Siegfried Wassertheurer

Validation of a Brachial Cuff-Based Method for Estimating Central Systolic Blood Pressure

The prognostic value of central systolic blood pressure has been established recently. At present, its noninvasive assessment is limited by the need of dedicated equipment and trained operators. Moreover, ambulatory and home blood pressure monitoring of central pressures are not feasible. An algorithm enabling conventional automated oscillometric blood pressure monitors to assess central systolic pressure could be of value. We compared central systolic pressure, calculated with a transfer-function like method (ARCSolver algorithm), using waveforms recorded with a regular oscillometric cuff suitable for ambulatory measurements, with simultaneous high-fidelity invasive recordings, and with noninvasive estimations using a validated device, operating with radial tonometry and a generalized transfer function. Both studies revealed a good agreement between the oscillometric cuff-based central systolic pressure and the comparator. In the invasive study, composed of 30 patients, mean difference between oscillometric cuff/ARCSolver-based and invasive central systolic pressures was 3.0 mm Hg (SD: 6.0 mm Hg) with invasive calibration of brachial waveforms and −3.0 mm Hg (SD: 9.5 mm Hg) with noninvasive calibration of brachial waveforms. Results were similar when the reference method (radial tonometry/transfer function) was compared with invasive measurements. In the noninvasive study, composed of 111 patients, mean difference between oscillometric cuff/ARCSolver–derived and radial tonometry/transfer function–derived central systolic pressures was −0.5 mm Hg (SD: 4.7 mm Hg). In conclusion, a novel transfer function-like algorithm, using brachial cuff-based waveform recordings, is suited to provide a realistic estimation of central systolic pressure.

Noninvasive determination of carotid-femoral pulse wave velocity depends critically on assessment of travel distance: a comparison with invasive measurement.

Objectives European Society of Hypertension guidelines recommend use of carotid– femoral pulse wave velocity (cfPWV) as a favored measure of aortic stiffness. However, there is no consensus on the measurement of distance travelled by the pulse wave along the aorta to the femoral artery. The aim of our study was to compare cfPWV, calculated with commonly used noninvasive methods for travel distance assessment, against aortic PWV measured invasively. Methods One hundred and thirty-five patients had aortic PWV measured invasively during cardiac catheterization, from the delay in wave foot and distance travelled as the catheter was withdrawn from the ascending aorta to the aortic bifurcation. On the following day, noninvasive cfPWV was assessed, using the SphygmoCor system, relating the delay between carotid and femoral wavefoot to travel distance, estimated with five different methods on body surface. Results Mean travel times were in good agreement [(travel time) TTinvasive was 63 ms, TTnoninvasive was 59.3 ms, Spearmans R: 0.8, P < 0.00001]. Mean PWVinvasive was 8.5 m/s. CfPWV, as assessed noninvasively, depended largely on the method used for travel distance estimation: 11.5, 9.9, 8.7, 11.9, and 9.6 m/s, using direct carotid–femoral distance, carotid–femoral minus carotid–suprasternal notch distances, suprasternal notch–femoral minus carotid–suprasternal notch distances, suprasternal notch–femoral plus carotid–suprasternal notch distances, and suprasternal notch–symphysis distance, respectively. There was acceptable correspondence between PWVinvasive and cfPWVnoninvasive (Spearmans R: 0.73–0.77, P < 0.0001). Conclusion For noninvasive assessment of cfPWV, estimation of pulse wave travel distance is critical. Best agreement with invasive measurements was found for the method of subtracting carotid–suprasternal notch distance from suprasternal notch–femoral distance.

Wave Reflections, Assessed With a Novel Method for Pulse Wave Separation, Are Associated With End-Organ Damage and Clinical Outcomes

