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SUPPLEMENTAL DIGITAL CONTENT 1
DETAILED METHODS
Data Analysis
Ventilatory complexity: noise titration. Flow and EAdi signals were first subsampled at 5 Hz, a frequency considered to provide an adequate compromise between oversampling that carries the risk of introducing artefactual linearities in the data and thus to make the detection of nonlinearities falsely fail and undersampling that carries the risk of aliasing ADDIN EN.CITE Barahona19961000Barahona, M.Poon, C. S.1996Detection of nonlinear dynamics in short, noisy time seriesNature381215-2171, ADDIN EN.CITE Wysocki200610701630333715312006AugChaotic dynamics of resting ventilatory flow in humans assessed through noise titration54-65Hamilton Medical AG, Via Nova, CH-7403 Rhazuns, Switzerland. mwysocki@hamilton-medical.chWysocki, M.Fiamma, M. N.Straus, C.Poon, C. S.Similowski, T.Respir Physiol NeurobiolAdultFemaleFourier AnalysisHumansMale*Noise*Nonlinear Dynamics*RespirationRest/*physiologyTidal Volume/*physiologyTime FactorsTitrimetry/methodshttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=163033372. The noise titration procedure ADDIN EN.CITE Poon200110101141619598132001Jun 19Titration of chaos with added noise7107-12Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. cpoon@mit.eduPoon, C. S.Barahona, M.Proc Natl Acad Sci U S AAlgorithms*ElectricityHumans*Nonlinear DynamicsPhysicsSocioeconomic Factorshttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=114161953 was then performed as previously described ADDIN EN.CITE Wysocki200610701630333715312006AugChaotic dynamics of resting ventilatory flow in humans assessed through noise titration54-65Hamilton Medical AG, Via Nova, CH-7403 Rhazuns, Switzerland. mwysocki@hamilton-medical.chWysocki, M.Fiamma, M. N.Straus, C.Poon, C. S.Similowski, T.Respir Physiol NeurobiolAdultFemaleFourier AnalysisHumansMale*Noise*Nonlinear Dynamics*RespirationRest/*physiologyTidal Volume/*physiologyTime FactorsTitrimetry/methodshttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16303337Fiamma20079601721843829252007MayEffects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humansR1985-93Universite Pierre et Marie Curie-Paris, Paris, France.Fiamma, M. N.Straus, C.Thibault, S.Wysocki, M.Baconnier, P.Similowski, T.Am J Physiol Regul Integr Comp PhysiolAdultFemaleHumansHypercapnia/*physiopathologyHypocapnia/*physiopathologyMaleNonlinear DynamicsPulmonary Ventilation/*physiologyhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17218438Mangin200812701838734716122008Apr 30Source of human ventilatory chaos: lessons from switching controlled mechanical ventilation to inspiratory pressure support in critically ill patients189-96Universite Paris 7 and Service de Physiologie-Explorations fonctionnelles, Hopital Bichat, AP-HP, Paris, France. laurence.mangin@bch.aphp.frMangin, L.Fiamma, M. N.Straus, C.Derenne, J. P.Zelter, M.Clerici, C.Similowski, T.Respir Physiol NeurobiolAdultAgedAlgorithmsFemaleHumans*Intermittent Positive-Pressure VentilationMaleMiddle Aged*Models, BiologicalNonlinear Dynamics*PeriodicityPulmonary Ventilation*Respiration, ArtificialRespiratory Mechanics/*physiologyhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18387347Samara200915601901354516512009Jan 1Effects of inspiratory loading on the chaotic dynamics of ventilatory flow in humans82-9UPMC Univ Paris 06, EA 2397, F-75013 Paris, France.Samara, Z.Raux, M.Fiamma, M. N.Gharbi, A.Gottfried, S. B.Poon, C. S.Similowski, T.Straus, C.Respir Physiol NeurobiolAdultAnalysis of VarianceFemaleHumansMale*Nonlinear DynamicsPeriodicityPulmonary Ventilation/*physiology*Respiration, ArtificialRespiratory Mechanics/*physiologyTidal Volume/physiologyTime FactorsYoung Adulthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=190135452,4-6. This method first involves the simulation of families of linear and nonlinear polynomial autoregressive models with varying memory and dynamical order using the Volterra-Wiener method ADDIN EN.CITE Barahona19961000Barahona, M.Poon, C. S.1996Detection of nonlinear dynamics in short, noisy time seriesNature381215-2171. The best linear and nonlinear models are chosen according to the minimal information theoretic criterion, and subsequently the null hypothesis (best linear model) is tested against the alternate hypothesis (best nonlinear model) using parametric (F-test) and nonparametric (Mann-Whitney) statistics. The algorithm is