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The goal of this study is to measure left ventricular stroke volume (SV) from the brachial artery (BA) using electrical bioimpedance. Doppler-derived SV was used for comparison. Twenty-nine healthy adults were recruited for study. Doppler echocardiographic-derived SV was obtained from the product of distal left ventricular outflow tract cross-sectional area and systolic velocity integral. SV from the BA was obtained by transbrachial electrical bioimpedance velocimetry (TBEV). Application of a current field across the left brachium was effected by injection of a constant magnitude, high frequency, low amperage, alternating current. Therein, a static voltage (U(0)) and pulsatile voltage change (ΔU(t)) were measured and converted to their corresponding impedances, Z(0) and ΔZ(t). TBEV-derived SV was obtained by multiplying a square root value of the normalized, acceleration-based, peak first time derivative of ΔZ(t) by a volume conductor and systolic flow time. Inter-method agreement was determined by the Bland-Altman method. To assess the contribution of blood resistivity variations to ΔZ(t), BA diameters were measured at end-diastole and peak systolic expansion. Results indicate that since the BA demonstrates parabolic, laminar flow, with minimal diameter changes, blood resistivity variations are likely responsible for the derived impedance changes. Bland-Altman analysis shows that SV is obtainable by TBEV from healthy humans at rest.

Continual vital sign assessment on the general care, medical-surgical floor is expected to provide early indication of patient deterioration and increase the effectiveness of rapid response teams. However, there is concern that continual, multi-parameter vital sign monitoring will produce alarm fatigue. The objective of this study was the development of a methodology to help care teams optimize alarm settings. An on-body wireless monitoring system was used to continually assess heart rate, respiratory rate, SpO2 and noninvasive blood pressure in the general ward of ten hospitals between April 1, 2014 and January 19, 2015. These data, 94,575 h for 3430 patients are contained in a large database, accessible with cloud computing tools. Simulation scenarios assessed the total alarm rate as a function of threshold and annunciation delay (s). The total alarm rate of ten alarms/patient/day predicted from the cloud-hosted database was the same as the total alarm rate for a 10 day evaluation (1550 h for 36 patients) in an independent hospital. Plots of vital sign distributions in the cloud-hosted database were similar to other large databases published by different authors. The cloud-hosted database can be used to run simulations for various alarm thresholds and annunciation delays to predict the total alarm burden experienced by nursing staff. This methodology might, in the future, be used to help reduce alarm fatigue without sacrificing the ability to continually monitor all vital signs.Electronic supplementary materialThe online version of this article (doi:10.1007/s10877-015-9790-8) contains supplementary material, which is available to authorized users.

Abstract:The Research Resource for Complex Physiologic Signals, supported by the National Institutes of Health (NIH), is intended to promote and facilitate investigations in the study of cardiovascular and other complex biomedical signals. The resource website (www.physionet.org) has 3 interdependent components: 1) PhysioBank is an archive of well-characterized digital recordings of physiologic signals and related data, including databases of electrocardiogram and heart rate time series from patients with heart failure, coronary disease, sleep apnea syndromes, and cardiac arrhythmias; 2) PhysioToolkit is a library of open-source software for physiologic signal processing and analysis; and 3) PhysioNet, for which the resource is named, is an on-line forum for dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. PhysioNet, in cooperation with the annual Computers in Cardiology conference, hosts a series of challenges inviting participants to tackle clinically interesting problems that are either unsolved or not well solved. PhysioNet invites contributions of databases and software from the biomedical community.