Vasilios E Papaioannou, Ioanna G Chouvarda, Nikos K Maglaveras, Ioannis A Pneumatikos
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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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":35905,"journal":{"name":"BMC Physiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1472-6793-11-2","citationCount":"28","resultStr":"{\"title\":\"Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials.\",\"authors\":\"Vasilios E Papaioannou, Ioanna G Chouvarda, Nikos K Maglaveras, Ioannis A Pneumatikos\",\"doi\":\"10.1186/1472-6793-11-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Results: </strong>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). 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引用次数: 28
摘要
背景:与机械通气分离是一项艰巨的任务,而迄今为止,传统的预测指标尚未被证明足够准确。一些研究探索了呼吸模式可变性的变化对断奶结果的预测,结果相互矛盾。在这项研究中,我们尝试在一组外科危重患者中,使用从复杂系统理论衍生的不同非线性方法来评估脱机试验期间的呼吸复杂性。结果:32例患者入组。有22人通过了断奶试验,10人没有通过。分两个阶段分析10分钟潮气量和平均吸气流量:1。压力支持(PS)通风(15- 20cm H2O);PS: 5cm H2O断奶试验。在所有患者和PS的两个阶段计算两种呼吸参数的样本熵(SampEn)、去趋势波动分析(DFA)指数、分形维数(FD)和最大李雅普诺夫指数(LLE)。与脱机成功患者相比,脱机失败患者呼吸模式复杂性显著降低,反映在样本熵和李雅普诺夫指数降低,呼吸流量时间序列的DFA指数增加(p < 0.001)。此外,在断奶试验的两个阶段,它们的变化是相反的。包括快速浅呼吸指数(RSBI)及其在0.1秒气道闭塞压下的积(P0.1)、SampEn和LLE在内的新模型比RSBI、P0.1和RSBI* P0.1预测更好的断奶结果(常规模型,R(2) = 0.874 vs 0.643, p < 0.001)。曲线下面积分别为0.916 vs 0.831 (p < 0.05)。结论:我们认为,呼吸信号的复杂性分析可以评估固有的呼吸模式动力学,并增加了对手术患者脱机结果的预后影响。
Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials.
Background: 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.
Results: 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).
Conclusions: 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.
BMC PhysiologyBiochemistry, Genetics and Molecular Biology-Physiology
CiteScore
9.60
自引率
0.00%
发文量
0
期刊介绍:
BMC Physiology is an open access journal publishing original peer-reviewed research articles in cellular, tissue-level, organismal, functional, and developmental aspects of physiological processes. BMC Physiology (ISSN 1472-6793) is indexed/tracked/covered by PubMed, MEDLINE, BIOSIS, CAS, EMBASE, Scopus, Zoological Record and Google Scholar.