used according to a trial-and-error process including the testing of K (embedding dimension) values of 4 to 6 and nonlinear degrees of 3 to 5. Nonlinearity is ascertained if the null hypothesis is rejected, namely if a nonlinear model best describes the data. A "noise limit" is then calculated as the amount of added white noise needed to mask the nonlinearity of the original signal. By definition therefore, a noise limit above zero indicates that the considered set of data contains nonlinearities and features a certain degree of complexity that can be called chaos in the case of a deterministic process. Noise limit changes from one state of the system to another state then bear witness of variations in its complexity provided that the signal-to-noise ratio remains constant. A system with a positive noise limit can adequately be characterized using traditional descriptors quantifying the sensitivity to initial conditions and unpredictability ADDIN EN.CITE Poon200110101141619598132001Jun 19Titration of chaos with added noise7107-12Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. cpoon@mit.eduPoon, C. S.Barahona, M.Proc Natl Acad Sci U S AAlgorithms*ElectricityHumans*Nonlinear DynamicsPhysicsSocioeconomic Factorshttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=114161953. Conversely, if the noise limit = 0 nonlinearity is not detected. This indicates that either the time series is not nonlinear or that the complex component of the signal has already been neutralized by the background noise within the data ADDIN EN.CITE Poon200110101141619598132001Jun 19Titration of chaos with added noise7107-12Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. cpoon@mit.eduPoon, C. S.Barahona, M.Proc Natl Acad Sci U S AAlgorithms*ElectricityHumans*Nonlinear DynamicsPhysicsSocioeconomic Factorshttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=114161953.
Ventilatory complexity: sensitivity to initial conditions and system unpredictability. Complex dynamical systems are sensitive to initial conditions, and exhibit an exponential divergence in the phase space. This can be quantified by deriving the spectrum of Lyapunov exponents and determining the largest Lyapunov exponent. In the present study, the largest Lyapunov exponent was calculated for flow and EAdi using the polynomial interpolation approach described by Briggs ADDIN EN.CITE Briggs19901160Briggs, K.1990An improved method for estimating Lyapunov exponents of chaotic time seriesPhysics Letters15127-32Fiamma20079601721843829252007MayEffects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humansR1985-93Universite Pierre et Marie Curie-Paris, Paris, France.Fiamma, M. N.Straus, C.Thibault, S.Wysocki, M.Baconnier, P.Similowski, T.Am J Physiol Regul Integr Comp PhysiolAdultFemaleHumansHypercapnia/*physiopathologyHypocapnia/*physiopathologyMaleNonlinear DynamicsPulmonary Ventilation/*physiologyhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=172184384,7 (Dataplore v 2.0.9, Datan, Teltow, Germany). The higher the largest Lyapunov exponent, the more sensitive the system to initial conditions. The degree of predictability of a complex chaotic system can be estimated by Kolmogorov-Sinai entropy. Kolmogorov-Sinai entropy measures the amount of regularity and is defined as the divergence of nearby points within the phase space (see discussion of three-dimensional phase portraits below). Kolmogorov-Sinai entropy was calculated as the sum of the positive Lyapunov exponents ADDIN EN.CITE Fiamma20079601721843829252007MayEffects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humansR1985-93Universite Pierre et Marie Curie-Paris, Paris, France.Fiamma, M. N.Straus, C.Thibault, S.Wysocki, M.Baconnier, P.Similowski, T.Am J Physiol Regul Integr Comp PhysiolAdultFemaleHumansHypercapnia/*physiopathologyHypocapnia/*physiopathologyMaleNonlinear DynamicsPulmonary Ventilation/*physiologyhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=172184384 for flow and EAdi (Dataplore v 2.0.9, Datan, Teltow, Germany). A low value of Kolmogorov-Sinai entropy indicates predictability and regularity, whereas a high value denotes unpredictable and random variation ADDIN EN.CITE Schurmann1996117012780271631996SepEntropy estimation of symbol sequences414-427Department of Theoretical Physics, University of Wuppertal, D-42097 Wuppertal, Germany.Schurmann, T.Grassberger, P.Chaoshttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=127802718.
