High-flow nasal cannula oxygen therapy alone or with non-invasive ventilation during the weaning period after extubation in ICU: the prospective randomised controlled HIGH-WEAN protocol

INTRODUCTION: Recent practice guidelines suggest applying non-invasive ventilation (NIV) to prevent postextubation respiratory failure in patients at high risk of extubation failure in intensive care unit (ICU). However, such prophylactic NIV has been only a conditional recommendation given the low certainty of evidence. Likewise, high-flow nasal cannula (HFNC) oxygen therapy has been shown to reduce reintubation rates as compared with standard oxygen and to be as efficient as NIV in patients at high risk. Whereas HFNC may be considered as an optimal therapy during the postextubation period, HFNC associated with NIV could be an additional means of preventing postextubation respiratory failure. We are hypothesising that treatment associating NIV with HFNC between NIV sessions may be more effective than HFNC alone and may reduce the reintubation rate in patients at high risk. METHODS AND ANALYSIS: This study is an investigator-initiated, multicentre randomised controlled trial comparing HFNC alone or with NIV sessions during the postextubation period in patients at high risk of extubation failure in the ICU. Six hundred patients will be randomised with a 1:1 ratio in two groups according to the strategy of oxygenation after extubation. The primary outcome is the reintubation rate within the 7 days following planned extubation. Secondary outcomes include the number of patients who meet the criteria for moderate/severe respiratory failure, ICU length of stay and mortality up to day 90. ETHICS AND DISSEMINATION: The study has been approved by the ethics committee and patients will be included after informed consent. The results will be submitted for publication in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT03121482

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Background Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. Methods We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders. Results Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders. Conclusion The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes

Comparison Between Neurally Adjusted Ventilatory Assist and Pressure Support Ventilation Levels in Terms of Respiratory Effort

OBJECTIVES: To understand the potential equivalence between neurally adjusted ventilatory assist and pressure support ventilation levels in terms of respiratory muscle unloading. To compare the respiratory pattern, variability, synchronization, and neuromuscular coupling within comparable ranges of assistance. DESIGN: Prospective single-center physiologic study. SETTING: A 13-bed university medical ICU. PATIENTS: Eleven patients recovering from respiratory failure. INTERVENTIONS: The following levels of assistance were consecutively applied in a random order: neurally adjusted ventilatory assist levels: 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, and 7 cm H2O/muvolt; pressure support levels: 7, 10, 15, 20, and 25 cm H2O. MEASUREMENTS AND MAIN RESULTS: Flow, airway pressure, esophageal pressures, and peak electrical activity of the diaphragm were continuously recorded. Breathing effort was calculated. To express the percentage of assist assumed by the ventilator, the total pressure including muscular and ventilator pressure was calculated. The median percentage of assist ranged from 33% (24-47%) to 82% (72-90%) between pressure support 7 and 25 cm H2O. Similar levels of unloading were observed for neurally adjusted ventilatory assist levels from 0.5 cm H2O/muvolt (46% [40-51%]) to 2.5 cm H2O/muvolt (80% [74-84%]). Tidal variability was higher during neurally adjusted ventilatory assist and ineffective efforts appeared only in pressure support. In neurally adjusted ventilatory assist, double triggering occurred sometimes when electrical activity of the diaphragm signal depicted a biphasic aspect, and an abnormal oscillatory pattern was frequently observed from 4 cm H2O/muvolt. For both modes, the relationship between peak electrical activity of the diaphragm and muscle pressure depicted a curvilinear profile. CONCLUSIONS: In patients recovering from acute respiratory failure, levels of neurally adjusted ventilatory assist between 0.5 and 2.5 cm H2O/muvolt are comparable to pressure support levels ranging from 7 to 25 cm H2O in terms of respiratory muscle unloading. Neurally adjusted ventilatory assist provides better patient-ventilator interactions but can be sometimes excessively sensitive to electrical activity of the diaphragm in terms of triggering

Patient-ventilator asynchrony during noninvasive ventilation: a bench and clinical study.

