Responses of Bilevel Ventilators to Unintentional Leak: A Bench Study.

BACKGROUND: The impact of leaks has mainly been assessed in bench models using continuous leak patterns which did not reflect real-life leakage. We aimed to assess the impact of the pattern and intensity of unintentional leakage (UL) using several respiratory models. METHODS: An active artificial lung (ASL 5000) was connected to three bilevel-ventilators set in pressure mode; the experiments were carried out with three lung mechanics (COPD, OHS and NMD) with and without upper airway obstruction. Triggering delay, work of breathing, pressure rise time, inspiratory pressure, tidal volume, cycling delay and the asynchrony index were measured at 0, 6, 24 and 36 L/min of UL. We generated continuous and inspiratory UL. RESULTS: Compared to 0 L/min of UL, triggering delays were significantly higher with 36 L/min of UL (+27 ms) and pressure rise times were longer (+71 ms). Cycling delays increased from -4 [-250-169] ms to 150 [-173-207] ms at, respectively 0 L/min and 36 L/min of UL and work of breathing increased from 0.15 [0.12-0.29] J/L to 0.19 [0.16-0.36] J/L. Inspiratory leakage pattern significantly increased triggering delays (+35 ms) and cycling delays (+263 ms) but decreased delivered pressure (-0.94 cmH2O) compared to continuous leakage pattern. Simulated upper airway obstruction significantly increased triggering delay (+199 ms), cycling delays (+371 ms), and decreased tidal volume (-407 mL) and pressure rise times (-56 ms). CONCLUSIONS: The pattern of leakage impacted more the device performances than the magnitude of the leakage per se. Flow limitation negatively reduced all ventilator performances.

Recommended Approaches to Minimize Aerosol Dispersion of SARS-CoV-2 During Noninvasive Ventilatory Support Can Cause Ventilator Performance Deterioration: A Benchmark Comparative Study.

BACKGROUND: SARS-CoV-2 aerosolization during noninvasive positive-pressure ventilation may endanger health care professionals. Various circuit setups have been described to reduce virus aerosolization. However, these setups may alter ventilator performance. RESEARCH QUESTION: What are the consequences of the various suggested circuit setups on ventilator efficacy during CPAP and noninvasive ventilation (NIV)? STUDY DESIGN AND METHODS: Eight circuit setups were evaluated on a bench test model that consisted of a three-dimensional printed head and an artificial lung. Setups included a dual-limb circuit with an oronasal 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 triggering 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 generated to the artificial lung, the total work of breathing, and the pressure-time product needed to trigger the ventilator. RESULTS: With NIV, the type of circuit setup had a significant impact on inspiratory flow preceding triggering of the ventilator (P < .0001), the inspiratory effort required to trigger the ventilator (P < .0001), the triggering delay (P < .0001), the maximal inspiratory pressure (P < .0001), the tidal volume (P = .0008), the work of breathing (P < .0001), and the pressure-time product needed to trigger the ventilator (P < .0001). Similar differences and consequences were seen with CPAP as well as with the addition of bacterial filters. Best performance was achieved with a dual-limb circuit with an oronasal mask. Worst performance was achieved with a dual-limb circuit with a helmet interface. INTERPRETATION: Ventilator performance is significantly impacted by the circuit setup. A dual-limb circuit with oronasal mask should be used preferentially.

NIV Is not Adequate for High Intensity Endurance Exercise in COPD.

Noninvasive ventilation (NIV) during exercise has been suggested to sustain higher training intensity but the type of NIV interface, patient-ventilator asynchronies (PVA) or technological limitation of the ventilator may interfere with exercise. We assessed whether these parameters affect endurance exercise capacity in severe COPD patients. In total, 21 patients with severe COPD not eligible to home NIV performed three constant workload tests. The first test was carried out on spontaneous breathing (SB) and the following ones with NIV and a nasal or oronasal mask in a randomized order. PVA and indicators of ventilator performance were assessed through a comprehensive analysis of the flow pressure tracing raw data from the ventilator. The time limit was significantly reduced with both masks (406 s (197-666), 240 s (131-385) and 189 s (115-545), p < 0.01 for tests in SB, with oronasal and nasal mask, respectively). There were few PVA with an oronasal mask (median: 3.4% (1.7-5.2)) but the ventilator reached its maximal generating capacity (median flowmax: 208.0 L/s (189.5-224.8) while inspiratory pressure dropped throughout exercise (from 10.1 (9.4-11.4) to 8.8 cmH2O (8.6-10.8), p < 0.01). PVA were more frequent with nasal mask (median: 12.8% (3.2-31.6), p < 0.01). Particularly, the proportion of patients with ineffective efforts > 10% was significantly higher with nasal interface (0% versus 33.3%, p < 0.01). NIV did not effectively improve endurance capacity in COPD patients not acclimated to home NIV. This was due to a technological limitation of the ventilator for the oronasal mask and the consequence either of an insufficient pressure support or a technological limitation for the nasal mask.

Prediction of severe acute exacerbation using changes in breathing pattern of COPD patients on home noninvasive ventilation.

Introduction: Acute 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. Methods: This 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. Results: A 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. Conclusion: Data from NIV can identify a change in breathing patterns that predicts severe AECOPD.

An easy-to-use technique to characterize cardiodynamics from first-return maps on ΔRR-intervals.

Heart rate variability analysis using 24-h Holter monitoring is frequently performed to assess the cardiovascular status of a patient. The present retrospective study is based on the beat-to-beat interval variations or ΔRR, which offer a better view of the underlying structures governing the cardiodynamics than the common RR-intervals. By investigating data for three groups of adults (with normal sinus rhythm, congestive heart failure, and atrial fibrillation, respectively), we showed that the first-return maps built on ΔRR can be classified according to three structures: (i) a moderate central disk, (ii) a reduced central disk with well-defined segments, and (iii) a large triangular shape. These three very different structures can be distinguished by computing a Shannon entropy based on a symbolic dynamics and an asymmetry coefficient, here introduced to quantify the balance between accelerations and decelerations in the cardiac rhythm. The probability P111111 of successive heart beats without large beat-to-beat fluctuations allows to assess the regularity of the cardiodynamics. A characteristic time scale, corresponding to the partition inducing the largest Shannon entropy, was also introduced to quantify the ability of the heart to modulate its rhythm: it was significantly different for the three structures of first-return maps. A blind validation was performed to validate the technique.