Three-dimensional phase portraits of the flow and Edi signals were also determined. The phase space of a dynamical system is the multidimensional space in which all the possible states of the system are represented. Every degree of freedom of the system defines an axis of this space, and each possible state of the system corresponds to one unique point. A succession of such plotted points is analogous to the systems state evolving over time and defines a trajectory. The phase portrait of a periodical system is a simple closed loop, whereas the phase portrait of a chaotic system is a complicated set of nonrepeating patterns, which is, however, confined in a finite zone of the phase space (which accounts for the deterministic nature of chaos). For our purposes, the phase portraits corresponding to each condition were constructed in three dimensions, corresponding for each value x of ventilatory flow or EAdi to x, x+ t, and x+ 2t according to the embedding theorem formulated by Takens ADDIN EN.CITE Takens19801407Takens, F.1980Detecting strange attractors in turbulence. Dynamical systems and turbulenceYoung, L. S.Lecture Notes MathBerlinSpringer898366–3819.
REFERENCES
ADDIN EN.REFLIST 1. Barahona M, Poon CS: Detection of nonlinear dynamics in short, noisy time series. Nature 1996; 381:215-17
2. Wysocki M, Fiamma MN, Straus C, Poon CS, Similowski T: Chaotic dynamics of resting ventilatory flow in humans assessed through noise titration. Respir Physiol Neurobiol 2006; 153:54-65
3. Poon CS, Barahona M: Titration of chaos with added noise. Proc Natl Acad Sci U S A 2001; 98:7107-12
4. Fiamma MN, Straus C, Thibault S, Wysocki M, Baconnier P, Similowski T: Effects of hypercapnia and hypocapnia on ventilatory variability and the chaotic dynamics of ventilatory flow in humans. Am J Physiol Regul Integr Comp Physiol 2007; 292:R1985-93
5. Mangin L, Fiamma MN, Straus C, Derenne JP, Zelter M, Clerici C, Similowski T: Source of human ventilatory chaos: Lessons from switching controlled mechanical ventilation to inspiratory pressure support in critically ill patients. Respir Physiol Neurobiol 2008; 161:189-96
6. Samara Z, Raux M, Fiamma MN, Gharbi A, Gottfried SB, Poon CS, Similowski T, Straus C: Effects of inspiratory loading on the chaotic dynamics of ventilatory flow in humans. Respir Physiol Neurobiol 2009; 165:82-9
7. Briggs K: An improved method for estimating Lyapunov exponents of chaotic time series. Physics Letters 1990; 151:27-32
8. Schurmann T, Grassberger P: Entropy estimation of symbol sequences. Chaos 1996; 6:414-27
9. Takens F: Detecting strange attractors in turbulence. Dynamical systems and turbulence, Lecture Notes Math. Edited by Young LS. Berlin, Springer, 1980, pp 36681
Table 1. Breathing Pattern, Diaphragm Electrical Activity (EAdi), and Comfort Scores during Pressure Support Ventilation and at Various Levels of NAVA
PSVNAVA1NAVA2NAVA3NAVA4Breathing patternVt (ml)448 ( 34422 ( 37457 ( 41473 ( 43495 ( 44 *Vt/kg (ml/kg)7.3 ( 0.56.8 ( 0.5 7.4 ( 0.67.7 ( 0.68.1 ( 0.7 *RR (breath/min)29 ( 231 ( 331 ( 331 ( 233 ( 4V'e (l/min)12.9 ( 1.212.5 ( 1.113.1 ( 1.5 13.9 ( 1.615.3 ( 1.9*Ti (sec)0.80 ( 0.060.71 ( 0.060.72 ( 0.050.71 ( 0.050.67 ( 0.05*Te (sec)1.4 ( 0.21.4 ( 0.21.5 ( 0.21.5 ( 0.21.4 ( 0.2Tt (sec)2.2 ( 0.22.1 ( 0.22.2 ( 0.22.2 ( 0.22.1 ( 0.2Ti / Tt ( % ) 3 7 ( 1 6 3 5 ( 1 5 3 4 ( 1 9 3 4 ( 1 9 3 4 ( 1 9 V t / T i ( m l / s e c ) 5 9 0 ( 5 7 6 0 0 ( 5 7 6 4 4 ( 7 1 6 9 2 ( 7 8 * 7 5 1 ( 8 5 * E a d i E A d i p e a k ( V ) 1 2 . 6 2 . 5 1 3 . 6 2 . 2 1 1 . 2 1 . 5 9 . 3 1 . 3 * 8 . 6 1 . 3 * E A d i p e a k / E A d i t p ( V . s e c - 1 ) 2 1 . 7 4 . 6 2 3 . 4 4 . 3 1 9 . 8 3 . 5 1 6 . 2 2 . 7 1 5 . 2 3 . 1 * +"E A d i / b r e a t h ( V . s e c ) 1 0 . 1 1 . 7 1 1 . 0 1 . 5 8 . 9 0 . 8 7 . 7 0 . 9 7 . 7 1 . 1 C o m f o r t A T I C E s c o r e 1 4 . 7 ( 1 . 2 1 4 . 3 ( 1 . 4 1 4 . 8 ( 1 . 3 1 5 . 2 ( 1 . 4 1 5 . 2 ( 1 . 3
A T I C E = A d a p t a t i o n t o t h e I n t e n s i v e C a r e E n v i r o n m e n t c o m f o r t s c o r e ; E A d i p e a k = p e a k d i a p h r a g m a t i c e l e c t r i c a l a c t i v i t y ; E A d i t p = t i m e t o p e a k E A d i ; E A d i p e a k / E A d i t p = m e a n i n s p i r a t o r y r a t e o f r i s e o f E A d i ; +"E A d i = a r e a u n d e r t h e E A d i w a v e f o r m p e r b r e a t h ; N A V A 1 , 2 , 3 , 4 = n e u r a l l y a d j u s t e d v e n t i l a t o r y a s s i s t w i t h a g a i n of 1, 2, 3 and 4 cmH2O.V-1 EAdi; PSV = pressure support ventilation; RR = respiratory rate; Te = expiratory time; Ti = inspiratory time; Tt = total respiratory cycle time; Ti/Tt = inspiratory duty cycle; V'e = minute ventilation; Vt = tidal volume; Vt/kg, tidal per kg ideal body weight; Vt/Ti = mean inspiratory flow.
Data provided as mean values standard error of the mean.
