Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques.

BACKGROUND: Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths. METHODS: Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔPes), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation. RESULTS: 6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6-88.3], and 86.8% [86.6-87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔPes > 10 cmH2O and 37.2% a ΔPes > 15 cmH2O. CONCLUSIONS: Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.

Specific Training Improves the Detection and Management of Patient-Ventilator Asynchrony.

BACKGROUND: Patient-ventilator asynchrony is common in patients undergoing mechanical ventilation. The proportion of health-care professionals capable of identifying and effectively managing different types of patient-ventilator asynchronies is limited. A few studies have developed specific training programs, but they mainly focused on improving patient-ventilator asynchrony detection without assessing the ability of health-care professionals to determine the possible causes. METHODS: We conducted a 36-h training program focused on patient-ventilator asynchrony detection and management for health-care professionals from 20 hospitals in Latin America and Spain. The training program included 6 h of a live online lesson during which 120 patient-ventilator asynchrony cases were presented. After the 6-h training lesson, health-care professionals were required to complete a 1-h training session per day for the subsequent 30 d. A 30-question assessment tool was developed and used to assess health-care professionals before training, immediately after the 6-h training lecture, and after the 30 d of training (1-month follow-up). RESULTS: One hundred sixteen health-care professionals participated in the study. The median (interquartile range) of the total number of correct answers in the pre-training, post-training, and 1-month follow-up were significantly different (12 [8.75-15], 18 [13.75-22], and 18.5 [14-23], respectively). The percentages of correct answers also differed significantly between the time assessments. Study participants significantly improved their performance between pre-training and post-training (P < .001). This performance was maintained after a 1-month follow-up (P = .95) for the questions related to the detection, determination of cause, and management of patient-ventilator asynchrony. CONCLUSIONS: A specific 36-h training program significantly improved the ability of health-care professionals to detect patient-ventilator asynchrony, determine the possible causes of patient-ventilator asynchrony, and properly manage different types of patient-ventilator asynchrony.

Hiccup-like Contractions in Mechanically Ventilated Patients: Individualized Treatment Guided by Transpulmonary Pressure.

Hiccups-like contractions, including hiccups, respiratory myoclonus, and diaphragmatic tremor, refer to involuntary, spasmodic, and inspiratory muscle contractions. They have been repeatedly described in mechanically ventilated patients, especially those with central nervous damage. Nevertheless, their effects on patient-ventilator interaction are largely unknown, and even more overlooked is their contribution to lung and diaphragm injury. We describe, for the first time, how the management of hiccup-like contractions was individualized based on esophageal and transpulmonary pressure measurements in three mechanically ventilated patients. The necessity or not of intervention was determined by the effects of these contractions on arterial blood gases, patient-ventilator synchrony, and lung stress. In addition, esophageal pressure permitted the titration of ventilator settings in a patient with hypoxemia and atelectasis secondary to hiccups and in whom sedatives failed to eliminate the contractions and muscle relaxants were contraindicated. This report highlights the importance of esophageal pressure monitoring in the clinical decision making of hiccup-like contractions in mechanically ventilated patients.

Comparative evaluation of three total full-face masks for delivering Non-Invasive Positive Pressure Ventilation (NPPV): a bench study.

Historically, the oro-nasal mask has been the preferred interface to deliver Non-Invasive Positive Pressure Ventilation (NPPV) in critically ill patients. To overcome the problems related to air leaks and discomfort, Total Full-face masks have been designed. No study has comparatively evaluated the performance of the total Full-face masks available.The aim of this bench study was to evaluate the influence of three largely diffuse models of total Full -face masks on patient-ventilator synchrony and performance during pressure support ventilation. NPPV was applied to a mannequin, connected to an active test lung through three largely diffuse Full-face masks: Dimar Full-face mask (DFFM), Performax Full-face mask (RFFM) and Pulmodyne Full-face mask (PFFM).The performance analysis showed that the ΔPtrigger was significantly lower with PFFM (p < 0.05) at 20 breaths/min (RRsim) at both pressure support (iPS) levels applied, while, at RRsim 30, DFFM had the longest ΔPtrigger compared to the other 2 total full face masks (p < 0.05). At all ventilator settings, the PTP200 was significantly shorter with DFFM than with the other two total full-face masks (p < 0.05). In terms of PTP500 ideal index (%), we did not observe significant differences between the interfaces tested.The PFFM demonstrated the best performance and synchrony at low respiratory rates, but when the respiratory rate increased, no difference between all tested total full-face masks was reported.

