Felix Kindler - Academia.edu (original) (raw)

Papers by Felix Kindler

Publikationsserver der RWTH Aachen University, 2011

Waveforms of EAdi, pressure and flow for all three modes (PSV100, NAVA100, PAV100) in the same pa... more Waveforms of EAdi, pressure and flow for all three modes (PSV100, NAVA100, PAV100) in the same patient.

Definitions of patient- ventilator interaction indices and the main asynchronies collected.

Distribution of the inspiratory trigger delays per mode.

Impact of ventilator mode and level of assistance on gas exchange.

Example of Type II double triggering under NAVA.

Impact of ventilator mode and assistance level on the coefficients of variation of neural respira... more Impact of ventilator mode and assistance level on the coefficients of variation of neural respiratory rate, tidal volume, and peak electrical activity of the diaphragm (EAdimax).

Inspiratory pressure (cmH2O) over PEEP for each patient under the three modes and three assist le... more Inspiratory pressure (cmH2O) over PEEP for each patient under the three modes and three assist levels.

Scientific Reports, 2019

Dyspnoea is frequent and distressing in patients receiving mechanical ventilation, but it is ofte... more Dyspnoea is frequent and distressing in patients receiving mechanical ventilation, but it is often not properly evaluated by caregivers. Electroencephalographic signatures of dyspnoea have been identified experimentally in healthy subjects. We hypothesized that adjusting ventilator settings to relieve dyspnoea in MV patients would induce EEG changes. This was a first-of-its-kind observational study in a convenience population of 12 dyspnoeic, mechanically ventilated patients for whom a decision to adjust the ventilator settings was taken by the physician in charge (adjustments of pressure support, slope, or trigger). Pre- and post-ventilator adjustment electroencephalogram recordings were processed using covariance matrix statistical classifiers and pre-inspiratory potentials. The pre-ventilator adjustment median dyspnoea visual analogue scale was 3.0 (interquartile range: 2.5–4.0; minimum-maximum: 1–5) and decreased by (median) 3.0 post-ventilator adjustment. Statistical classifier...

B50. MECHANICAL VENTILATION: NONINVASIVE VENTILATION AND WEANING, 2011

PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text ... more PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. Neurally adjusted ventilatory assist and proportional assist ventilation both improve patient-ventilator interaction

B47. INTENSIVE CARE UNIT PHYSIOTHERAPY AND WEANING: MIND OVER MUSCLE?, 2012

B51. PHYSIOLOGY AND VENTILATION, 2011

Journal of applied physiology (Bethesda, Md. : 1985), Jan 5, 2015

In normal humans during quiet breathing, expiration is mostly driven by elastic recoil of the lun... more In normal humans during quiet breathing, expiration is mostly driven by elastic recoil of the lungs. Expiration becomes active when ventilation must be increased to meet augmented metabolic demands, or in response to expiratory loading, be it experimental or disease-related. The response to expiratory loading is considered to be mediated by both reflex and cortical mechanisms, but the latter have not been neurophysiologically characterized. We recorded the electroencephalogram (EEG) in in 20 healthy volunteers (9 men, 11 women, age: 22 to 50 years) during unloaded breathing, voluntary expirations, and in response to 50 cmH2O.L(-1).s expiratory resistive load ("ERL"), 20 cmH2O expiratory threshold load ("high ETL"), and 10 cmH2O expiratory threshold load ("low ETL"). EEGs were processed by ensemble averaging expiratory time-locked segments and examined for pre-expiratory potentials, defined as a slow negative shift from the baseline signal preceding expi...