* p < 0.05 versus PSV. p < 0.05 versus NAVA4
Table 2. Breathing Pattern Variability and autocorrelation Analysis during Pressure Support Ventilation and at Various Levels of NAVA
PSVNAVA1NAVA2NAVA3NAVA4Coefficient of variationVt11.1 ( 1.919.6 ( 2.9 24.0 ( 4.4 *25.5 ( 2.6 *31.1 ( 6.5 *RR10.4 ( 1.612.7 ( 1.8 15.5 ( 3.615.3 ( 2.419.6 ( 3.4 *V'e10.8 ( 1.617 ( 2.0 20.2 ( 2.5 *22.1 ( 1.9 *28.0 ( 4.5 *Ti9.6 ( 1.612.5 ( 1.7 15.8 ( 3.2 *16.5 ( 2.1 *22.7 ( 4.4 *Te 15.5 ( 2.616.0 ( 2.0 18.0 ( 2.720.4 ( 3.425.4 ( 5.0 *Tt11.4 ( 1.912.3 ( 1.5 14.5 ( 2.615.9 ( 2.419.8 ( 3.8 *Ti/Tt9.2 ( 1.611.6 ( 1.3 12.5 ( 1.5 14.5 ( 2.0 *18.1 ( 2.3 *Vt/Ti6.6 ( 1.113.3 ( 1.8 *17.0 ( 2.3 *17.6 ( 1.3 *24.7 ( 3.7 *Lag (number of cycles)
Vt8.1 ( 2.24.1 ( 2.33.9 ( 0.93.5 ( 0.53.7 ( 0.6RR12.1 ( 2.58.7 ( 3.08.9 ( 2.07.4 ( 1.84.4 ( 1.0V'e3.8 ( 1.35.6 ( 1.55.1 ( 0.64.6 ( 1.04.1 ( 0.7Ti 6.5 ( 1.42.9 ( 0.54.2 ( 0.73.8 ( 1.13.2 ( 0.7Te9.2 ( 2.45.4 ( 1.76.5 ( 1.65.6 ( 1.23.7 ( 0.6Tt12.1 ( 2.67.4 ( 2.08.2 ( 1.96.3 ( 1.54.5 ( 1.1 *Ti/Tt2.2 ( 0.22.4 ( 0.23.1 ( 0.73.7 ( 1.42.4 ( 0.2Vt/Ti16.9 ( 3.97.4 ( 2.0 *5.3 ( 0.6 *4.8 ( 0.7 *4.5 ( 0.7 *Autocorrelation coefficientVt0.28 0.050.18 0.040.22 0.040.16 0.040.19 0.04RR0.43 0.040.20 0.04 0.30 0.04 0.24 0.05 0.21 0.03 *V'e0.16 0.060.18 0.040.23 0.040.18 0.040.22 0.04Ti0.25 0.040.15 0.040.18 0.040.11 0.020.14 0.04Te0.26 0.040.17 0.040.20 0.040.19 0.040.17 0.04Tt0.38 0.050.20 0.04 *0.27 0.040.21 0.04 *0.18 0.03 *Ti/Tt0.10 0.030.17 0.020.16 0.030.16 0.040.14 0.04Vt/Ti0.45 0.050.24 0.05 *0.28 0.05 *0.24 0.04 *0.19 0.06 *
NAVA1,2,3,4 = neurally adjusted ventilatory assist with a gain of 1, 2, 3, 4 cmH20/V EAdi; PSV = pressure support ventilation; RR = respiratory rate; Te = expiratory time; Ti = inspiratory time; Tt = total respiratory cycle time; Ti/Ttot = inspiratory duty cycle; V'e = minute ventilation; Vt = tidal volume; Vt/ Ti = mean inspiratory flow.
