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

Liou, Jing-Yang; Ting, Chien‐Kun; Mandell, M. Susan; Chang, Kuang-Yi; Teng, Wei-Nung; Huang, Yu-Yin; Tsou, Mei-Yung

doi:10.1213/ane.0000000000001299

Cited by 9 publications

(11 citation statements)

References 38 publications

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“…We had to convert alfentanil to remifentanil according to their potency before calculation for RSM’s accuracy. Our previous studies indicated that such conversion did not affect these RSMs’ applications to sedate patients undergoing medical procedures 10–12 .…”

Section: Discussionmentioning

confidence: 97%

“…Response surface models (RSMs), unlike traditional isobolograms that represent the concentrations of combirned agents to produce a single degree of drug effect ( e.g ., a C50 level—the concentration producing 50% of maximal drug effect), characterise the complete spectrum of interaction between two or more agents for possible levels of concentration and effects in response to a given stimulus 9 . Several medical procedures requiring moderate sedation and analgesia have evaluated these published RSMs 10–12 . Using a modified Greco construct equation 13 , LaPierre et al .…”

Section: Introductionmentioning

confidence: 99%

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Previously published drug interaction models predict loss of response for transoesophageal echocardiography sedation well but not response to oesophageal instrumentation

Ting

Liou

et al. 2019

Sci Rep

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Response surface models (RSMs) were used to predict effects of multiple drugs interactions. Our study was aimed to validate accuracy of the previous published volunteer models during transoesophageal echocardiography (TEE). This is a cross-sectional study with 20 patients scheduled for transesophageal echocardiography in Taipei Veterans General Hospital, Taiwan. Effect-site concentration pairs of alfentanil and propofol were recorded and converted to equivalent remifentanil and propofol effect-site concentrations. Observer’s Assessment of Alertness/Sedation (OAA/S) scores were assessed every 2 minutes. Using these data, previous published models of loss of response (LOR), intolerable ventilatory depression (IVD), and loss of response to esophageal instrumentation (LREI) were then estimated. Accuracy of prediction is assessed by calculating the difference between the true response and the model-predicted probability. Clinical events such as interruption of TEE were recorded. The average procedure time was 11 minutes. Accuracy for prediction of LOR and LREI is 63.6% and 38.5%, respectively. There were four patients experienced desaturation for less than 1 minute, which were not predicted by IVD model, and one interruption of TEE due to involuntary movement. The previous published drug-interaction RSMs predict LOR well but not LREI for TEE sedation. Further studies using response surface methodology are needed to improve quality for TEE sedation and clinical implementation.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Previously published drug interaction models predict loss of response for transoesophageal echocardiography sedation well but not response to oesophageal instrumentation

Ting

Liou

et al. 2019

Sci Rep

Self Cite

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Section: Discussionmentioning

confidence: 99%

“…Assuming a χ2 distribution with 1 degree of freedom and P = .05, if the difference between the objective function values (OFV) > 3.84, it would be considered significantly different. 15 The correlation coefficient of the regression parameter estimates was used to evaluate how well the final model described the observed data. A value of the correlation coefficient ≥.7 indicates that the observed response could be well predicted by the model.…”

Section: Discussionmentioning

confidence: 99%

“…For each pharmacodynamic response, data were combined to build a three-dimensional response surface using a naïve-pooled technique. Model parameters were determined with an iterative approach minimising the −2 times the logarithm of maximal likelihood (−2LL), 15 as followswhere N is the number for all observations. R i is the observed response, which is 0 (response to intubation) or 1 (no response to intubation).…”

Section: Methodsmentioning

confidence: 99%

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A Response Surface Analysis of the Combination of Dexmedetomidine and Sufentanil for Attenuating the Haemodynamic Response to Endotracheal Intubation

