A Non-invasive Monitoring of Propofol Concentration in Blood by a Virtual Surface Acoustic Wave Sensor Array

Propofol-induced in-situ formation of silver nanoparticles: A sensing colorimetric method

A simple and eco-friendly colorimetric sensing method has been developed for the extremely effective detection of propofol in exhaled breath condensate (EBC). In this study, we put forward a Tollens' procedure, in which silver nanoparticles (AgNPs) were produced using propofol as a reducing agent. To verify the in-situ synthesis of AgNPs, the TEM images, and UV-Vis absorbance were recorded in the absence and presence of propofol. The solution turned from a colorless to yellow and deep yellow color due to the surface plasmon resonance absorption band of formed AgNPs. The intensity of nanoparticle absorbance was quantitatively correlated with the propofol concentration. The proposed sensor revealed good linearity over the range of 0.01-0.8 µg mL^-1 at 422 nm with the detection limit of 8.8 ng mL^-1 under optimum conditions. Finally, the proposed colorimetric sensor was successfully used for the determination of propofol in the EBC sample of patients receiving propofol.

Propofol detection for monitoring of intravenous anaesthesia: a review

This paper presents a review of established and emerging methods for detecting and quantifying the intravenous anaesthetic propofol in solution. There is growing evidence of numerous advantages of total intravenous anaesthesia using propofol compared to conventional volatile-based anaesthesia, both in terms of patient outcomes and environmental impact. However, volatile-based anaesthesia still accounts for the vast majority of administered general anaesthetics, largely due to a lack of techniques for real-time monitoring of patient blood propofol concentration. Herein, propofol detection techniques that have been developed to date are reviewed alongside a discussion of remaining challenges.

Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning

Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved with electrochemical techniques, and several options are present in literature to measure propofol (widely used anaesthetics). Still, the sensors proposed do not enable in-situ detection, they do not provide this information continuously, and they are based on bulky and costly lab equipment. In this paper, we present a novel smart pen-shaped electronic system for continuous monitoring of propofol in human serum. The system consists of a needle-shaped sensor, a quasi digital front-end, a smart machine learning data processing, in a single wireless battery-operated embedded device featuring Bluetooth Low Energy (BLE) communication. The system has been tested and characterized in real, undiluted human serum, at 37  ^°C. The device features a limit of detection of 3.8  μM, meeting the requirement of the target application, with an electronics system 59% smaller and 81% less power consuming w.r.t. the state-of-the-art, using a smart machine learning classification for data processing, which guarantees up to twenty continuous measure.

Evaluating Propofol Concentration in Blood From Exhaled Gas Using a Breathing-Related Partition Coefficient

BACKGROUND

The anesthetic side effects of propofol still occur in clinical practice because no reliable monitoring techniques are available. In this regard, continuous monitoring of propofol in breath is a promising method, yet it remains infeasible because there is large variation in the blood/exhaled gas partial pressure ratio (RBE) in humans. Further evaluations of the influences of breathing-related factors on RBE would mitigate this variation.

METHODS

Correlations were analyzed between breathing-related factors (tidal volume [TV], breath frequency [BF], and minute ventilation [VM]) and RBE in 46 patients. Furthermore, a subset of 10 patients underwent pulmonary function tests (PFTs), and the parameters of the PFTs were then compared with the RBE. We employed a 1-phase exponential decay model to characterize the influence of VM on RBE. We also proposed a modified RBE (RBEM) that was not affected by the different breathing patterns of the patients. The blood concentration of propofol was predicted from breath monitoring using RBEM and RBE.

RESULTS

We found a significant negative correlation (R = -0.572; P < .001) between VM and RBE (N = 46). No significant correlation was shown between PFTs and RBE in the subset (N = 10). RBEM demonstrated a standard Gaussian distribution (mean, 1.000; standard deviation [SD], 0.308). Moreover, the predicted propofol concentrations based on breath monitoring matched well with the measured blood concentrations. The 90% prediction band was limited to within ±1 μg·mL.

CONCLUSIONS

The prediction of propofol concentration in blood was more accurate using RBEM than when using RBE and could provide reference information for anesthesiologists. Moreover, the present study provided a general approach for assessing the influence of relevant physiological factors and will inform noninvasive and accurate breath assessment of volatile drugs or metabolites in blood.