Response Surface Model Predictions of Wake-Up Time During Scoliosis Surgery

Ting, Chien‐Kun; Johnson, Ken B.; Teng, Wei-Nung; Synoid, Noah D.; LaPierre, Cris D.; Yu, Lu; Westenskow, Dwayne R.

doi:10.1213/ane.0000000000000094

Cited by 19 publications

(12 citation statements)

References 26 publications

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“…The authors hypothesised that the interaction models would accurately predict return of responsiveness during a wake‐up test. Their results confirmed the study hypothesis, and suggested that this modelling may help anaesthetists to more accurately predict return of responsiveness during a wake‐up test in patients undergoing spine surgery .…”

Section: Other Applicationssupporting

confidence: 63%

An introduction to predictive modelling of drug concentration in anaesthesia monitors

DeCou

Johnson

2017

Anaesthesia

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Summary A significant amount of anaesthetists’ work involves the prediction of drug effects and interactions to produce a smooth general anaesthetic that minimises drug side effects and promotes rapid emergence. Successfully managing this process requires a basic understanding of drug effects, experience and inevitably some guesswork, since it is difficult (and in some cases impossible) to anticipate all relevant patient and surgical factors. Although data are generally available to allow calculation of plasma drug and effect site concentrations, this is often difficult to apply in complex clinical contexts, particularly when multiple drug types are used. In recent years, manufacturers have developed and incorporated into anaesthetic workstations technologies that use drug pharmacodynamic and pharmacokinetic data to predict drug effects and interactions. Such systems can predict the duration and effects of drugs during anaesthesia and assist the anaesthetist to understand complex drug interactions. With this information available, different drug types, doses and combinations may be tailored in a scientific way to maximise useful effects whilst minimising overdose and side‐effects, particularly in high‐risk patients. Examples are used to illustrate how such systems can be used in practice, and how drug effects and interactions can be simulated to “rehearse” an anaesthetic before any drugs are actually administered. At present only a small number of anaesthetic workstations use this technology, and as yet they are not able to manage all drugs used in anaesthetic practice. However, such systems have the potential to help anaesthetists manage the complexity of their work, and to provide information on predicted drug effects in a way that is useful and relevant to both experienced anaesthetists and trainees.

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Section: Other Applicationssupporting

confidence: 63%

An introduction to predictive modelling of drug concentration in anaesthesia monitors

DeCou

Johnson

2017

Anaesthesia

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“…93 More recently, a study showed accurate predictions for wake-up time from a sevoflurane-remifentanil interaction model adapted to equipotent sufentanil-desflurane doses that were used. 94…”

Section: Volatile Anaesthetic-opioid Interactionsmentioning

confidence: 99%

Pharmacokinetic and pharmacodynamic interactions in anaesthesia. A review of current knowledge and how it can be used to optimize anaesthetic drug administration

Berg

Vereecke

Proost

et al. 2017

British Journal of Anaesthesia

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This review describes the basics of pharmacokinetic and pharmacodynamic drug interactions and methodological points of particular interest when designing drug interaction studies. It also provides an overview of the available literature concerning interactions, with emphasis on graphic representation of interactions using isoboles and response surface models. It gives examples on how to transform this knowledge into clinically and educationally applicable (bedside) tools.

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“…Fourth, accuracy deviation of <50% was used for this study. 6 We are uncertain whether a more strict definition would improve model parameters, and the effects on correlation are unknown. Figure 6.…”

Section: Discussionmentioning

confidence: 99%

“…The model introduced a central concept, θ a new drug as a ratio of alfentanil (U Alf ) and midazolam (U mid ) (Equation 6). …”

Section: Minto Modelmentioning

confidence: 99%

“…1-4 Data from these models guide drug dosing by presenting the desired drug effects for a specific clinical target such as depth of sedation and time to awakening. 5,6 There are models for predicting depth of anesthesia or sedation in a single-staged procedure for hypnotichypnotic, 1,2 opioid-hypnotic, 3,6,7 or analgesic-analgesic 8 drug pairs. However, there are no response surface models for predicting benzodiazepine-opioid interactions in procedures with distinct phases associated with differing degrees of stimulation.…”

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confidence: 99%

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Predicting the Best Fit: A Comparison of Response Surface Models for Midazolam and Alfentanil Sedation in Procedures With Varying Stimulation

Liou

Ting

Mandell

et al. 2016

Anesthesia &Amp; Analgesia

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Response Surface Model Predictions of Wake-Up Time During Scoliosis Surgery

Cited by 19 publications

References 26 publications

An introduction to predictive modelling of drug concentration in anaesthesia monitors

An introduction to predictive modelling of drug concentration in anaesthesia monitors

Pharmacokinetic and pharmacodynamic interactions in anaesthesia. A review of current knowledge and how it can be used to optimize anaesthetic drug administration

Predicting the Best Fit: A Comparison of Response Surface Models for Midazolam and Alfentanil Sedation in Procedures With Varying Stimulation

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