Jia

et al. 2022

Dose-Response

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Purpose Dexmedetomidine combined with opioids has been extensively used to blunt cardiovascular responses to endotracheal intubation. To determine their interaction, we aimed to develop a response surface model between dexmedetomidine and sufentanil. Methods One hundred and twenty patients undergoing scheduled gynaecological surgery were recruited. According to a simulation of slice design, patients received different dose pairs of dexmedetomidine (0 to 1.1 μg/kg) and sufentanil (.1 to .5 μg/kg). The mean arterial blood pressure and heart rate of patients were recorded just before endotracheal intubation, immediately after intubation, and during the first 3 min after intubation. The primary outcomes were haemodynamic changes. The full dose–response relationship between dexmedetomidine and sufentanil was analysed using a logit model. Results This response surface model revealed that the interaction between dexmedetomidine and sufentanil was additive. The dose pairs that could effectively attenuate the haemodynamic response to endotracheal intubation primarily ranged from .3 to .4 μg/kg and .5 to 1.1 μg/kg for sufentanil and dexmedetomidine, respectively. Conclusion When used propofol as the main hypnotic drug during anaesthesia induction, dexmedetomidine could effectively reduce the requirement of sufentanil in an additive manner. However, it is not an effective drug for ablating the cardiovascular response to endotracheal intubation when used alone. The clinical trial registry. The trial registry name: Chinese Clinical Trial Registry. Registration number: ChiCTR1800015273. URL: http://www.chictr.org.cn

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Predicting favorable response to intravenous morphine in pediatric critically ill cardiac patients

et al. 2023

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Introduction: Analgesia and sedation are integral to the care of critically ill children.However, the choice and dose of the analgesic or sedative drug is often empiric, and models predicting favorable responses are lacking. We aimed to compute models to predict a patient's response to intravenous morphine. Methods:We retrospectively analyzed data from consecutive patients admitted to the Cardiac Intensive Care Unit (January 2011-January 2020) who received at least one intravenous bolus of morphine. The primary outcome was a decrease in the State Behavioral Scale (SBS) ≥1 point; the secondary outcome was a decrease in the heart rate Z-score (zHR) at 30 min. Effective doses were modeled using logistic regression, Lasso regression, and random forest modeling. Results: A total of 117,495 administrations of intravenous morphine among 8140 patients (median age 0.6 years [interquartile range [IQR] 0.19, 3.3]) were included.The median morphine dose was 0.051 mg/kg (IQR 0.048, 0.099) and the median 30day cumulative dose was 2.2 mg/kg (IQR 0.4, 15.3). SBS decreased following 30% of doses, did not change following 45%, and increased following 25%. The zHR significantly decreased after morphine administration (median delta-zHR −0.34 [IQR−1.03, 0.00], p < 0.001). The following factors were associated with favorable response to morphine: A concomitant infusion of propofol, higher prior 30-day cumulative dose, being invasively ventilated and/or on vasopressors. Higher morphine dose, higher zHR pre-morphine, an additional analgosedation bolus ±30 min around the index bolus, a concomitant ketamine or dexmedetomidine infusion, and showing signs of withdrawal syndrome were associated with unfavorable response. Logistic regression (area under the receiver operating characteristic [ROC] curve [AUC] 0.900) and machine learning models (AUC 0.906) performed comparably, with a sensitivity of 95%, specificity of 71%, and negative predictive value of 97%.Conclusions: Statistical models identify 95% of effective intravenous morphine doses in pediatric critically ill cardiac patients, while incorrectly suggesting an effective dose in 29% of cases. This work represents an important step toward computer-aided, personalized clinical decision support tool for sedation and analgesia in ICU patients.

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

Cited by 9 publications

References 38 publications

Previously published drug interaction models predict loss of response for transoesophageal echocardiography sedation well but not response to oesophageal instrumentation

Previously published drug interaction models predict loss of response for transoesophageal echocardiography sedation well but not response to oesophageal instrumentation

A Response Surface Analysis of the Combination of Dexmedetomidine and Sufentanil for Attenuating the Haemodynamic Response to Endotracheal Intubation

Predicting favorable response to intravenous morphine in pediatric critically ill cardiac patients

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