15 Hz) band is significantly and positively correlated with the LF power of HP variability when expressed in normalized units and with the LF power of SAP variability when expressed in absolute units, this suggests that HP and SAP spectral indexes, when expressed in suitable units, may provide insight into autonomic regulatory mechanisms (21). Because the spectral indexes that describe MSNA variability are traditionally computed directly from a low-pass-filtered version of the MSNA signal (4, 10, 13, 18, 21, 26, 27, 31), these markers quantify the magnitude of changes in burst amplitude and area about their mean value more than the variations in their rate of occurrence. In other words, the variability series of MSNA, as currently derived from the MSNA signal, has the same physical dimension of the MSNA signal (i.e., mV) and does not have the physical dimension of a neural discharge (i.e., bursts per unit of time). As a consequence, the spectral indexes derived from MSNA variability depend on numerous factors including number of active fibers, proximity to the recording electrode to the bundle, operator's experience in picking the nerve up, acquisition system settings (e.g., the gain of the neural traffic amplifier), and level of noise superimposed on the MSNA signal. These factors increase