Anesthetics affect peripheral venous pressure waveforms and the cross-talk with arterial pressure

Abstract

Analysis of peripheral venous pressure (PVP) waveforms is a novel method of monitoring intravascular volume. Two pediatric cohorts were studied to test the effect of anesthetic agents on the PVP waveform and cross-talk between peripheral veins and arteries: (1) dehydration setting in a pyloromyotomy using the infused anesthetic propofol and (2) hemorrhage setting during elective surgery for craniosynostosis with the inhaled anesthetic isoflurane. PVP waveforms were collected from 39 patients that received propofol and 9 that received isoflurane. A multiple analysis of variance test determined if anesthetics influence the PVP waveform. A prediction system was built using k-nearest neighbor (k-NN) to distinguish between: (1) PVP waveforms with and without propofol and (2) different minimum alveolar concentration (MAC) groups of isoflurane. 52 porcine, 5 propofol, and 7 isoflurane subjects were used to determine the cross-talk between veins and arteries at the heart and respiratory rate frequency during: (a) during and after bleeding with constant anesthesia, (b) before and after propofol, and (c) at each MAC value. PVP waveforms are influenced by anesthetics, determined by MANOVA: p value < 0.01, η2 = 0.478 for hypovolemic, and η2 = 0.388 for euvolemic conditions. The k-NN prediction models had 82% and 77% accuracy for detecting propofol and MAC, respectively. The cross-talk relationship at each stage was: (a) ρ = 0.95, (b) ρ = 0.96, and (c) could not be evaluated using this cohort. Future research should consider anesthetic agents when analyzing PVP waveforms developing future clinical monitoring technology that uses PVP.

Use of artificial intelligence in paediatric anaesthesia: a systematic review

“…Machine learning was the most represented with 38 included studies describing its use, 13 23 28 29 31 32 , 34 , 35 , 36 , 38 , 39 , 40 , 42 44 46 47 , 49 , 50 , 51 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 70 whereas two applications incorporated the use of fuzzy logic 21 , 22 and only one study described the use of each of natural language processing 23 and computer vision. 13 Many branches of machine learning were discussed, including regression models, 29 45 46 49 50 51 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 decision trees, 23 , 33 , 34 , 35 expert systems, …”

“… 11 Robles-Rubio and colleagues 35 K-nearest neighbours A non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. 43 Al-Alawi and colleagues 44 Hu and colleagues 29 Neural networks Network of nodes that communicate with other nodes via connections. Connections between nodes are weighted based upon their ability to provide a desired outcome, becoming strengthened when their neurones have correlated outputs.…”

“…Applications intended to monitor physiological variables during anaesthesia were described in six studies. 35 , 38 , 39 , 40 , 42 44 Al-Alawi and colleagues 44 used a K-nearest neighbour model to monitor changes in peripheral venous pressure waveforms in relation to propofol and isoflurane administration. Ansermino and colleagues, 38 Dosani and colleagues, 39 and Jalali and colleagues 40 described applications to monitor physiological signals during procedures in order to give early warning of potential upcoming adverse events.…”

“… 23 31 32 35 40 45 49 51 58 , 60 , 61 , 62 , 66 70 Similarly, most applications were developed using small population sizes and incomplete input data (missing/unreliable information), which limited the ability of the studies to accurately evaluate the performance of the developed tools. 29 44 45 50 56 59 64 As is becoming increasingly evident, challenges regarding the incorporation of AI systems in medicine translate far beyond the immediate AI application and extend throughout the organisation of our healthcare systems. 73 For such technologies to reach their full potential, emphasis needs to be placed upon establishing, storing, cleaning, and sharing accurate data collections between groups, institutions, and organisations.…”