Publications by authors named "Jos Settels"

Background: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aimed to identify and independently validate endotypes of hypotension in big datasets of surgical and critically ill patients using unsupervised deep learning.

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Background: The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiologic changes that may lead to hypotension. The original validation used a case control (backward) analysis that has been suggested to be biased. This study therefore conducted a cohort (forward) analysis and compared this to the original validation technique.

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Invasive intraarterial blood pressure measurement is currently the gold standard for intraoperative hemodynamic monitoring but accurate systolic blood pressure (SBP) measurement is difficult in everyday clinical practice, mostly because of problems with hyper-resonance or damping within the measurement system, which can lead to erroneous treatment decisions if these phenomena are not recognized. A hyper-resonant blood pressure trace significantly overestimates true systolic blood pressure while underestimating the diastolic pressure. Invasively measured systolic blood pressure is also significantly more affected than mean blood pressure by the site of measurement within the arterial system.

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Dynamic arterial elastance (Ea), the ratio between arterial pulse pressure and stroke volume changes during respiration, has been postulated as an index of the coupling between the left ventricle (LV) and the arterial system. We aimed to confirm this hypothesis using the gold-standard for defining LV contractility, afterload, and evaluating ventricular-arterial (VA) coupling and LV efficiency during different loading and contractile experimental conditions. Twelve Yorkshire healthy female pigs submitted to three consecutive stages with two opposite interventions each: changes in afterload (phenylephrine/nitroprusside), preload (bleeding/fluid bolus), and contractility (esmolol/dobutamine).

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An algorithm derived from machine learning uses the arterial waveform to predict intraoperative hypotension some minutes before episodes, possibly giving clinician's time to intervene and prevent hypotension. Whether the Hypotension Prediction Index works well with noninvasive arterial pressure waveforms remains unknown. We therefore evaluated sensitivity, specificity, and positive predictive value of the Index based on non-invasive arterial waveform estimates.

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Background: The aim of this study was to quantify the impact of different cardiovascular factors on left ventricular ejection fraction (LVEF) and test a novel LVEF calculation considering these factors.

Results: 10 pigs were studied. The experimental protocol consisted of sequentially changing afterload, preload and contractility.

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To compare the effective arterial elastance (Ea) obtained from the arterial pressure with Ea calculated from left-ventricular (LV) pressure-volume analysis. Experimental study. LV pressure-volume data was obtained with a conductance catheter and arterial pressures were measured via a fluid-filled catheter placed in the proximal aorta, femoral and radial arteries.

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Background: Maximal left ventricular (LV) pressure rise (LV dP/dt), a classical marker of LV systolic function, requires LV catheterization, thus surrogate arterial pressure waveform measures have been proposed. We compared LV and arterial (femoral and radial) dP/dt to the slope of the LV end-systolic pressure-volume relationship (Ees), a load-independent measure of LV contractility, to determine the interactions between dP/dt and Ees as loading and LV contractility varied.

Methods: We measured LV pressure-volume data using a conductance catheter and femoral and radial arterial pressures using a fluid-filled catheter in 10 anesthetized pigs.

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What We Already Know About This Topic: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors' goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility.

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Spontaneous oscillations of blood pressure (BP) and interbeat interval (IBI) may reveal important information on the underlying baroreflex control and regulation of BP We evaluated the method of continuously measured instantaneous baroreflex sensitivity by cross correlation (xBRS) validating its mean value against the gold standard of phenylephrine (Phe) and nitroprusside (SNP) bolus injections, and focusing on its spontaneous changes quantified as variability around the mean. For this purpose, we analyzed data from an earlier study of eight healthy males (aged 25-46 years) who had received Phe and SNP in conditions of baseline and autonomic blocking agents: atropine, propranolol, and clonidine. Average xBRS corresponds well to Phe/SNP-BRS, with xBRS levels ranging from 1.

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Aims: We investigated the accuracy and feasibility of a non-invasive arterial pulse contour technique for continuous measurement of stroke volume (SV) in optimization of atrioventricular (AV) delay in cardiac resynchronization therapy (CRT), by comparing SV changes assessed by Nexfin CO-Trek® (Nexfin) and echo Doppler aortic velocity-time integral (VTIao). Furthermore, we investigated whether AV-delay optimization increases the effect of CRT when compared with a default AV delay (120 ms).

Methods And Results: In 23 CRT patients, biventricular pacing (BiVP) was applied at various AV delays, while recording 10 beats preceding BiVP (baseline) and the first 10 BiVP beats, for both methods in parallel.

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Introduction: Continuous noninvasive arterial blood pressure can be measured in finger arteries using an inflatable finger cuff (FINAP) with a special device and has proven to be feasible and reliable in adults. We studied prototype pediatric finger cuffs and pediatric software to compare this blood pressure measurement with intraarterially measured blood pressure (IAP) in critically ill children.

Methods: We included sedated and mechanically ventilated children admitted to our pediatric intensive care unit.

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Background: The Finapres methodology offers continuous measurement of blood pressure (BP) in a noninvasive manner. The latest development using this methodology is the Nexfin monitor. The present study evaluated the accuracy of Nexfin noninvasive arterial pressure (NAP) compared with auscultatory BP measurements (Riva-Rocci/Korotkoff, RRK).

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Our goal was to study the feasibility of continuous noninvasive finger blood pressure (BP) monitoring in very young children, aged 0-4 y. To achieve this, we designed a set of small-sized finger cuffs based on the assessment of finger circumference. Finger arterial BP measured by a volume clamp device (Finapres technology) was compared with simultaneously measured intra-arterial BP in 15 very young children (median age, 5 mo; range, 0-48), admitted to the intensive care unit for vital monitoring.

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