Data provided as mean values standard error of the mean.
* p < 0.05 versus PSV, p < 0.05 versus NAVA4.
Table 3. Breath-to-breath Variability and Autocorrelation Analysis of Diaphragm Electrical Activity (EAdi) during Pressure Support Ventilation and at Various Levels of NAVA.
PSVNAVA1NAVA2NAVA3NAVA4Coefficient of variation (%)EAdipeak28.9 2.623.6 1.8 27.7 2.528.4 1.933.0 3.5EAdipeak/EAditp35.3 3.131.6 3.2 34.7 3.0 35.6 2.7 44.6 4.8 * +"E A d i 2 8 . 9 8 . 7 2 3 . 6 6 . 1 2 7 . 7 8 . 2 2 8 . 4 6 . 3 3 3 . 0 1 1 . 8 L a g ( n u m b e r o f c y c l e s ) E A d i p e a k 1 6 . 4 5 . 5 5 . 6 0 . 9 6 . 5 0 . 9 7 . 4 1 . 3 7 . 6 2 . 7 E A d i p e a k / E A d i t p 2 . 4 0 . 2 2 . 9 0 . 2 2 . 5 0 . 2 2 . 6 0 . 2 2 . 6 0 . 9 +"E A d i 1 1 . 6 4 . 1 9 . 4 4 . 3 5 . 2 0 . 9 7 . 3 1 . 8 7 . 7 2 . 0 A u t o c o r r e l a t i o n c o e f f i c i e n t E A d i p e a k 0 . 3 8 0 . 0 6 0 . 2 4 0 . 0 5 0 . 3 3 0 . 0 4 0 . 2 8 0 . 0 5 0 . 3 2 0 . 0 4 E A d i p e a k / E A d i t p 0 . 1 5 0 . 0 4 0 . 2 1 0 . 0 4 0 . 1 7 0 . 0 3 0 . 1 5 0 . 0 4 0 . 1 6 0 . 0 4 +"E A d i 0 . 2 9 0 . 0 5 0 . 2 2 0 . 0 4 0 . 2 7 0 . 0 4 0 . 2 3 0 . 0 5 0 . 2 6 0 . 0 5
E A d i p e a k = p e a k E A d i ; E A d i t p = t i m e t o p e a k E A d i ; E A d i p e a k / E A d i t p = r a t e o f r i s e o f E A d i ; +"E A d i = a r e a u n d e r t h e E A d i w a v e f o r m p e r b r e a t h ; N A V A 1 , 2 , 3 , 4 = n e u r a l l y a d j u s t e d v e n t i l a t o r y a s s i s t w i t h a g a i n o f 1 , 2 , 3 , 4 c m H 2 O . V-1 diaphragmatic electrical activity (EAdi); PSV = pressure support ventilation;
Data provided as means standard error of the mean.
