Even though temperature is a continuous quantitative variable, its measurement has been considered a snapshot of a process, indicating whether a patient is febrile or afebrile. Recently, other ...diagnostic techniques have been proposed for the association between different properties of the temperature curve with severity of illness in the Intensive Care Unit (ICU), based on complexity analysis of continuously monitored body temperature. In this study, we tried to assess temperature complexity in patients with systemic inflammation during a suspected ICU-acquired infection, by using wavelets transformation and multiscale entropy of temperature signals, in a cohort of mixed critically ill patients.
Twenty-two patients were enrolled in the study. In five, systemic inflammatory response syndrome (SIRS, group 1) developed, 10 had sepsis (group 2), and seven had septic shock (group 3). All temperature curves were studied during the first 24 hours of an inflammatory state. A wavelet transformation was applied, decomposing the signal in different frequency components (scales) that have been found to reflect neurogenic and metabolic inputs on temperature oscillations. Wavelet energy and entropy per different scales associated with complexity in specific frequency bands and multiscale entropy of the whole signal were calculated. Moreover, a clustering technique and a linear discriminant analysis (LDA) were applied for permitting pattern recognition in data sets and assessing diagnostic accuracy of different wavelet features among the three classes of patients.
Statistically significant differences were found in wavelet entropy between patients with SIRS and groups 2 and 3, and in specific ultradian bands between SIRS and group 3, with decreased entropy in sepsis. Cluster analysis using wavelet features in specific bands revealed concrete clusters closely related with the groups in focus. LDA after wrapper-based feature selection was able to classify with an accuracy of more than 80% SIRS from the two sepsis groups, based on multiparametric patterns of entropy values in the very low frequencies and indicating reduced metabolic inputs on local thermoregulation, probably associated with extensive vasodilatation.
We suggest that complexity analysis of temperature signals can assess inherent thermoregulatory dynamics during systemic inflammation and has increased discriminating value in patients with infectious versus noninfectious conditions, probably associated with severity of illness.
Separation from mechanical ventilation is a difficult task, whereas conventional predictive indices have not been proven accurate enough, so far. A few studies have explored changes of breathing ...pattern variability for weaning outcome prediction, with conflicting results. In this study, we tried to assess respiratory complexity during weaning trials, using different non-linear methods derived from theory of complex systems, in a cohort of surgical critically ill patients.
Thirty two patients were enrolled in the study. There were 22 who passed and 10 who failed a weaning trial. Tidal volume and mean inspiratory flow were analyzed for 10 minutes during two phases: 1. pressure support (PS) ventilation (15-20 cm H2O) and 2. weaning trials with PS: 5 cm H2O. Sample entropy (SampEn), detrended fluctuation analysis (DFA) exponent, fractal dimension (FD) and largest lyapunov exponents (LLE) of the two respiratory parameters were computed in all patients and during the two phases of PS. Weaning failure patients exhibited significantly decreased respiratory pattern complexity, reflected in reduced sample entropy and lyapunov exponents and increased DFA exponents of respiratory flow time series, compared to weaning success subjects (p < 0.001). In addition, their changes were opposite between the two phases of the weaning trials. A new model including rapid shallow breathing index (RSBI), its product with airway occlusion pressure at 0.1 sec (P0.1), SampEn and LLE predicted better weaning outcome compared with RSBI, P0.1 and RSBI* P0.1 (conventional model, R(2) = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05).
We suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.
Abstract Purpose The aim of the study was to investigate heart rate (HR) and respiratory rate (RR) complexity in patients with weaning failure or success, using both linear and nonlinear techniques. ...Materials and Methods Forty-two surgical patients were enrolled in the study. There were 24 who passed and 18 who failed a weaning trial. Signals were analyzed for 10 minutes during 2 phases: (1) pressure support (PS) ventilation (15-20 cm H2 O) and (2) weaning trials with PS (5 cm H2 O). Low- and high-frequency (LF, HF) components of HR signals, HR multiscale entropy (MSE), RR sample entropy, cross-sample entropy between cardiorespiratory signals, Poincaré plots, and α 1 exponent were computed in all patients and during the 2 phases of PS. Results Weaning failure patients exhibited significantly decreased RR sample entropy, LF, HF, and α 1 exponent, compared with weaning success subjects ( P < .001). Their changes were opposite between the 2 phases, except for MSE that increased between and within groups ( P < .001). A new model including rapid shallow breathing index (RSBI), α 1 exponent, RR, and cross-sample entropies predicted better weaning outcome compared with RSBI, airway occlusion pressure at 0.1 second (P0.1 ), and RSBI × P0.1 (conventional model, R2 = 0.887 vs 0.463; P < .001). Areas under the curve were 0.92 vs 0.86, respectively ( P < .005). Conclusions We suggest that nonlinear analysis of cardiorespiratory dynamics has increased prognostic impact upon weaning outcome in surgical patients.
