Study of the respiratory pattern variability in patients during weaning trials

Characterization of the respiratory pattern variability of patients with different pressure support levels

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

Variability of respiratory mechanics during sleep in overweight and obese subjects with and without asthma

Variability of respiration may provide information regarding disease states. We sought to characterize variability of ventilation and resistance in healthy and asthma, to determine how respiratory control may be altered in sleep and with bi-level positive airway pressure (BPAP). Overweight and obese subjects with and without asthma were studied during sleep at baseline and with BPAP, while measuring respiratory system resistance (Rrs) continuously. Stable periods (>20min) of wake, NREM, and REM sleep were identified and correlation metrics of respiratory parameters were calculated, including coefficient of variation (CV). Variability of Rrs was also characterized over short time scales (20 breaths) during sleep and defined as either "leading to arousal" or "not leading to arousal". Data from 10 control and 10 subjects with asthma were analyzed. CV of Rrs was decreased in asthma at baseline (p<0.001) and decreased on BPAP as compared to baseline (p<0.001). Long time scale correlations were found in respiratory parameters, but the degree of correlations was decreased from wake to sleep (p<0.05). The variance and CV of Rrs was increased preceding an arousal from sleep at baseline; however, during BPAP, the CV was decreased and was not increased preceding arousals. At baseline, resistance was greater in those with asthma, but variability was smaller. BPAP reduced both resistance and overall variability. We conclude that the BPAP-induced decrease in variability may indicate that those with asthma are more likely to remain in a low resistance state, and that low resistance variability may reduce arousals from sleep.

Analysis of roots in ARMA model for the classification of patients on weaning trials

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques

One of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). A total of 153 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S); 38 patients that failed to maintain spontaneous breathing (group F), and 21 patients who had successful weaning trials, but required reintubation in less than 48 h (group R). The respiratory pattern was characterized by their time series. The results show that significant differences were obtained with parameters as model order and first coefficient of AR model, and final prediction error by ARMA model. An accuracy of 86% (84% sensitivity and 86% specificity) has been obtained when using order model and first coefficient of AR model, and mean of breathing duration.

Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury

Acute lung injury evokes a pulmonary inflammatory response and changes in the breathing pattern. The inflammatory response has a centrally mediated component which depends on the vagi. We hypothesize that the central inflammatory response, complimentary to the pulmonary inflammatory response, is expressed in the nuclei tractus solitarii (nTS) and that the expression of cytokines in the nTS is associated with breathing pattern changes. Adult, male Sprague-Dawley rats (n=12) received intratracheal instillation of either bleomycin (3units in 120μl of saline) or saline (120μl). Respiratory pattern changed by 24h. At 48h, bronchoalveolar lavage fluid and lung tissue had increased IL-1β and TNF-α levels, but not IL-6. No changes in these cytokines were noted in serum. Immunocytochemical analysis of the brainstem indicated increased expression of IL-1β in the nTS commissural subnucleus that was localized to neurons. We conclude that breathing pattern changes in acute lung injury were associated with increased levels of IL-1β in brainstem areas which integrate cardio-respiratory sensory input.

Multi-parameter analysis of ECG and respiratory flow signals to identify success of patients on weaning trials

Statistical analysis, power spectral density, and Lempel Ziv complexity, are used in a multi-parameter approach to analyze four temporal series obtained from the Electrocardiographic and Respiratory Flow signals of 126 patients on weaning trials. In which, 88 patients belong to successful group (SG), and 38 patients belong to failure group (FG), i.e. failed to maintain spontaneous breathing during trial. It was found that mean values of cardiac inter-beat and breath durations give higher values for SG than for FG; Kurtosis coefficient of the spectrum of the rapid shallow breathing index is higher for FG; also Lempel Ziv complexity mean values associated with the respiratory flow signal are bigger for FG. Patients were then classified with a pattern recognition neural network, obtaining 80% of correct classifications (81.6% for FG and 79.5% for SG).