Accuracy of algorithms for detection of atrial fibrillation from short duration beat interval recordings.

Atrial fibrillation (AF) is characterised by highly variable beat intervals. The aims of the study were to assess the accuracy of AF detection algorithms from short analysis durations and to validate prospectively the accuracy on a large community-based cohort of elderly subjects. Three algorithms for AF detection were evaluated: coefficient of variation (CV), mean successive difference (Δ) and coefficient of sample entropy (COSEn), using two databases of beat interval recordings: 167 recordings of 300 s duration for a range of rhythms acquired in a hospital setting and 2130 recordings of 10s duration acquired in the community. Using the longer recordings receiver operating characteristic (ROC) analysis was used to identify optimal algorithm thresholds and to evaluate analysis durations ranging from 5s to 60s. An ROC area of 93% was obtained at recording duration of 60s but remained above 90% for durations as low as 5s. Prospective analysis on the 2130 recordings gave AF detector sensitivities from 90.5% (CV and Δ) to 95.2% (COSEn), specificities from 89.3% (Δ) to 93.4% (COSEn) and accuracy from 89.3% (Δ) to 93.4% (COSEn), not significantly different to those obtained on the initial database. AF detection algorithms are effective for short analysis durations, offering the prospect of a simple and rapid diagnostic test based on beat intervals alone.

Numerical simulation of the hemodynamic response to hemodialysis-induced hypovolemia.

To provide a framework for analyzing cardiovascular response to hemodialysis-induced hypovolemia, we developed a computer model which simulates arterial pressure changes caused by loss of blood volume. The model includes arterial and venous systemic circulation, Starling's law and inotropic regulation of heart, arterial and cardiopulmonary baroreflex control of capacitance, and resistance vessels. The performance of this model was assessed by analyzing the hemodynamic responses recorded in 12 patients undergoing chronic hemodialysis, 6 classified as hypotension resistant (stable group) and 6 as hypotension prone (unstable group). Arterial pressure, heart rate, and blood volume were recorded during regular hemodialysis. Blood volume and heart rate were used as inputs to the simulator whereas the arterial pressure response obtained by simulation was fitted to the measured data by tuning simulator parameters relative to the capacitance and resistance controls. Although analyzed pressure responses exhibited a wide variety of time patterns, for each one it was possible to identify an optimal set of parameters allowing the recorded pressure data to be accurately reproduced by the model. Sensitivity analysis performed with the model indicated that pressure response strongly depends on the parameter Kv accounting for the capability to control vascular capacitance. According to these results, the parameter Kv in the stable group was 9 times that of the unstable group, thereby suggesting a possible cause of their different hemodynamic behavior.