Influence of total face masks design and circuit on synchrony and performance during pressure support ventilation: A bench study.

BACKGROUND: Few studies investigated the influence of the circuit applied during non-invasive ventilation (NIV) with a total face mask. The aim of this bench study was to evaluate the effects of separated inflow and outflow ports in a total face mask on patient ventilator interaction and performance during NIV through a total face mask. METHODS: A mannequin was connected to an active lung simulator. NIV was applied both via a standard total face mask (STFM) with a Y-piece connector for inflow/outflow gases and a modified total face mask (MTFM) with 2 different connectors for inflow and outflow gases. RESULTS: The MTFM showed both a significantly better patient-ventilator interaction and a significantly higher performance. The MTFM showed a significantly lower Δtrigger compared to STFM (p<0.01) and shorter value of PTPtrigger during all ventilator setting tested (p < 0.01). Significant differences in PTP 200, PTP 300, and PTP 500 were observed between the MTFM and STFM (p < 0.01) in all conditions tested. Concerning PTP 500 ideal index, in all the conditions tested, the MTFM presented higher values compared to STFM, although those differences were not statistically significant. At both RRsim and ventilator settings tested, the MTFM showed a significantly shorter Delaytrinsp and Delaytrexp compared to STFM (p < 0.01). At both RRsim tested and both ventilator settings, the MTFM showed a significantly longer Timesync compared to STFM (p < 0.01). CONCLUSIONS: The MTFM showed a significantly better patient ventilator interaction and a better ventilator performance, suggesting that this kind of total face mask design should be preferred in clinical practice.

Waveforms-guided cycling-off during pressure support ventilation improves both inspiratory and expiratory patient-ventilator synchronisation.

OBJECTIVE: To test the performance of a software able to control mechanical ventilator cycling-off by means of automatic, real-time analysis of ventilator waveforms during pressure support ventilation. DESIGN: Prospective randomised crossover study. SETTING: University Intensive Care Unit. PATIENTS: Fifteen difficult-to-wean patients under pressure support ventilation. INTERVENTIONS: Patients were ventilated using a G5 ventilator (Hamilton Medical, Bonaduz, Switzerland) with three different cycling-off settings: standard (expiratory trigger sensitivity set at 25% of peak inspiratory flow), optimised by an expert clinician and automated; the last two settings were tested at baseline pressure support and after a 50% increase in pressure support. MEASUREMENTS AND MAIN RESULTS: Ventilator waveforms were recorded and analysed by four physicians experts in waveforms analysis. Major and minor asynchronies were detected and total asynchrony time computed. Automation compared to standard setting reduced cycling delay from 407 ms [257-567] to 59 ms [22-111] and ineffective efforts from 12.5% [3.4-46.4] to 2.8% [1.9-4.6]) at baseline support (p < 0.001); expert optimisation performed similarly. At high support both cycling delay and ineffective efforts increased, mainly in the case of expert setting, with the need of reoptimisation of expiratory trigger sensitivity. At baseline support, asynchrony time decreased from 39.9% [27.4-58.7] with standard setting to 32% [22.3-39.4] with expert optimisation (p < 0.01) and to 24.4% [19.6-32.5] with automation (p < 0.001). Both at baseline and at high support, asynchrony time was lower with automation than with expert setting. CONCLUSIONS: Cycling-off guided by automated real-time waveforms analysis seems a reliable solution to improve synchronisation in difficult-to-wean patients under pressure support ventilation.

Timing of inspiratory muscle activity detected from airway pressure and flow during pressure support ventilation: the waveform method.