B70. SLEEP DISORDERED BREATHING: PATHOPHYSIOLOGY, 2012

Critical Care, 2015

The objective was to compare the impact of three assistance levels of different modes of mechanic... more The objective was to compare the impact of three assistance levels of different modes of mechanical ventilation; neurally adjusted ventilatory assist (NAVA), proportional assist ventilation (PAV), and pressure support ventilation (PSV) on major features of patient-ventilator interaction. Methods: PSV, NAVA, and PAV were set to obtain a tidal volume (V T ) of 6 to 8 ml/kg (PSV 100 , NAVA 100 , and PAV 100 ) in 16 intubated patients. Assistance was further decreased by 50% (PSV 50 , NAVA 50 , and PAV 50 ) and then increased by 50% (PSV 150 , NAVA 150 , and PAV 150 ) with all modes. The three modes were randomly applied. Airway flow and pressure, electrical activity of the diaphragm (EAdi), and blood gases were measured. V T , peak EAdi, coefficient of variation of V T and EAdi, and the prevalence of the main patient-ventilator asynchronies were calculated. Results: PAV and NAVA prevented the increase of V T with high levels of assistance (median 7.4 (interquartile range (IQR) 5.7 to 10.1) ml/kg and 7.4 (IQR, 5.9 to 10.5) ml/kg with PAV 150 and NAVA 150 versus 10.9 (IQR, 8.9 to 12.0) ml/kg with PSV 150 , P <0.05). EAdi was higher with PAV than with PSV at level 100 and level 150 . The coefficient of variation of V T was higher with NAVA and PAV (19 (IQR, 14 to 31)% and 21 (IQR 16 to 29)% with NAVA 100 and PAV 100 versus 13 (IQR 11 to 18)% with PSV 100 , P <0.05). The prevalence of ineffective triggering was lower with PAV and NAVA than with PSV (P <0.05), but the prevalence of double triggering was higher with NAVA than with PAV and PSV (P <0.05). Conclusions: PAV and NAVA both prevent overdistention, improve neuromechanical coupling, restore the variability of the breathing pattern, and decrease patient-ventilator asynchrony in fairly similar ways compared with PSV. Further studies are needed to evaluate the possible clinical benefits of NAVA and PAV on clinical outcomes.

Réanimation, 2014

ABSTRACT La ventilation n’est pas un phénomène monotone; elle est au contraire variable dans le t... more ABSTRACT La ventilation n’est pas un phénomène monotone; elle est au contraire variable dans le temps, non seulement au gré des ajustements homéostasiques aux besoins de l’organisme, mais aussi d’un cycle à l’autre (périodicité dite « anharmonique »). Cette variabilité cycle-à-cycle du volume courant et de ses composantes est liée à la nature complexe, pseudochaotique — au sens mathématique de ces termes — de la dynamique de la commande respiratoire centrale qui génère le débit ventilatoire. Une certaine variabilité respiratoire est synonyme de « bonne santé » respiratoire, et la diminution de la variabilité du comportement ventilatoire est pathologique. Cette diminution peut traduire soit des modifications centrales, soit le « filtrage » de la variabilité de la commande par des modifications de charge mécanique: la complexité du débit ventilatoire et la variabilité cycle-à-cycle de la ventilation sont liées au couplage neuromécanique respiratoire et à l’équilibre charge-capacité. Ainsi, en réanimation, une faible variabilité ventilatoire prédit l’échec du sevrage la ventilation mécanique. Elle est de plus un facteur indépendant de surmortalité. De plus, dans des modèles animaux, l’adjonction artificielle d’une variabilité extrinsèque améliore la mécanique respiratoire et les échanges gazeux. La restauration de la variabilité ventilatoire naturelle par certains modes d’assistance pourrait ainsi s’avérer bénéfique. Abstract Ventilation is not monotonous. In contrast, the breathing pattern components vary with time, not only with respect to homeostatic adjustment needs, but also from one cycle to another (often referred to as the “anharmonic” period). These breath-by-breath variations in tidal volume and its components are a result of the complex and chaotic nature — in mathematical terms — of the central ventilator command that creates the tidal volume. Respiratory variability reflects “healthy’ breathing, whereas decreasing variability of the breathing pattern components is a reflection of “poor health”. This decrease may be due to central command changes or, it may be due to the “filtering” of the central variability changes in regards to the mechanical loads: the complexity of the ventilatory flow and its breath-by-breath variability is related to both the neuro-mechanical coupling and the load-capacity adequacy. Thus, in intensive care, low ventilatory variability predicts mechanical ventilation weaning failure and is also an independent risk factor of death. Moreover, in animal models, the addition of extrinsic variability has shown improvements in both lung mechanics and gas exchange. The restoration of a natural variability through new mechanical ventilation modes may prove beneficial.