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

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.

Venous Physiology Predicts Dehydration in the Pediatric Population.

BACKGROUND: No standard dehydration monitor exists for children. This study attempts to determine the utility of Fast Fourier Transform (FFT) of a peripheral venous pressure (PVP) waveform to predict dehydration. MATERIALS AND METHODS: PVP waveforms were collected from 18 patients. Groups were defined as resuscitated (serum chloride ≥ 100 mmol/L) and hypovolemic (serum chloride < 100 mmol/L). Data were collected on emergency department admission and after a 20 cc/kg fluid bolus. The MATLAB (MathWorks) software analyzed nonoverlapping 10-s window signals; 2.4 Hz (144 bps) was the most demonstrative frequency to compare the PVP signal power (mmHg). RESULTS: Admission FFTs were compared between 10 (56%) resuscitated and 8 (44%) hypovolemic patients. The PVP signal power was higher in resuscitated patients (median 0.174 mmHg, IQR: 0.079-0.374 mmHg) than in hypovolemic patients (median 0.026 mmHg, IQR: 0.001-0.057 mmHg), (P < 0.001). Fourteen patients received a bolus regardless of laboratory values: 6 (43%) resuscitated and 8 (57%) hypovolemic. In resuscitated patients, the signal power did not change significantly after the fluid bolus (median 0.142 mmHg, IQR: 0.032-0.383 mmHg) (P = 0.019), whereas significantly increased signal power (median 0.0474 mmHg, IQR: 0.019-0.110 mmHg) was observed in the hypovolemic patients after a fluid bolus at 2.4 Hz (P < 0.001). The algorithm predicted dehydration for window-level analysis (sensitivity 97.95%, specificity 93.07%). The algorithm predicted dehydration for patient-level analysis (sensitivity 100%, specificity 100%). CONCLUSIONS: FFT of PVP waveforms can predict dehydration in hypertrophic pyloric stenosis. Further work is needed to determine the utility of PVP analysis to guide fluid resuscitation status in other pediatric populations.

Critical Information from High Fidelity Arterial and Venous Pressure Waveforms During Anesthesia and Hemorrhage.

PURPOSE: Peripheral venous pressure (PVP) waveform analysis is a novel, minimally invasive, and inexpensive method of measuring intravascular volume changes. A porcine cohort was studied to determine how venous and arterial pressure waveforms change due to inhaled and infused anesthetics and acute hemorrhage. METHODS: Venous and arterial pressure waveforms were continuously collected, while each pig was under general anesthesia, by inserting Millar catheters into a neighboring peripheral artery and vein. The anesthetic was varied from inhaled to infused, then the pig underwent a controlled hemorrhage. Pearson correlation coefficients between the power of the venous and arterial pressure waveforms at each pig's heart rate frequency were calculated for each variation in the anesthetic, as well as before and after hemorrhage. An analysis of variance (ANOVA) test was computed to determine the significance in changes of the venous pressure waveform means caused by each variation. RESULTS: The Pearson correlation coefficients between venous and arterial waveforms decreased as anesthetic dosage increased. In an opposing fashion, the correlation coefficients increased as hemorrhage occurred. CONCLUSION: Anesthetics and hemorrhage alter venous pressure waveforms in distinctly different ways, making it critical for researchers and clinicians to consider these confounding variables when utilizing pressure waveforms. Further work needs to be done to determine how best to integrate PVP waveforms into clinical decision-making.

Unsupervised Bayesian learning for rice panicle segmentation with UAV images.

BACKGROUND: In this paper, an unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV) over paddy fields. Unlike existing supervised learning methods that require a large amount of labeled training data, the unsupervised learning approach detects panicle pixels in UAV images by analyzing statistical properties of pixels in an image without a training phase. Under the Bayesian framework, the distributions of pixel intensities are assumed to follow a multivariate Gaussian mixture model (GMM), with different components in the GMM corresponding to different categories, such as panicle, leaves, or background. The prevalence of each category is characterized by the weights associated with each component in the GMM. The model parameters are iteratively learned by using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling, without the need of labeled training data. RESULTS: Applying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and F 1 score of 96.49%, 72.31%, and 82.10%, respectively. These numbers outperform existing supervised learning approaches. CONCLUSIONS: Experimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.