BACKGROUND: Whether respiratory efforts and their timing can be reliably detected during pressure support ventilation using standard ventilator waveforms is unclear. This would give the opportunity to assess and improve patient-ventilator interaction without the need of special equipment. METHODS: In 16 patients under invasive pressure support ventilation, flow and pressure waveforms were obtained from proximal sensors and analyzed by three trained physicians and one resident to assess patient's spontaneous activity. A systematic method (the waveform method) based on explicit rules was adopted. Esophageal pressure tracings were analyzed independently and used as reference. Breaths were classified as assisted or auto-triggered, double-triggered or ineffective. For assisted breaths, trigger delay, early and late cycling (minor asynchronies) were diagnosed. The percentage of breaths with major asynchronies (asynchrony index) and total asynchrony time were computed. RESULTS: Out of 4426 analyzed breaths, 94.1% (70.4-99.4) were assisted, 0.0% (0.0-0.2) auto-triggered and 5.8% (0.4-29.6) ineffective. Asynchrony index was 5.9% (0.6-29.6). Total asynchrony time represented 22.4% (16.3-30.1) of recording time and was mainly due to minor asynchronies. Applying the waveform method resulted in an inter-operator agreement of 0.99 (0.98-0.99); 99.5% of efforts were detected on waveforms and agreement with the reference in detecting major asynchronies was 0.99 (0.98-0.99). Timing of respiratory efforts was accurately detected on waveforms: AUC for trigger delay, cycling delay and early cycling was 0.865 (0.853-0.876), 0.903 (0.892-0.914) and 0.983 (0.970-0.991), respectively. CONCLUSIONS: Ventilator waveforms can be used alone to reliably assess patient's spontaneous activity and patient-ventilator interaction provided that a systematic method is adopted.

Flow Index accurately identifies breaths with low or high inspiratory effort during pressure support ventilation.

BACKGROUND: Flow Index, a numerical expression of the shape of the inspiratory flow-time waveform recorded during pressure support ventilation, is associated with patient inspiratory effort. The aim of this study was to assess the accuracy of Flow Index in detecting high or low inspiratory effort during pressure support ventilation and to establish cutoff values for the Flow index to identify these conditions. The secondary aim was to compare the performance of Flow index,of breathing pattern parameters and of airway occlusion pressure (P0.1) in detecting high or low inspiratory effort during pressure support ventilation. METHODS: Data from 24 subjects was included in the analysis, accounting for a total of 702 breaths. Breaths with high inspiratory effort were defined by a pressure developed by inspiratory muscles (Pmusc) greater than 10 cmH2O while breaths with low inspiratory effort were defined by a Pmusc lower than 5 cmH2O. The areas under the receiver operating characteristic curves of Flow Index and respiratory rate, tidal volume,respiratory rate over tidal volume and P0.1 were analyzed and compared to identify breaths with low or high inspiratory effort. RESULTS: Pmusc, P0.1, Pressure Time Product and Flow Index differed between breaths with high, low and intermediate inspiratory effort, while RR, RR/VT and VT/kg of IBW did not differ in a statistically significant way. A Flow index higher than 4.5 identified breaths with high inspiratory effort [AUC 0.89 (CI 95% 0.85-0.93)], a Flow Index lower than 2.6 identified breaths with low inspiratory effort [AUC 0.80 (CI 95% 0.76-0.83)]. CONCLUSIONS: Flow Index is accurate in detecting high and low spontaneous inspiratory effort during pressure support ventilation.

Neurally Adjusted Ventilatory Assist in Newborns.

Patient-ventilator asynchrony is very common in newborns. Achieving synchrony is quite challenging because of small tidal volumes, high respiratory rates, and the presence of leaks. Leaks also cause unreliable monitoring of respiratory metrics. In addition, ventilator adjustment must take into account that infants have strong vagal reflexes and demonstrate central apnea and periodic breathing, with a high variability in breathing pattern. Neurally adjusted ventilatory assist (NAVA) is a mode of ventilation whereby the timing and amount of ventilatory assist is controlled by the patient's own neural respiratory drive. As NAVA uses the diaphragm electrical activity (Edi) as the controller signal, it is possible to deliver synchronized assist, both invasively and noninvasively (NIV-NAVA), to follow the variability in breathing pattern, and to monitor patient respiratory drive, independent of leaks. This article provides an updated review of the physiology and the scientific literature pertaining to the use of NAVA in children (neonatal and pediatric age groups). Both the invasive NAVA and NIV-NAVA publications since 2016 are summarized, as well as the use of Edi monitoring. Overall, the use of NAVA and Edi monitoring is feasible and safe. Compared with conventional ventilation, NAVA improves patient-ventilator interaction, provides lower peak inspiratory pressure, and lowers oxygen requirements. Evidence from several studies suggests improved comfort, less sedation requirements, less apnea, and some trends toward reduced length of stay and more successful extubation.

Patient-Ventilator Dyssynchrony in Critically Ill Patients.

