Slow QT interval adaptation to heart rate changes in normal ambulatory subjects.

BACKGROUND: Clinical formulas for QT correction utilize instantaneous HR. We showed previously that longer-term HR affects QT duration. We extend these findings, identifying more accurate models of QT behavior. METHOD: Multiple models of QT dependence on HR were tested in 2 independent populations. Holter recordings were analyzed in population A (healthy volunteers, n = 14, 6 males, age 26.9 ± 12.3 yr). The hypotheses generated in population A were tested in an independent group population B, healthy volunteers, n = 15, 9 males, age 52.9 ± 15.6 yr). Linear models of QT interval dependence on a weighted average of RR intervals in the preceding 3 minutes were compared to models based on the immediately preceding RR interval (instantaneous HR). RESULTS: In population A, linear models based on RR intervals over the preceding minute performed better than the best nonlinear model based on the single RR interval immediately preceding the QT interval. Linear models including HR values preceding the QT interval by more than 60 s further improved model fit. This model hierarchy was confirmed in population B. Linear formula for QT correction based on exponential decay of HR effect with 60 s time constant outperformed Bazett and Fridericia formulas in both populations. CONCLUSIONS: QT duration in normal ambulatory subjects is affected by noninstantaneous HR, including HR history dating back more than 60 s. Exponential decay of this "memory effect" with time constant of 1 minute provides an accurate description of QT adaptation. This may be of clinical importance when HR is not steady.

Wireless Instantaneous Neurotransmitter Concentration Sensing System (WINCS) for intraoperative neurochemical monitoring.

The Wireless Instantaneous Neurotransmitter Concentration Sensing System (WINCS) measures extracellular neurotransmitter concentration in vivo and displays the data graphically in nearly real time. WINCS implements two electroanalytical methods, fast-scan cyclic voltammetry (FSCV) and fixed-potential amperometry (FPA), to measure neurotransmitter concentrations at an electrochemical sensor, typically a carbon-fiber microelectrode. WINCS comprises a battery-powered patient module and a custom software application (WINCSware) running on a nearby personal computer. The patient module impresses upon the electrochemical sensor either a constant potential (for FPA) or a time-varying waveform (for FSCV). A transimpedance amplifier converts the resulting current to a signal that is digitized and transmitted to the base station via a Bluetooth radio link. WINCSware controls the operational parameters for FPA or FSCV, and records the transmitted data stream. Filtered data is displayed in various formats, including a background-subtracted plot of sequential FSCV scans - a representation that enables users to distinguish the signatures of various analytes with considerable specificity. Dopamine, glutamate, adenosine and serotonin were selected as analytes for test trials. Proof-of-principle tests included in vitro flow-injection measurements and in vivo measurements in rat and pig. Further testing demonstrated basic functionality in a 3-Tesla MRI unit. WINCS was designed in compliance with consensus standards for medical electrical device safety, and it is anticipated that its capability for real-time intraoperative monitoring of neurotransmitter release at an implanted sensor will prove useful for advancing functional neurosurgery.

QT interval variability and adaptation to heart rate changes in patients with long QT syndrome.

BACKGROUND: Increased QT variability (QTV) has been reported in conditions associated with ventricular arrhythmias. Data on QTV in patients with congenital long QT syndrome (LQTS) are limited. METHODS: Ambulatory electrocardiogram recordings were analyzed in 23 genotyped LQTS patients and in 16 healthy subjects (C). Short-term QTV was compared between C and LQTS. The dependence of QT duration on heart rate was evaluated with three different linear models, based either on the RR interval preceding the QT interval (RR(0)), the RR interval preceding RR(0) (RR(-1)), or the average RR interval in the 60-second period before QT interval (mRR). RESULTS: Short-term QTV was significantly higher in LQTS than in C subjects (14.94 +/- 9.33 vs 7.31 +/- 1.29 ms; P < 0.001). It was also higher in the non-LQT1 than in LQT1 patients (23.00 +/- 9.05 vs 8.74 +/- 1.56 ms; P < 0.001) and correlated positively with QTc in LQTS (r = 0.623, P < 0.002). In the C subjects, the linear model based on mRR predicted QT duration significantly better than models based on RR(0) and RR(-1). It also provided better fit than any nonlinear model based on RR(0). This was also true for LQT1 patients. For non-LQT1 patients, all models provided poor prediction of QT interval. CONCLUSIONS: QTV is elevated in LQTS patients and is correlated with QTc in LQTS. Significant differences with respect to QTV exist among different genotypes. QT interval duration is strongly affected by noninstantaneous heart rate in both C and LQT1 subjects. These findings could improve formulas for QT interval correction and provide insight on cellular mechanisms of QT adaptation.

Heart rate dependence of the QT interval duration: differences among congenital long QT syndrome subtypes.

INTRODUCTION: The heart rate dependence of QT interval duration is abnormal in patients with congenital long QT syndrome. Patients with LQT1 have a defective I(Ks) current, a major determinant of QT response to heart rate. METHODS AND RESULTS: We studied the heart rate dependence of QT interval duration in different long QT syndrome genotypes and control subjects using computerized QT measurements obtained from Holter recordings. The dependence of QT duration on heart rate is steeper in long QT syndrome than in control subjects (0.347 +/- 0.263 vs 0.162 +/- 0.083 at heart rate 100 beats/min; P < 0.05). In addition, QT interval is significantly longer in LQT2 and LQT3 than in LQT1 patients at slow (533 +/- 23 ms vs 468 +/- 30 ms at heart rate 60 beats/min; P < 0.0001) but not at rapid heart rate. The heart rate dependence of QT interval is steeper in LQT2 and LQT3 than in LQT1 (0.623 +/- 0.245 vs 0.19 +/- 0.079 at heart rate 100 beats/min; P < 0.05). For a given heart rate, the QT intervals vary more in LQT2 and LQT3 than in LQT1 patients (25.98 +/- 11.18 ms vs 14.39 +/- 1.55 ms; P < 0.01). CONCLUSION: Individual long QT syndrome genotypes differ with respect to QT interval dependence on heart rate. These differences may relate to the propensity of LQT2 and LQT3 patients to develop arrhythmias during bradycardia.