We recently developed a novel method for assessment of arterial wave reflections (ARCSolver method): based on adopted Windkessel methods, flow curves are estimated from pressure waveforms, and wave separation analysis is performed, yielding the amplitudes of the forward and backward waves. The aim of this study was to investigate their clinical correlates and prognostic impact. In 725 patients (417 men; mean age, 64 years) undergoing coronary angiography, we determined wave reflections from radial tonometry and transfer function-derived aortic waveforms using pulse wave analysis, as well as wave separation analysis. Measures of pulsatile arterial function were statistically significant, although moderately associated with markers of cardiac load and subclinic cardiac, renal, and aortic end-organ damage. After a median follow-up duration of 1399 days, 139 patients reached the combined cardiovascular end point (death, myocardial infarction, stroke, coronary, cerebrovascular, and peripheral revascularization). In univariate analysis, the relative risk of the combined end point increased with increasing levels of incident pressure wave height, augmented pressure, and forward and backward wave amplitude (hazard ratio for 1 SD was 1.302, 1.236, 1.226, and 1.276; P<0.01 for all, respectively). In multivariate analysis, backward wave amplitude was the most consistent predictor of the combined end point. Of note, its predictive value was independent of brachial systolic, diastolic, and mean blood pressures and was superior to brachial pulse pressure. In conclusion, the amplitude of the reflected wave, as assessed with a novel method for wave separation, is associated with hypertensive end organ damage and is an independent predictor of cardiovascular events in high-risk patients.

Oscillometric estimation of aortic pulse wave velocity: comparison with intra-aortic catheter measurements.

ObjectivesRecently, a novel method to estimate aortic pulse wave velocity (aPWV) noninvasively from an oscillometric single brachial cuff waveform reading has been introduced. We investigated whether this new approach provides acceptable estimates of aPWV compared with intra-aortic catheter measurements. MethodsEstimated values of aPWV obtained from brachial cuff readings were compared with those obtained using an intra-aortic catheter in 120 patients (mean age 61.8±10.8 years) suspected for coronary artery disease undergoing cardiac catheterization. Differences between aPWV values obtained from the test device and those obtained from catheter measurements were estimated using Bland–Altman analysis. ResultsThe mean difference±SD between brachial cuff-derived values and intra-aortic values was 0.43±1.24 m/s. Comparison of aPWV measured by the two methods showed a significant linear correlation (Pearson’s R=0.81, P<0.0001). The mean difference for repeated oscillometric measurements of aPWV was 0.05 m/s, with 95% confidence interval limits from −0.47 to 0.57 m/s. ConclusionaPWV can be obtained using an oscillometric device with brachial cuffs with acceptable accuracy compared with intra-aortic readings.

Modeling arterial and left ventricular coupling for non-invasive measurements

Abstract The aim of the presented work has been the development of an algorithm for a non-invasive, portable, easy-to-use, and affordable device for measuring systemic cardiovascular parameters such as cardiac output and peripheral resistance. The data acquisition is based on a common oscillometric measurement using an occlusive blood pressure cuff, and no additional calibration is necessary. The novel algorithm introduced here combines several simulation techniques like neural networks or differential equations, which will be explained briefly. The determination of the hemodynamical parameters is based on the idea that the ejection work of the left ventricle is subject to an optimization principle. This kind of model needs no additional external calibration and opens therefore good perspectives for non-expert use in cardiovascular risk stratification and hypertension therapy optimization. To verify the approach we present some clinical results and a relevant discussion on it, followed by a view of future work.

Wave reflection quantification based on pressure waveforms alone-Methods, comparison, and clinical covariates

Within the last decade the quantification of pulse wave reflections mainly focused on measures of central aortic systolic pressure and its augmentation through reflections based on pulse wave analysis (PWA). A complementary approach is the wave separation analysis (WSA), which quantifies the total amount of arterial wave reflection considering both aortic pulse and flow waves. The aim of this work is the introduction and comparison of aortic blood flow models for WSA assessment. To evaluate the performance of the proposed modeling approaches (Windkessel, triangular and averaged flow), comparisons against Doppler measurements are made for 148 patients with preserved ejection fraction. Stepwise regression analysis between WSA and PWA parameters are performed to provide determinants of methodological differences. Against Doppler measurement mean difference and standard deviation of the amplitudes of the decomposed forward and backward pressure waves are comparable for Windkessel and averaged flow models. Stepwise regression analysis shows similar determinants between Doppler and Windkessel model only. The results indicate that the Windkessel method provides accurate estimates of wave reflection in subjects with preserved ejection fraction. The comparison with waveforms derived from Doppler ultrasound as well as recently proposed simple triangular and averaged flow waves showed that this approach may reduce variability and provide realistic results.

Validation of non-invasive central blood pressure devices: ARTERY Society task force consensus statement on protocol standardization

This article was published in European Heart Journal on 30 January 2017, available open access at https://doi.org/10.1093/eurheartj/ehw632

Assessment of systolic aortic pressure and its association to all cause mortality critically depends on waveform calibration.