* p < 0.05 versus PSV, p < 0.05 versus NAVA4
Table 4. Complexity of Ventilatory Flow and Diaphragmatic Electrical Activity (EAdi).
PSVNAVA1NAVA2NAVA3NAVA4FlowNoise limit >0>0>0>0>0LLE (bits/iteration)0.50 0.050.55 0.040.63 0.02 *0.63 0.03 *0.65 0.03 *KSE (bits/iteration)0.50 0.060.56 0.050.66 0.04 *0.65 0.04 *0.68 0.04 *EAdiNoise limit >0>0>0>0>0LLE (bits/iteration)0.57 0.040.55 0.040.56 0.030.56 0.040.55 0.03 KSE (bits/iteration)0.68 0.050.64 0.060.65 0.04 0.67 0.050.67 0.05
EAdi = diaphragmatic electrical activity; KSE = Kolmogorov- Sinai entropy; LLE = largest Lyapunov exponent; NAVA1,2,3,4, = neurally adjusted ventilatory assist with a gain of 1, 2, 3, 4 cmH20/V EAdi; PSV = pressure support ventilation.
Data provided as means standard error of the mean.
* p < 0.05 versus PSV
Table 5. Correlation between the Coefficient of Variation of Breathing Pattern or Electrical Activity of the Diaphragm (EAdi) Indices with the Largest Lyapunov Exponents of the Flow (LLEflow) and EAdi (LLEEAdi).
R (IC95%)p valueCorrelation with LLEflow CV-Vt0.32 (0.07 to 0.54)0.01 CV-RR 0.30 (0.04 to 0.52)0.02 CV-Ti/Tt0.40 (0.16 to 0.60)0.002 CV-Vt/Ti0.38 (0.14 to 0.58)0.003Correlation with LLEEAdi CV-EAdipeak-0.14 (- 0 . 3 9 t o 0 . 1 3 ) N S C V - +"E a d i 0 . 0 7 ( - 0 . 2 1 t o 0 . 3 3 ) N S C V - E A d i p e a k / E A d i t p - 0 , 1 6 ( - 0 , 4 0 t o 0 . 1 1 ) N S
C V = c o e f f i c i e n t o f v a r i a t i o n ; C V - E A d i p e a k = C V o f t h e m a x i m u m d i a p h r a g m a t i c e l e c t r i c a l a c t i v i t y ; C V - +"E a d i = C V o f t h e a r e a u n d e r t h e E Adi waveform per breath; CV-RR = CV of the respiratory rate; CV-Ti/Tt = CV of the flow-based inspiratory duty cycle; CV-Vt = CV of the tidal volume; CV-Vt/Ti = CV of the mean inspiratory flow; CV-EAdipeak/EAditp = CV of the EAdi rising slope; EAditp = time to peak of EAdi; IC95% = 95% confidence interval; R = Pearson correlation;
= nonparametric test (Spearman correlation)
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