Comparison of Blood Pressures obtained with the Pulse Decomposition Algorithm and Intra-Arterial Catheters in ICU Patients Introduction The object of the work presented here was to validate a new approach to tracking blood pressure that is based on the pulse analysis of the peripheral arterial pressure pulse. The approach, referred to as the Pulse Decomposition Analysis (PDA) model, goes beyond traditional pulse analysis by invoking a physical model that comprehensively links the components of the peripheral pressure pulse envelope with two reflection sites in the centralarteries. The first reflection site is the juncture between thoracic and abdominal aorta, which is marked by a significant decrease in diameter and a change in elasticity. The second site arises from the juncture between abdominal aorta and the common iliac arteries. The PDA model integrates and goes beyond the findings of a number of studies that have confirmed the existence of the two reflection sites. [Kriz 2008, Latham 1985] A consequence of these reflection sites are two reflected arterial pressure pulses, referred to as component pulses, which counter-propagate to the direction of the single arterial pressure pulse, due to left ventricular contraction, that gave rise to them. The scenario, sketched in Figure 1, has been described in detail elsewhere. [Baruch 2011] In the arterial periphery, and specifically at the radial or digital arteries, these reflected pulses, the renal reflection pulse (P2, also known as the second systolic pulse) and the iliac reflection pulse (P3), arrive with distinct time delays. In the case of P2 the delay is typically between 70 and 140 milliseconds, in the case of P3 between 180 to 450 milliseconds. Quantification of physiological parameters is accomplished by extracting pertinent component pulse parameters. In the case of the beat-by-beat tracking of blood pressure the PDA model’s predictions and previous experimental studies have shown that two pulse parameters are of particular importance. The ratio of the amplitude of the renal reflection pulse (P2) to that of the primary systolic pulse (P1) tracks changes in beat-by-beat systolic pressure. The time difference between the arrival of the primary systolic (P1) pulse and the iliac reflection (P3) pulse, referred to as T13, tracks changes in arterial pulse pressure. Patients and Methods In these experiments, approved by the University of Virginia Medical Center Review Board, the arterial blood pressures of patients (23 m/11 f, mean age: 44.05 y, SD: 13.9 y, mean height: 173.3 cm, SD: 9.4 cm, mean weight: 95.3 kg, SD: 27.4 kg) hospitalized in the University of Virginia Medical Intensive Care Unit (MICU) were monitored using radial intra-arterial catheters, while the CareTaker system collected pulse line shapes at the lower phalange of the thumb. The overlap recording sessions with both monitoring systems were scheduled for four hours but were frequently shorter because of medical procedures. BedMaster (Excel Medical, Jupiter, FL) hardware and software was used to digitize and record intra-arterial waveform data from the GE Unity network at a sample rate of 240 Hz. Consent for data collection was obtained for 60 patients, and 34 successful data overlaps between arterial catheter and CareTaker were obtained. Eleven arterial data sets were lost because of a programming error in the BedMaster software, causing data to be overwritten when the overall arterial catheter recording session extended beyond one day after the CareTaker/arterial catheter overlap recording window. In 4 cases the BedMaster system was found to have been inoperative during the recording window. In 4 cases the arterial catheter recordings did not include the overlap recording window for unknown reasons, while in 4 additional cases the arterial catheter failed. Two cases involved such substantial movement artifacts due to movement, extubation etc. that neither system was able to obtain valid recordings. In one case the CareTaker device became accidently disconnected early in the session. A. CareTaker Device and PDA model The hardware platform, which is the Care-Taker device (Empirical Technologies Corporation, Charlottesville, Virginia) the model, and the algorithm implementation have been described in detail elsewhere [Baruch]. B. Statistical analysis We present regression coefficients and linear fits between arterial catheter blood pressures and the PDA parameters P2P1 and T13 for an individual patient, histogram distributions of the slopes of the linear fits for individual patients, as well as overall linear fit-based comparisons between central catheter blood pressures and blood pressures obtained from the PDA parameters using a single set of conversion constants. Bland-Altman comparisons of the two sets of blood pressures are also provided. C. Data Inclusion Data were excluded from analysis on the following basis: In the case of the