Julio C. Spinelli

Experimental validation of pulse contour methods for estimating stroke volume at pacing onset

Stroke volume (SV) might be an important variable in assessing cardiac function of patients under pacing therapy. To assess the accuracy of estimating SV changes from aortic pressure during VDD pacing, we implanted electromagnetic flowmeters in the aortic root of five mongrel dogs. Three pacing sites, including right ventricle, left ventricle and both ventricles were selected and paced at 5 atrioventricular delays. Each pacing site/AV delay combination was repeated 5 times in a randomized order. The protocol alternated 5 paced beats and 15 intrinsic beats. Flow and aortic pressure data were recorded and analyzed. We evaluated five pulse contour equations relating pulse pressure (PP), ejection time (ET), diastolic time (DT) and aortic pressure area during systole over the end-diastolic pressure (Psa). A good correlation (r=0.84-0.89) was shown between percent change in SV and percent change in PP, PP*ET, Psa, Psa*ET and Psa*(1+ET/DT) over the preceding baseline. The average estimated percent change in SV also correlated with the average measured percent change in SV for each pacing site/AV delay combination. In conclusion, PP, PP*ET, Psa, Psa*ET and Psa*(1+ET/DT) may be clinically applicable for assessing SV at the start of VDD pacing and may effectively predict the effects of pacing sites and AV delays on SV.

The Optimal Pacing Rate: An Unpredictable Parameter

A three phase relation has been demonstrated between increasing heart rate and cardiac output at rest. Phase I with cardiac output increasing with increasing heart rate, phase II a plateau, and phase III decreasing cardiac output with any further increase in heart rate. The “optimal rate” can be defined as the heart rate at the onset of phase II. Twenty patients were studied, 13 male, mean age 60 years (range 31–71 years). All had chronic complete heart block and established DDD pacing. A maximal exercise test was performed to determine peak sinus rate. Exercise hemodynamics were measured using an ambulatory monitor (Capintec Vest), which permits measurement of relative cardiac output and relative ejection fraction. The patients were programmed to VVI pacing at a rate of 60 beats/min and performed three exercise tests at different workloads. The order of workloads was randomized and selected from a range (0, 25, 50, or 75 W) depending on fitness. After 3‐minute stabilization, the VVI pacing rate was increased at 1‐minute intervals until higher than peak sinus rate giving a total exercise time of 12 minutes. The “optimal rate band” was determined at each workload. The mean of this “optimal rate band” for each workload varied in a nonlinear manner. There was no correlation between “mean optimal rate” and age or the peak rate predicted by the Astrand formula. Current definitions of chronotropic incompetence are inaccurate. Are some of these people at their “optimal rate” already? The arbitrary selection of rate response curves on age related criteria may lead to an impaired hemodynamic response.

Comparison of paced and intrinsic ECG characteristics for measurement of pacing threshold

Accurate determination of pacing threshold is required for increased longevity of pacemakers and to ensure contraction of the heart. During pacing threshold measurements, surface ECG analysis is necessary for capture verification. Time and frequency domain characteristics of paced and intrinsic surface ECG events were analyzed in 12 pacemaker patients. When compared with amplitude and slew rate, the QRS area provides the best possible discrimination between paced and intrinsic events. Frequency analysis indicates that the power spectrum of a paced ECG is distributed over a shorter frequency range when compared with the intrinsic ECG event. Parameters either in the time or frequency domain or both can be utilized to develop an algorithm for automatic determination of pacing threshold.