Propofol detection for monitoring of intravenous anaesthesia: a review

Ferrier, David C.; Kiely, Janice; Luxton, Richard

doi:10.1007/s10877-021-00738-5

Cited by 13 publications

(7 citation statements)

References 60 publications

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“…In the generally available measures, the preoperative physiological status of patient can be deemed as partially controllable characteristic and could be readjusted accordingly with the appropriate instruction; comparatively, as the fully controllable feature, the drug dosage of initial infusion manifests appreciable importance and can be properly manipulated by anesthetists. The medicinal effectiveness of propofol and lidocaine on attenuating hemodynamic responses to intubation/extubation have been noted in the previous researches [40][41][42][43]. Other drugs' influence could be further considered with the current model architecture on an extended dataset.…”

Section: Discussionmentioning

confidence: 71%

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Te,

Zhu,

et al. 2024

BMC Anesthesiol

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Background Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values. Methods In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2). Results The ICV values were proved to be consistent with the anesthetists’ ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511–0.0513), RMSE (0.0792, 95% CI: 0.0790–0.0794), and MAPE (0.2086, 95% CI: 0.2077–0.2095) and the largest R2 (0.9047, 95% CI: 0.9043–0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values. Conclusions Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions.

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Section: Discussionmentioning

confidence: 71%

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Te,

Zhu,

et al. 2024

BMC Anesthesiol

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“…Propofol is one of the most common intravenous anesthetics. Due to its rapid onset, stable efficacy, and short duration, it has been widely used in clinical practice [ 9 , 10 ]. It can be used for induction and maintenance of general anesthesia, long-term sedation, monitored anesthesia care (MAC), and conscious sedation in the intensive care unit (ICU) [ 11 ].…”

Section: The Current Situation Of Non-opioid Anesthetics Addictionmentioning

confidence: 99%

Non-Opioid Anesthetics Addiction: A Review of Current Situation and Mechanism

Deng,

Wu,

Gao

et al. 2023

Brain Sciences

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Drug addiction is one of the major worldwide health problems, which will have serious adverse consequences on human health and significantly burden the social economy and public health. Drug abuse is more common in anesthesiologists than in the general population because of their easier access to controlled substances. Although opioids have been generally considered the most commonly abused drugs among anesthesiologists and nurse anesthetists, the abuse of non-opioid anesthetics has been increasingly severe in recent years. The purpose of this review is to provide an overview of the clinical situation and potential molecular mechanisms of non-opioid anesthetics addiction. This review incorporates the clinical and biomolecular evidence supporting the abuse potential of non-opioid anesthetics and the foreseeable mechanism causing the non-opioid anesthetics addiction phenotypes, promoting a better understanding of its pathogenesis and helping to find effective preventive and curative strategies.

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“…Propofol (2,6-diisopropylphenol) is a general anesthetic widely used for the induction and maintenance of anesthesia in different clinical scenarios [ 1 , 2 ]. Given its favorable pharmacokinetic and pharmacodynamic profile, including fast onset and offset effects as well as the low incidence of vomiting or nausea, propofol has been the most frequently administered anesthetic drug for the last 30 years [ 3 ]. Despite these advantages, propofol administration is commonly accompanied by exaggerated vascular relaxation [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

TRPA1 Polymorphisms Modify the Hypotensive Responses to Propofol with No Change in Nitrite or Nitrate Levels

Melo

Oliveira-Paula

Ferezin

et al. 2022

CIMB

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Anesthesia with propofol is frequently associated with hypotension. The TRPA1 gene contributes to the vasodilator effect of propofol. Hypotension is crucial for anesthesiologists because it is deleterious in the perioperative period. We tested whether the TRPA1 gene polymorphisms or haplotypes interfere with the hypotensive responses to propofol. PCR-determined genotypes and haplotype frequencies were estimated. Nitrite, nitrates, and NOx levels were measured. Propofol induced a more expressive lowering of the blood pressure (BP) without changing nitrite or nitrate levels in patients carrying CG+GG genotypes for the rs16937976 TRPA1 polymorphism and AG+AA genotypes for the rs13218757 TRPA1 polymorphism. The CGA haplotype presented the most remarkable drop in BP. Heart rate values were not impacted. The present exploratory analysis suggests that TRPA1 genotypes and haplotypes influence the hypotensive responses to propofol. The mechanisms involved are probably other than those related to NO bioavailability. With better genetic knowledge, planning anesthesia with fewer side effects may be possible.

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Propofol detection for monitoring of intravenous anaesthesia: a review

Cited by 13 publications

References 60 publications

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Non-Opioid Anesthetics Addiction: A Review of Current Situation and Mechanism

TRPA1 Polymorphisms Modify the Hypotensive Responses to Propofol with No Change in Nitrite or Nitrate Levels

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