BACKGROUND: Different kinds of ventilators are available to perform noninvasive ventilation (NIV) in ICUs. Which type allows the best patient-ventilator synchrony is unknown. The objective was to compare patient-ventilator synchrony during NIV between ICU, transport-both with and without the NIV algorithm engaged-and dedicated NIV ventilators. METHODS: First, a bench model simulating spontaneous breathing efforts was used to assess the respective impact of inspiratory and expiratory leaks on cycling and triggering functions in 19 ventilators. Second, a clinical study evaluated the incidence of patient-ventilator asynchronies in 15 patients during three randomized, consecutive, 20-min periods of NIV using an ICU ventilator with and without its NIV algorithm engaged and a dedicated NIV ventilator. Patient-ventilator asynchrony was assessed using flow, airway pressure, and respiratory muscles surface electromyogram recordings. RESULTS: On the bench, frequent auto-triggering and delayed cycling occurred in the presence of leaks using ICU and transport ventilators. NIV algorithms unevenly minimized these asynchronies, whereas no asynchrony was observed with the dedicated NIV ventilators in all except one. These results were reproduced during the clinical study: The asynchrony index was significantly lower with a dedicated NIV ventilator than with ICU ventilators without or with their NIV algorithm engaged (0.5% [0.4%-1.2%] vs 3.7% [1.4%-10.3%] and 2.0% [1.5%-6.6%], P < .01), especially because of less auto-triggering. CONCLUSIONS: Dedicated NIV ventilators allow better patient-ventilator synchrony than ICU and transport ventilators, even with their NIV algorithm. However, the NIV algorithm improves, at least slightly and with a wide variation among ventilators, triggering and/or cycling off synchronization

Predictors of Intubation in Patients With Acute Hypoxemic Respiratory Failure Treated With a Noninvasive Oxygenation Strategy*:

International audienceObjectives: In patients with acute hypoxemic respiratory failure, noninvasive ventilation and high-flow nasal cannula oxygen are alternative strategies to conventional oxygen therapy. Endotracheal intubation is frequently needed in these patients with a risk of delay, and early predictors of failure may help clinicians to decide early. We aimed to identify factors associated with intubation in patients with acute hypoxemic respiratory failure treated with different noninvasive oxygenation techniques. Design: Post hoc analysis of a randomized clinical trial. Setting: Twenty-three ICUs. Patients: Patients with a respiratory rate greater than 25 breaths/min and a Pao(2)/Fio(2) ratio less than or equal to 300mm Hg. Intervention: Patients were treated with standard oxygen, high-flow nasal cannula oxygen, or noninvasive ventilation. Measurement and Main Results: Respiratory variables one hour after treatment initiation. Under standard oxygen, patients with a respiratory rate greater than or equal to 30 breaths/min were more likely to need intubation (odds ratio, 2.76; 95% CI, 1.13-6.75; p = 0.03). One hour after high-flow nasal cannula oxygen initiation, increased heart rate was the only factor associated with intubation. One hour after noninvasive ventilation initiation, a Pao(2)/Fio(2) ratio less than or equal to 200 mm Hg and a tidal volume greater than 9 mL/kg of predicted body weight were independent predictors of intubation (adjusted odds ratio, 4.26; 95% CI, 1.62-11.16; p = 0.003 and adjusted odds ratio, 3.14; 95% CI, 1.22-8.06; p = 0.02, respectively). A tidal volume above 9 mL/kg during noninvasive ventilation remained independently associated with 90-day mortality. Conclusions: In patients with acute hypoxemic respiratory failure breathing spontaneously, the respiratory rate was a predictor of intubation under standard oxygen, but not under high-flow nasal cannula oxygen or noninvasive ventilation. A Pao(2)/Fio(2) below 200mm Hg and a high tidal volume greater than 9mL/kg were the two strong predictors of intubation under noninvasive ventilation