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Cardiac output (CO) plays a crucial role in the hemodynamic management of critically ill patients treated in the intensive care unit and in surgical patients undergoing major surgery. In the field of cardiovascular dynamics, innovative techniques for CO determination are increasingly available. Therefore, the number of studies comparing these techniques with a reference, such as pulmonary artery thermodilution, is rapidly growing. There are mainly two outcomes of such method comparison studies: (1) the accuracy of agreement and (2) the precision of agreement. The precision of agreement depends on the precision of each method, i.e., the precision that the studied and the reference technique are able to achieve. We call this "precision of method". A decomposition of variance shows that method agreement does not only depend on the precision of method but also on another important source of variability, i.e., the method's general variability about the true values. Ignorance of that fact leads to falsified conclusions about the precision of method of the studied technique. In CO studies, serial measurements are frequently confused with repeated measurements. But as the actual CO of a subject changes from assessment to assessment, there is no real repetition of a measurement. This situation equals a scenario in which single measurements are given for multiple true values per subject. In such a case it is not possible to assess the precision of method.

Development of accurate Situation Awareness (SA) depends on experience and may be impaired during excessive workload. In order to gain adequate SA for decision making and performance, anaesthetists need to distribute visual attention effectively. Therefore, we hypothesized that in more experienced anaesthetists performance is better and increase of physiological workload is less during critical incidents. Additionally, we investigated the relation between physiological workload indicators and distribution of visual attention. In fifteen anaesthetists, the increase of pupil size and heart rate was assessed in course of a simulated critical incident. Simulator log files were used for performance assessment. An eye-tracking device (EyeSeeCam) provided data about the anaesthetists' distribution of visual attention. Performance was assessed as time until definitive treatment. T tests and multivariate generalized linear models (MANOVA) were used for retrospective statistical analysis. Mean pupil diameter increase was 8.1% (SD ± 4.3) in the less experienced and 15.8% (±10.4) in the more experienced subjects (p = 0.191). Mean heart rate increase was 10.2% (±6.7) and 10.5% (±8.3, p = 0.956), respectively. Performance did not depend on experience. Pupil diameter and heart rate increases were associated with a shift of visual attention from monitoring towards manual tasks (not significant). For the first time, the following four variables were assessed simultaneously: physiological workload indicators, performance, experience, and distribution of visual attention between "monitoring" and "manual" tasks. However, we were unable to detect significant interactions between these variables. This experimental model could prove valuable in the investigation of gaining and maintaining SA in the operation theatre.

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.