IntroductionAcute exacerbation of COPD (AECOPD) is associated with poor outcome. Noninvasive ventilation (NIV) is recommended to treat end-stage COPD. We hypothesized that changing breathing pattern of COPD patients on NIV could identify patients with severe AECOPD prior to admission.MethodsThis is a prospective monocentric study including all patients with COPD treated with long-term home NIV. Patients were divided in two groups: a stable group in which patients were admitted for the usual respiratory review and an exacerbation group in which patients were admitted for inpatient care of severe AECOPD. Data from the ventilator were downloaded and analyzed over the course of the 10 days that preceded the admission.ResultsA total of 62 patients were included: 41 (67%) in the stable group and 21 (33%) in the exacerbation group. Respiratory rate was higher in the exacerbation group than in the stable group over the 10 days preceding inclusion (18.2±0.5 vs 16.3±0.5 breaths/min, respectively) (P=0.034). For 2 consecutive days, a respiratory rate outside the interquartile limit of the respiratory rate calculated over the 4 preceding days was associated with an increased risk of severe AECOPD of 2.8 (95% CI: 1.4–5.5) (P<0.001). This assessment had the sensitivity, specificity, positive predictive, and negative predictive values of 57.1, 80.5, 60.0, and 78.6% respectively. Over the 10 days’ period, a standard deviation (SD) of the daily use of NIV >1.0845 was associated with an increased risk of severe AECOPD of 4.0 (95% CI: 1.5–10.5) (P=0.001). This assessment had the sensitivity, specificity, positive predictive, and negative predictive values of 81.0, 63.4, 53.1, and 86.7%, respectively.ConclusionData from NIV can identify a change in breathing patterns that predicts severe AECOPD.

Background SARS-CoV-2 aerosolization during noninvasive positive pressure ventilation may endanger healthcare professionals. Various circuit setups have been described in order to reduce virus aerosolization. However, these setups may alter ventilator performances. Research question What are the consequences of the different suggested circuit setups on ventilator’s efficacy during continuous positive airway pressure (CPAP) and noninvasive ventilation (NIV)? Study Design and Method Eight circuit setups were evaluated on a bench made of a 3-D printed head and an artificial lung. Setups were a dual-limb circuit with an oro-nasal mask, a dual-limb circuit with a helmet interface, a single-limb circuit with a passive exhalation valve, three single-limb circuits with custom-made additional leaks and two single-limb circuits with active exhalation valves. All setups were evaluated during NIV and CPAP. The following variables were recorded: the inspiratory flow preceding trigger of the ventilator, the inspiratory effort required to trigger the ventilator, the triggering delay, the maximal inspiratory pressure delivered by the ventilator, the tidal volume (V t ) generated to the artificial lung, the total work of breathing (WOB) and the pressure time product to trigger the ventilator (PTPt). Results With NIV, the type of circuit setup had a significant impact on inspiratory flow preceding the trigger of the ventilator (p<0.0001), the inspiratory effort required to trigger the ventilator (p<0.0001), the triggering delay (p<0.0001); the maximal inspiratory pressure (p<0.0001), the V t (p:0.0008), the WOB (p<0.0001), the PTPt (p<0.0001). Similar differences and consequences were seen with CPAP as well as with the addition of bacterial filters. Best performance was achieved using a dual limb circuit with an oro-nasal mask. Worst performance was achieved using a dual-limb circuit with a helmet interface. Interpretation Ventilator performance is significantly impacted by the circuit setup. The use of dual-limb circuit with oro-nasal masks should be used preferentially.

Background: An important issue in noninvasive mechanical ventilation consists in understanding the origins of patient-ventilator asynchrony for reducing their incidence by adjusting ventilator settings to the intrinsic ventilatory dynamics of each patient. One of the possible ways for doing this is to evaluate the performances of the domiciliary mechanical ventilators using a test bench. Such a procedure requires to model the evolution of the pressure imposed by respiratory muscles, but for which there is no standard recommendations. Methods:In this paper we propose a mathematical model for simulating the muscular pressure developed by the inspiratory muscles and corresponding to different patient ventilatory dynamics to drive the ASL 5000 mechanical lung. Our model is based on the charge and discharge of a capacitor through a resistor, simulating contraction and relaxation phases of the inspiratory muscles.Results: Our resulting equations were used to produce 420 time series of the muscle pressure with various contraction velocities, amplitudes and shapes, in order to represent the inter-patient variability clinically observed. All these dynamics depend on two parameters, the ventilatory frequency and the mouth occlusion pressure. Conclusion:Based on the equation of the respiratory movement and its electrical analogy, the respiratory muscle pressure was simulated with more consistency in regards of physiological evidences than those provided by the ASL 5000 software. The great variability in the so-produced inspiratory efforts can cover most of realistic patho-physiological conditions.