Patient-ventilator dyssynchrony is a mismatch between the patient's respiratory efforts and mechanical ventilator delivery. Dyssynchrony can occur at any phase throughout the respiratory cycle. There are different types of dyssynchrony with different mechanisms and different potential management: trigger dyssynchrony (ineffective efforts, autotriggering, and double triggering); flow dyssynchrony, which happens during the inspiratory phase; and cycling dyssynchrony (premature cycling and delayed cycling). Dyssynchrony has been associated with patient outcomes. Thus, it is important to recognize and address these dyssynchronies at the bedside. Patient-ventilator dyssynchrony can be detected by carefully scrutinizing the airway pressure-time and flow-time waveforms displayed on the ventilator screens along with assessing the patient's comfort. Clinicians need to know how to depict these dyssynchronies at the bedside. This review aims to define the different types of dyssynchrony and then discuss the evidence for their relationship with patient outcomes and address their potential management.

Patient-ventilator asynchrony, impact on clinical outcomes and effectiveness of interventions: a systematic review and meta-analysis.

BACKGROUND: Patient-ventilator asynchrony (PVA) is a common problem in patients undergoing invasive mechanical ventilation (MV) in the intensive care unit (ICU), and may accelerate lung injury and diaphragm mis-contraction. The impact of PVA on clinical outcomes has not been systematically evaluated. Effective interventions (except for closed-loop ventilation) for reducing PVA are not well established. METHODS: We performed a systematic review and meta-analysis to investigate the impact of PVA on clinical outcomes in patients undergoing MV (Part A) and the effectiveness of interventions for patients undergoing MV except for closed-loop ventilation (Part B). We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, ClinicalTrials.gov, and WHO-ICTRP until August 2020. In Part A, we defined asynchrony index (AI) ≥ 10 or ineffective triggering index (ITI) ≥ 10 as high PVA. We compared patients having high PVA with those having low PVA. RESULTS: Eight studies in Part A and eight trials in Part B fulfilled the eligibility criteria. In Part A, five studies were related to the AI and three studies were related to the ITI. High PVA may be associated with longer duration of mechanical ventilation (mean difference, 5.16 days; 95% confidence interval [CI], 2.38 to 7.94; n = 8; certainty of evidence [CoE], low), higher ICU mortality (odds ratio [OR], 2.73; 95% CI 1.76 to 4.24; n = 6; CoE, low), and higher hospital mortality (OR, 1.94; 95% CI 1.14 to 3.30; n = 5; CoE, low). In Part B, interventions involving MV mode, tidal volume, and pressure-support level were associated with reduced PVA. Sedation protocol, sedation depth, and sedation with dexmedetomidine rather than propofol were also associated with reduced PVA. CONCLUSIONS: PVA may be associated with longer MV duration, higher ICU mortality, and higher hospital mortality. Physicians may consider monitoring PVA and adjusting ventilator settings and sedatives to reduce PVA. Further studies with adjustment for confounding factors are warranted to determine the impact of PVA on clinical outcomes. Trial registration protocols.io (URL: https://www.protocols.io/view/the-impact-of-patient-ventilator-asynchrony-in-adu-bsqtndwn , 08/27/2020).

High-flow Oxygen Therapy via Tracheostomy to Liberate COVID-19-induced ARDS from Invasive Ventilation: A Case Series.

Lung involvement with differing phenotypes characterizes COVID-19-induced acute respiratory distress syndrome (CARDS). The liberation of these patients from mechanical ventilation has been challenging. Excessive stress and strain following increased respiratory efforts spiral their vulnerable lung tissue into ventilator-induced lung injury vortex. The use of high-flow oxygen therapy via tracheostomy (HFOTTracheal)eases weaning process. As a safe option for both the patient and the healthcare workers, HFOTTracheal was successfully employed to wean two CARDS patients from the mechanical ventilator. How to cite this article: Vadi S, Phadtare S, Shetty K. High-flow Oxygen Therapy via Tracheostomy to Liberate COVID-19-induced ARDS from Invasive Ventilation: A Case Series. Indian J Crit Care Med 2021;25(6):724-728.

Flow Index: a novel, non-invasive, continuous, quantitative method to evaluate patient inspiratory effort during pressure support ventilation.