Background: The aim of this study is the prospective investigation of the association of brachial SBP (bSBP) and aortic SBP (aSBP) to all-cause mortality, with special emphasis on different calibration methods for central pressure estimates, in particular, brachial systolic and diastolic, as well as brachial mean and diastolic pressures. Method: One hundred and fifty-nine patients were enrolled in a longitudinal, prospective study of arterial stiffness and cardiovascular risk in a chronic kidney disease stages 2–4 cohort. Office measurements of bSBP and aSBP were assessed by a validated oscillometric device. Prognostic factors of survival were identified by use of Cox proportional-hazards regression models. Results: After a mean follow-up duration of 42 months (range 30–50 months), 13 patients died. In univariate Cox analysis, bSBP and aSBP calibrated using bSBP and bDBP did not significantly predict mortality, only aSBP assessed using measured mean and diastolic pressure calibration was significantly associated with mortality (hazard ratio 1.027, P = 0.008). This remained significant in multivariate analysis after adjustment for age, sex, and anthropometric measures. More important, adding bSBP to the multivariate model (hazard ratio 0.91, P = 0.003) lead to a significantly increased prognostic power of aortic systolic pressure (hazard ratio 1.097, P < 0.001) and indicated that differences between bSBP and aSBP are of potential interest. Conclusion: Within our cohort, only aSBP assessed with measured mean and diastolic pressure independently predicted mortality and provided additional prognostic value on top of bSBP readings. Therefore, the method of calibration plays an important role for predictive power of aSBP.

Noninvasive methods to assess pulse wave velocity: comparison with the invasive gold standard and relationship with organ damage.

Objectives: To compare noninvasive methods to assess pulse wave velocity (PWV) with the invasive gold standard in terms of absolute values, age-related changes, and relationship with subclinical organ damage. Methods: Invasive aortic PWV (aoPWVinv) was measured in 915 patients undergoing cardiac catheterization (mean age 61 years, range 27–87 years). Carotid–femoral PWV (cfPWV) was measured with tonometry, using subtracted distance (cfPWVsub), body height-based estimated distance (cfPWVbh), direct distance × 0.8 (cfPWVdir0.8), and caliper-based distance (cfPWVcalip) for travel distance calculation. Aortic PWV was estimated (aoPWVestim) from single-point radial waveforms, age, and SBP. Results: Invasive and noninvasive transit times were strikingly similar (median values 60.8 versus 61.7 ms). In the entire group, median value of aoPWVinv was 8.3 m/s, of cfPWVsub and cfPWVbh 8.1 m/s, and of aoPWVest 8.5 m/s. CfPWVsub overestimated aoPWVinv in younger patients by 0.7 m/s and underestimated aoPWVinv in older patients by 1.7 m/s, with good agreement from 50 to 70 years of age. AoPWVestim differed from aoPWVinv by no more than 0.4 m/s across all age groups. CfPWVdir0.8, measured in 632 patients, overestimated aoPWVinv by 1.7 m/s in younger patients, with good agreement in middle-aged and older patients. CfPWVcalip, measured in 336 patients, underestimated aoPWVinv in all ages. In 536 patients with preserved systolic function, aoPWVinv and aoPWVestim were superior to cfPWVs in predicting coronary atherosclerosis, renal function impairment, left atrial enlargement, and diastolic dysfunction. Conclusion: CfPWVsub, cfPWVdir0.8, and aoPWVestim are reasonable surrogates for aoPWVinv. AoPWVinv predicts subclinical organ damage better than cfPWVs, and as good as aoPWVestim.

On Entropy-Based Data Mining

In the real world, we are confronted not only with complex and high-dimensional data sets, but usually with noisy, incomplete and uncertain data, where the application of traditional methods of knowledge discovery and data mining always entail the danger of modeling artifacts. Originally, information entropy was introduced by Shannon (1949), as a measure of uncertainty in the data. But up to the present, there have emerged many different types of entropy methods with a large number of different purposes and possible application areas. In this paper, we briefly discuss the applicability of entropy methods for the use in knowledge discovery and data mining, with particular emphasis on biomedical data. We present a very short overview of the state-of-the-art, with focus on four methods: Approximate Entropy (ApEn), Sample Entropy (SampEn), Fuzzy Entropy (FuzzyEn), and Topological Entropy (FiniteTopEn). Finally, we discuss some open problems and future research challenges.