Statistical monitoring techniques

Baker

2001

Anaesthesia

SummaryCardiac output is one of the major determinants of the rate of uptake, and therefore the end-expired concentration (F E H ) of volatile anaesthetic agents. The purpose of this theoretical study was to explore the effect of cardiac output changes on F E H for a range of volatile anaesthetics. A multicompartment model of anaesthetic uptake and distribution which produces constant values of F E H was used. The minimum detectable change in cardiac output was determined for a variety of anaesthetic agents for four patterns of cardiac output change. The effect of a step change in cardiac output from 5 to 10 l.min 21 was also recorded. The smallest cardiac output changes (average 33%) were detected with isoflurane. As blood solubility increased or decreased, larger cardiac output changes were needed before they could be detected. With a large step change in cardiac output and with increasing solubility, the final change in F E H increased but the initial rate of change of F E H is decreased. A significant cardiac output change will produce a change in volatile anaesthetic uptake. An unexpected change in F E H should be considered as a possible signal of a sudden cardiac output change. The difference between agents may represent a balance between the amount of agent taken up and the size of the tissue`sink' for that agent.

Section: Methodsmentioning

confidence: 99%

The effect of cardiac output changes on end‐expired volatile anaesthetic concentrations – a theoretical study

Baker

2001

Anaesthesia

“…When the threshold is exceeded, the system signals that it considers a change has occurred. We have previously determined the optimum threshold values for simulated arterial BP data sampled at 10-s intervals from the same model to be 0.96 (9), while ␣ is conventionally set at 0.2 (10).…”

Section: Methodsmentioning

confidence: 99%

The Effect of the Interval Between Blood Pressure Determinations on the Delay in the Detection of Changes: A Computer Simulation

French²

2003

Anesthesia & Analgesia

“…The prediction for the next period: u t =ad t +(1-a)u t-1 where d t is the current value a is a constant between 0 and 1 which determines the weighting typical values are 0.1 or 0.2 11 The error in the prediction e t =d t -u t-1 The smoothed error s t =ae t +(1-a)s t-1 and the mean absolute deviation for the error MAD t =a|e t |+(1-a)MAD t-1 Trigg's Tracking Variable is then calculated T t =s t /MAD t Finally variables relating to the previous time period are updated: u t-1 =u t s t-1 =s t MAD t-1 =MAD t…”

Section: Appendixmentioning

confidence: 99%

“…One finding of this study was that a graphical display led to faster detection of changes than a numeric display, primarily because more information per unit of time could be presented. We have also been interested for some time in the use of simple statistical trend detection tools, in particular Trigg's Tracking Variable (TTV) 10 , which was described in detail by Lewis 11 and first implemented in an anaesthetic context by Hope in 1973 12 .…”

mentioning

confidence: 99%

The Effect of a Graphical Interpretation of a Statistic Trend Indicator (Trigg's Tracking Variable) on the Detection of Simulated Changes

Merry

2011

Anaesth Intensive Care

Anaesthesia involves processing large amounts of information over time. One task of the anaesthetist is to detect substantive changes in physiological variables promptly and reliably. It has been previously demonstrated that a graphical trend display of historical data leads to more rapid detection of such changes. We examined the effect of a graphical indication of the magnitude of Trigg's Tracking Variable, a simple statistically based trend detection algorithm, on the accuracy and latency of the detection of changes in a micro-simulation. Ten anaesthetists each viewed 20 simulations with four variables displayed as the current value with a simple graphical trend display. Values for these variables were generated by a computer model, and updated every second; after a period of stability a change occurred to a new random value at least 10 units from baseline. In 50% of the simulations an indication of the rate of change was given by a five level graphical representation of the value of Trigg's Tracking Variable. Participants were asked to indicate when they thought a change was occurring. Changes were detected 10.9% faster with the trend indicator present (mean 13.1 [SD 3.1] cycles vs 14.6 [SD 3.4] cycles, 95% confidence interval 0.4 to 2.5 cycles, P=0.013. There was no difference in accuracy of detection (median with trend detection 97% [interquartile range 95 to 100%], without trend detection 100% [98 to 100%]), P=0.8. We conclude that simple statistical trend detection may speed detection of changes during routine anaesthesia, even when a graphical trend display is present.