Abstract Purpose The purpose of the study was to investigate longitudinally over time heart rate (HR) and blood pressure variability and baroreflex sensitivity in acute brain injury patients and ...relate them with the severity of neurologic dysfunction and outcome. Methods Data from 20 brain injured patients due to multiple causes and treated in the intensive care unit were used, with HR and blood pressure recorded from monitors and analyzed on a daily basis. We performed power spectral analysis estimating low frequencies (LF: 0.04-0.15 Hz), high frequencies (HF: 0.15-0.4 Hz), and their ratio and calculated the approximate entropy, which assesses periodicity within a signal and transfer function (TF), that estimates baroreflex sensitivity. Heart rate variance was considered as a measure of HR variability. Results Nonsurvivors (brain dead) had lower approximate entropy (0.65 ± 0.24 vs 0.84 ± 0.26, P < .05) and lower variance mean values (0.48 ± 0.54 vs 1.29 ± 0.42 ms2 /Hz, P < .01), lower LF and HF minimum values (0.31 ± 0.88 vs 1.11 ± 0.46, P < .01; and 0.27 ± 0.42 vs 0.86 ± 0.30, P < .01, respectively), lower LF/HF (0.22 ± 0.29 vs 0.62 ± 0.28, P < .01), and lower TF mean values (0.43 ± 0.29 vs 1.11 ± 0.74, P < .05) during their whole stay in the intensive care unit in relation with survivors. The mean variance ( P < .05), mean TF ( P < .05), and mean LF/HF ( P < .05) were significantly successful in separating survivors from nonsurvivors. Conclusions We conclude that in acute brain injury patients, low variability, low baroreflex sensitivity, and sustained decrease in LF/HF of HR signals are linked with a high mortality rate.
Abstract Objective The objective of this study is the development and evaluation of efficient neurophysiological signal statistics, which may assess the driver’s alertness level and serve as ...potential indicators of sleepiness in the design of an on-board countermeasure system. Methods Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects exposed to real field driving conditions. A number of severe driving errors occurred during the experiments. The analysis was performed in two main dimensions: the macroscopic analysis that estimates the on-going temporal evolution of physiological measurements during the driving task, and the microscopic event analysis that focuses on the physiological measurements’ alterations just before, during, and after the driving errors. Two independent neurophysiologists visually interpreted the measurements. The EEG data were analyzed by using both linear and non-linear analysis tools. Results We observed the occurrence of brief paroxysmal bursts of alpha activity and an increased synchrony among EEG channels before the driving errors. The alpha relative band ratio (RBR) significantly increased, and the Cross Approximate Entropy that quantifies the synchrony among channels also significantly decreased before the driving errors. Quantitative EEG analysis revealed significant variations of RBR by driving time in the frequency bands of delta, alpha, beta, and gamma. Most of the estimated EEG statistics, such as the Shannon Entropy, Kullback–Leibler Entropy, Coherence, and Cross-Approximate Entropy, were significantly affected by driving time. We also observed an alteration of eyes blinking duration by increased driving time and a significant increase of eye blinks’ number and duration before driving errors. Conclusions EEG and EOG are promising neurophysiological indicators of driver sleepiness and have the potential of monitoring sleepiness in occupational settings incorporated in a sleepiness countermeasure device. Significance The occurrence of brief paroxysmal bursts of alpha activity before severe driving errors is described in detail for the first time. Clear evidence is presented that eye-blinking statistics are sensitive to the driver’s sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.
Many experimental and clinical studies have confirmed a continuous cross-talk between both sympathetic and parasympathetic branches of autonomic nervous system and inflammatory response, in different ...clinical scenarios. In cardiovascular diseases, inflammation has been proven to play a pivotal role in disease progression, pathogenesis and resolution. A few clinical studies have assessed the possible inter-relation between neuro-autonomic output, estimated with heart rate variability analysis, which is the variability of R-R in the electrocardiogram, and different inflammatory biomarkers, in patients suffering from stable or unstable coronary artery disease (CAD) and heart failure. Moreover, different indices derived from heart rate signals' processing, have been proven to correlate strongly with severity of heart disease and predict final outcome. In this review article we will summarize major findings from different investigators, evaluating neuro-immunological interactions through heart rate variability analysis, in different groups of cardiovascular patients. We suggest that markers originating from variability analysis of heart rate signals seem to be related to inflammatory biomarkers. However, a lot of open questions remain to be addressed, regarding the existence of a true association between heart rate variability and autonomic nervous system output or its adoption for risk stratification and therapeutic monitoring at the bedside. Finally, potential therapeutic implications will be discussed, leading to autonomic balance restoration in relation with inflammatory control.