arterial catheter data the criteria for exclusion were as follows: A. visual inspection of the data was used to identify sections of obvious catheter failure, characterized by either continuous or pervasive intermittent analog/digital readings of -/+32768. B. sections contaminated by excessive motion artifact were identified as such if the peak detection algorithm was no longer able to identify FactorTime (Seconds) heart beats, as evidenced by inspection of the resulting implausible and discontinuous inter-beat interval spectrum. In the case of the CareTaker data a Fourier spectral analysis approach was used to establish a signal/noise factor (SNF) to identify poor quality data sections. To this end the standard deviations and integrated amplitudes of different spectral bands were calculated. The band associated with the physiological signal was chosen from 1-10 Hz, based on data by the authors and published results by others. The signal strength associated with this band was compared with those of the 100-250 Hz frequency band, which is subject to ambient noise but contains no physiologically relevant signal. The spectrogram (top graph) displayed in figure 10 provides a motivation. Data sections characterized by low noise feature low high-frequency spectral amplitudes and high low-frequency spectral amplitudes as well as “structured” physiological bands, i.e. significant amplitude variations between the harmonic bands. This motivates the analytical form of the SNF parameter, which is established by taking the ratio A-line DiastoleTime (Seconds) of the standard deviation of the 1-10 Hz band to the product of the integrated band strengths of the 1- 10 Hz and 100-250 frequency bands. The bottom graph of Figure 2 displays the SNF parameter for the raw CareTaker data presented in the center graph of Figure 2. Data sections with an SNF below 80 were excluded from the analysis. Results The overlap of the CareTaker data streams and the central catheter data streams was established, after an initial alignment based on data collection systems clocks, by matching time-based inter-beat interval spectra obtained via pulse detection obtained from both data streams. Figure 3 presents an example of such an overlap for patient 21. The figure presents both an overall overlay of the data streams, as well as a 70 second expanded section. The linear model used to perform the conversion of the P2P1 parameters to systolic blood pressure for patient 21 was (140 × P2P1 (unitless ratio) + 59.2), while the corresponding linear conversion for the T13 parameter to pulse pressure was (0.1 × T13 (milliseconds) + 13.6). Both the offsets and the slope factors were patient-specific and obtained by chi^2 minimization of the fit of both data streams. The details of obtaining the slope factor and the offset for individual patients, as well as the physiological relevance of the slope factor regarding arterial stiffness, are discussed later. In figures 9 and 10 we present the correlation and Bland-Altman results for the interbeat interval data for patient 21, which was presented in Figure 2. The data presented in the Bland-Altman plot of figure 9 demonstrates the resolution limits of the data streams, which presents itself as diagonally-running striations that are visible in the data. Had the data rate in both data streams been equal, the striations would run at 45 degrees. In the case of the CareTaker data, the acquisition rate was 500 Hz, or a data point spacing of 2.0 milliseconds, while the catheter-based data was collected at 240 Hz, corresponding to a data point spacing of 4.16 milliseconds. Consequently the slope of the striations is 0.48, the ratio of lower to higher data acquisition rate. Overall Blood Pressure Results The overall results of the study are presented as blood pressure correlations and Bland-Altman graphs in figures 11 through 14. The data comparisons are based on two-second averages of 40-minute sections from each patient. This approach is an established procedure that has been used in previous presentations of comparative blood pressure data by others. 1 One important benefit of this approach is that it gives equal weight to the data set of each patient 1 Martina JR et. al., Noninvasive continuous arterial blood pressure monitoring with Nexfin, Anesthesiology. 2012 May;116(5):1092-103. Average Inter-beat Interval (Seconds)) mean: -0.056 millisecondsSD: 6.0 milliseconds larger standard deviation observed here is affected by two factors: 1. the comparatively low sampling rate of the catheter signal, 240 Hz corresponding to 4.2 milliseconds, and 2. the significantly broader structure of the blood pressure pulse compared to the EKG signal. The first limits the temporal resolution of peak detection, while the second limits the threshold accuracy with which a given peak can be detected relative to other peaks, likewise introducing uncertainty into the temporal accuracy.