We identified clinical factors associated with PIH and eIOH. The use of these factors to estimate the risk of PIH and eIOH might allow the avoidance or timely treatment of hypotensive episodes during general anaesthesia.

The determination of blood flow, i.e. cardiac output, is an integral part of haemodynamic monitoring. This is a review on noninvasive continuous cardiac output monitoring in perioperative and intensive care medicine. We present the underlying principles and validation data of the following technologies: thoracic electrical bioimpedance, thoracic bioreactance, vascular unloading technique, pulse wave transit time, and radial artery applanation tonometry. According to clinical studies, these technologies are capable of providing cardiac output readings noninvasively and continuously. They, therefore, might prove to be innovative tools for the assessment of advanced haemodynamic variables at the bedside. However, for most technologies there are conflicting data regarding the measurement performance in comparison with reference methods for cardiac output assessment. In addition, each of the reviewed technology has its own limitations regarding applicability in the clinical setting. In validation studies comparing cardiac output measurements using these noninvasive technologies in comparison with a criterion standard method, it is crucial to correctly apply statistical methods for the assessment of a technology's accuracy, precision, and trending capability. Uniform definitions for 'clinically acceptable agreement' between innovative noninvasive cardiac output monitoring systems and criterion standard methods are currently missing. Further research must aim to further develop the different technologies for noninvasive continuous cardiac output determination with regard to signal recording, signal processing, and clinical applicability.

No abstract

No abstract

When comparing 2 technologies for measuring hemodynamic parameters with regard to their ability to track changes, 2 graphical tools are omnipresent in the literature: the 4-quadrant plot and the polar plot recently proposed by Critchley et al. The polar plot is thought to be the more advanced statistical tool, but care should be taken when it comes to its interpretation. The polar plot excludes possibly important measurements from the data. The polar plot transforms the data nonlinearily, which may prevent it from being seen clearly. In this article, we compare the 4-quadrant and the polar plot in detail and thoroughly describe advantages and limitations of each. We also discuss pitfalls concerning the methods to prepare the researcher for the sound use of both methods. Finally, we briefly revisit the Bland-Altman plot for the use in this context.

No abstract

The CNAP system (CNSystems Medizintechnik AG, Graz, Austria) provides noninvasive continuous arterial pressure measurements by using the volume clamp method. Recently, an algorithm for the determination of cardiac output by pulse contour analysis of the arterial waveform recorded with the CNAP system became available. We evaluated the agreement of the continuous noninvasive cardiac output (CNCO) measurements by CNAP in comparison with cardiac output measurements invasively obtained using transpulmonary thermodilution (TDCO). In this proof-of-concept analysis we studied 38 intensive care unit patients from a previously set up database containing CNAP-derived arterial pressure data and TDCO values obtained with the PiCCO system (Pulsion Medical Systems SE, Feldkirchen, Germany). We applied the new CNCO algorithm retrospectively to the arterial pressure waveforms recorded with CNAP and compared CNCO with the corresponding TDCO values (criterion standard). Analyses were performed separately for (1) CNCO calibrated to the first TDCO (CNCO-cal) and (2) CNCO autocalibrated to biometric patient data (CNCO-auto). We did not perform an analysis of trending capabilities because the patients were hemodynamically stable. The median age and APACHE II score of the 22 male and 16 female patients was 63 years and 18 points, respectively. 18 % were mechanically ventilated and in 29 % vasopressors were administered. Mean ± standard deviation for CNCO-cal, CNCO-auto, and TDCO was 8.1 ± 2.7, 6.4 ± 1.9, and 7.8 ± 2.4 L/min, respectively. For CNCO-cal versus TDCO, Bland-Altman analysis demonstrated a mean difference of +0.2 L/min (standard deviation 1.0 L/min; 95 % limits of agreement -1.7 to +2.2 L/min, percentage error 25 %). For CNCO-auto versus TDCO, the mean difference was -1.4 L/min (standard deviation 1.8 L/min; 95 % limits of agreement -4.9 to +2.1 L/min, percentage error 45 %). This pilot analysis shows that CNCO determination is feasible in critically ill patients. A percentage error of 25 % indicates acceptable agreement between CNCO-cal and TDCO. The mean difference, the standard deviation, and the percentage error between CNCO-auto and TDCO were higher than between CNCO-cal and TDCO. A hyperdynamic cardiocirculatory state in a substantial number of patients and the hemodynamic stability making trending analysis impossible are main limitations of our study.

We aimed to describe and evaluate an autocalibrating algorithm for determination of cardiac output (CO) based on the analysis of an arterial pressure (AP) waveform recorded using radial artery applanation tonometry (AT) in a continuous non-invasive manner. To exemplarily describe and evaluate the CO algorithm, we deliberately selected 22 intensive care unit patients with impeccable AP waveforms from a database including AP data obtained with AT (T-Line system; Tensys Medical Inc.). When recording AP data for this prospectively maintained database, we had simultaneously noted CO measurements obtained from just calibrated pulse contour analysis (PiCCO system; Pulsion Medical Systems) every minute. We applied the autocalibrating CO algorithm to the AT-derived AP waveforms and noted the computed CO values every minute during a total of 15 min of data recording per patient (3 × 5-min intervals). These 330 AT-derived CO (AT-CO) values were then statistically compared to the corresponding pulse contour CO (PC-CO) values. Mean ± standard deviation for PC-CO and AT-CO was 7.0 ± 2.0 and 6.9 ± 2.1 L/min, respectively. The coefficient of variation for PC-CO and AT-CO was 0.280 and 0.299, respectively. Bland-Altman analysis demonstrated a bias of +0.1 L/min (standard deviation 0.8 L/min; 95% limits of agreement -1.5 to 1.7 L/min, percentage error 23%). CO can be computed based on the analysis of the AP waveform recorded with AT. In the selected patients included in this pilot analysis, a percentage error of 23% indicates clinically acceptable agreement between AT-CO and PC-CO.