Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation

Knopp, Jennifer L.; Chase, J. Geoffrey; Kim, Kyeong Tae

doi:10.1016/j.cmpb.2021.106300

Cited by 27 publications

(9 citation statements)

References 85 publications

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“…EAdi is the best indicator of the central driving force [14,15]. In this study, two patients had no EAdi and could not convert to NAVA, which was caused by inhibition of the respiratory center or damage to the nerve conduction pathway.…”

Section: Discussionmentioning

confidence: 62%

The predictive value of neurally adjusted ventilatory assist indexes for the outcome of patients with severe hemorrhagic stroke

Tang

Niu

et al. 2023

Preprint

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Objective: This study assessed the predictive value of electrical activity of the diaphragm (EAdi) and the EAdi-derived monitoring index in the outcome of patients with severe hemorrhagic stroke. Methods: Ninety patients with severe hemorrhagic stroke were admitted to the neurosurgery intensive care unit of Yijishan Hospital from April 2019 to June 2021 and were divided into the good outcome group (Glasgow Outcome Scale [GOS] ≥4) and poor outcome group [GOS]≤3). The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate prediction accuracy. Results: EAdi, neuro-ventilatory efficiency (NVE), and neuro-muscular efficiency (NME) in patients with good outcome were significantly higher than those in patients with poor outcome (4.707 µV vs 2.80 µV, P < 0.001; 141.85 ml/µV vs 66.01 ml/µV, P = 0.000; 2.57 cm H2O/µV vs 1.37 cm H2O/µV, P = 0.000). The area under the ROC curve for the EAdi score was 0.719, with sensitivity of 69.70% and specificity of 68.42% when EAdi was 3.6 µV. The AUC for NVE score was 0.793, with sensitivity of 75.76% and specificity of 77.19% when the NVE value was 95.32 ml/µV. The AUC for NME score was 0.792, with sensitivity of 69.70% and specificity of 78.95% when the NME value was 2.06 H2O/µV. The 6-month survival time of patients with higher EAdi, NVE, and NME was significantly longer than that of patients with lower EAdi, NVE, and NME. Conclusion: EAdi, NVE, and NME can be used as indices for predicting the outcome of patients with severe hemorrhagic stroke. Trial registration: No.ChiCTR1900022861. Registered April 28, 2016, http:// www.chictr.org.cn

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Section: Discussionmentioning

confidence: 62%

The predictive value of neurally adjusted ventilatory assist indexes for the outcome of patients with severe hemorrhagic stroke

Tang

Niu

et al. 2023

Preprint

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“…Firstly, the assumption is made that the modeled patient effort p mus (t ) is non-positive. In [22], this same assumption is adopted and it is shown that this can give realistic estimates in a large group of patients. Typically inspiration is an active process, where the diaphragm is contracted.…”

Section: B Patient Effort Modelmentioning

confidence: 99%

“…However, no realistic time-varying patient effort is obtained. In [22], strictly negative b-spline basis functions are used to model the patient effort. Using these basis functions, the patient's effort, lung elastance, and lung resistance are estimated.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Breathing Effort Estimation of Mechanically Ventilated Patients Using Sparse Optimization

Reinders

Hunnekens²,

Wouw

et al. 2022

IEEE Open J. Control. Syst.

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Mechanical ventilators facilitate breathing for patients who cannot breathe (sufficiently) on their own. The aim of this paper is to estimate relevant lung parameters and the spontaneous breathing effort of a ventilated patient that help keeping track of the patient's clinical condition. A key challenge is that estimation using the available sensors for typical model structures results in a non-identifiable parametrization. A sparse optimization algorithm to estimate the lung parameters and the patient effort, without interfering with the patient's treatment, using an 1 -regularization approach is presented. It is confirmed that accurate estimates of the lung parameters and the patient effort can be retrieved through a simulation case study and an experimental case study.

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“…Various pressure-based techniques can estimate the patient's inspiratory muscle effort during supportive mechanical ventilation in clinical practice: the esophageal-derived muscle pressure, work of breathing, the pressure time product, P 0.1 , an end-expiratory occlusion test, and proportional assist ventilation (PAV) [6] , [7] . Furthermore, experimental algorithms for non-invasive estimation are published such as the least square fitting method [8] , constrained optimization [9] , Gaussian effort model [10] , b-spline effort model [11] , and sparse optimization [12] ; however, these methods are not clinically tested yet.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation

Diepen¹,

Bakkes²,

Bie³

et al. 2023

Heliyon

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Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation

Cited by 27 publications

References 85 publications

The predictive value of neurally adjusted ventilatory assist indexes for the outcome of patients with severe hemorrhagic stroke

The predictive value of neurally adjusted ventilatory assist indexes for the outcome of patients with severe hemorrhagic stroke

Noninvasive Breathing Effort Estimation of Mechanically Ventilated Patients Using Sparse Optimization

Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation

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