Abstract Objective The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the ...temporal characteristics of different hypoxic levels’ residual effect upon the brain electrical activity. Methods Multichannel EEG, pO2 , pCO2 , ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn’s time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined. Results A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions. Conclusions The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons’ functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies. Significance The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.
Even with the advent of highly efficacious therapies for CLL, like the FCR regimen, many initially responding patients will eventually relapse, underscoring a characteristic resistance of the disease ...to current treatment options. The prognosis and management of patients relapsing after treatment differs significantly based on the nature of 1st line therapy and the quality and duration of remission to that therapy, as well as on other prognostic factors, especially 17p and 11q deletions and IGHV mutational status. However, robust and specific markers predictive of response to treatment are still lacking and, therefore, understanding the mechanisms underlying clinical aggressiveness and resistance to treatment constitutes a research priority. Aberrant DNA methylation is increasingly recognized as relevant for CLL with strong correlations between promoter methylation and transcriptional silencing for critical genes. There is very limited information on DNA methylation changes in CLL patients relapsing after treatment, with the single published study reporting results on 9 patients treated with alkylating agents. With this in mind, we profiled the DNA methylation of paired samples from 14 CLL cases before treatment and relapsing after treatment with FCR (n=10), Rituximab-Bendamustine (n=3) and FC (n=1). DNA from tumor samples (≥70% CD19+ B cells) was bisulfite-converted and analyzed with the Infinium HumanMethylation450 BeadChip array. The methylation level of each CpG site was calculated in GenomeStudio Methylation module, while analysis for differentially methylated CpG sites (DMCpGs) was performed using the Genomestudio software and in-house developed Perl applications. Comparison between all samples before treatment versus all the relapsed samples revealed no DMCpGs; similar results were obtained for the FCR-treated subgroup. However, intra-individual analysis of the before treatment sample versus the relapsed sample revealed significant differences in most patients. The total number of DMCpGs was found to vary among patients from 0 to 58,648 out of ∼485,000 studied CpG sites. We next identified the genes related to DMCpGs across the gene region (promoter, 5' UTR, first exon, gene body, 3' UTR), ranging from 8 to 12,003 genes/case. In order to search for distinctive gene patterns among differentially methylated genes, we performed pathway enrichment analysis (KEGG Pathway database) with the WebGestalt bioinformatics tool, and using as input the total number of genes related to DMCpGs per case without subdividing them into hypo- and hypermethylated, after noticing that different CpGs of the same gene displayed different methylation status after relapse (some hypomethylated, others hypermethylated). Gene lists from 10 out of the 14 analyzed patients showed significant (p≤0.001) enrichment for several KEGG pathways (ranging from 17 to 173). Following, we searched for common pathways among patients and observed 4 common pathways (Focal Adhesion, Cell adhesion molecules, Calcium signaling pathway, Arrhythmogenic right ventricular cardiomyopathy) among all 10 patients, and 16 pathways (including MAPK, Notch and Wnt signaling pathways) common among 9/10 patients. In parallel we performed transcription factor (TF) target enrichment analysis (MsigDB database) for the 10 gene lists showing enrichment for KEGG pathways and observed significant (p≤0.001) enrichment for genes sharing common TF binding sites. We focused on the top-10 statistically significant TF binding sites for each case and found that 6/10 TF binding sites were common among all cases corresponding to 5 known TFs (TCF3, LEF1, MAZ, FOXO4, NFATC) and one unknown. Most of these TFs are predicted to target critical genes of significant B-cell signaling pathways e.g. TCF3 (E2A) and LEF1 targeting the MAPK, Notch, Wnt and Calcium signaling pathways. In conclusion, our analysis indicates that deregulation of DNA methylation in CLL cases relapsing after treatment is not stochastic but rather it selectively affects distinct pathways critical for B-cell/CLL biology. Intriguingly, on the basis of the present in silico findings, it could be argued that specific TFs acting as master regulators of B-cell differentiation may target several molecules of the epigenetically deregulated signaling pathways, creating a possible dynamic network.
No relevant conflicts of interest to declare.
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