BACKGROUND: The evaluation of patient effort is pivotal during pressure support ventilation, but a non-invasive, continuous, quantitative method to assess patient inspiratory effort is still lacking. We hypothesized that the concavity of the inspiratory flow-time waveform could be useful to estimate patient's inspiratory effort. The purpose of this study was to assess whether the shape of the inspiratory flow, as quantified by a numeric indicator, could be associated with inspiratory effort during pressure support ventilation. METHODS: Twenty-four patients in pressure support ventilation were enrolled. A mathematical relationship describing the decay pattern of the inspiratory flow profile was developed. The parameter hypothesized to estimate effort was named Flow Index. Esophageal pressure, airway pressure, airflow, and volume waveforms were recorded at three support levels (maximum, minimum and baseline). The association between Flow Index and reference measures of patient effort (pressure time product and pressure generated by respiratory muscles) was evaluated using linear mixed effects models adjusted for tidal volume, respiratory rate and respiratory rate/tidal volume. RESULTS: Flow Index was different at the three pressure support levels and all group comparisons were statistically significant. In all tested models, Flow Index was independently associated with patient effort (p < 0.001). Flow Index prediction of inspiratory effort agreed with esophageal pressure-based methods. CONCLUSIONS: Flow Index is associated with patient inspiratory effort during pressure support ventilation, and may provide potentially useful information for setting inspiratory support and monitoring patient-ventilator interactions.

Identifying and managing patient–ventilator asynchrony: An international survey / Identificación y manejo de la asincronía paciente-ventilador: encuesta internacional

Objective To describe the main factors associated with proper recognition and management of patient–ventilator asynchrony (PVA). Design An analytical cross-sectional study was carried out. Setting An international study conducted in 20 countries through an online survey. Participants Physicians, respiratory therapists, nurses and physiotherapists currently working in the Intensive Care Unit (ICU). Main variables of interest Univariate and multivariate logistic regression models were used to establish associations between all variables (profession, training in mechanical ventilation, type of training program, years of experience and ICU characteristics) and the ability of HCPs to correctly identify and manage 6 PVA. Results A total of 431 healthcare professionals answered a validated survey. The main factors associated to proper recognition of PVA were: specific training program in mechanical ventilation (MV) (OR 2.27; 95%CI 1.14–4.52; p=0.019), courses with more than 100h completed (OR 2.28; 95%CI 1.29–4.03; p=0.005), and the number of ICU beds (OR 1.037; 95%CI 1.01–1.06; p=0.005). The main factor influencing the management of PVA was the correct recognition of 6 PVAs (OR 118.98; 95%CI 35.25–401.58; p<0.001). Conclusion Identifying and managing PVA using ventilator waveform analysis is influenced by many factors, including specific training programs in MV, the number of ICU beds, and the number of recognized PVAs. (AU)

Objetivo Describir los factores asociados al correcto reconocimiento y manejo de la asincronía paciente-ventilador (APV). Diseño Estudio analítico transversal. Ámbito Estudio internacional realizado en 20 países mediante una encuesta a través de Internet. Participantes Médicos, terapeutas respiratorios, enfermeras/os y fisioterapeutas que trabajan actualmente en unidades de cuidados intensivos (UCI). Principales variables de interés Se utilizó un análisis uni y multivariado para describir la asociación entre todas las variables (profesión, formación en ventilación mecánica, tipo de programa de formación, años de experiencia y características de la UCI en la cual trabajan los profesionales) con la correcta identificación y manejo de 6 APV. Resultados Un total de 431 profesionales respondieron una encuesta validada previamente. Los factores asociados a una correcta identificación de 6 APV fueron: haber completado un programa de formación específico sobre ventilación mecánica (OR: 2,27; IC 95%: 1,14-4,52; p=0,019), programa de formación con más de 100h (OR: 2,28; IC 95%: 1,29-4,03; p=0,005) y el número de camas de UCI (OR: 1,037; IC 95%: 1,01-1,06; p=0,005). El principal factor asociado a un adecuado manejo de la APV fue la correcta identificación de 6 APV (OR: 118,98; IC 95%: 35,25-401,58; p<0,001). Conclusiones La identificación y el manejo de la asincronía paciente-ventilador, mediante el análisis de las curvas del ventilador está influenciada por programas de formación, específicos sobre ventilación mecánica, el número de camas de la UCI y el número de asincronías identificadas. (AU)

Mechanical ventilation strategy for pulmonary rehabilitation based on patient-ventilator interaction.

Mechanical ventilation is an effective medical means in the treatment of patients with critically ill, COVID-19 and other pulmonary diseases. During the mechanical ventilation and the weaning process, the conduct of pulmonary rehabilitation is essential for the patients to improve the spontaneous breathing ability and to avoid the weakness of respiratory muscles and other pulmonary functional trauma. However, inappropriate mechanical ventilation strategies for pulmonary rehabilitation often result in weaning difficulties and other ventilator complications. In this article, the mechanical ventilation strategies for pulmonary rehabilitation are studied based on the analysis of patient-ventilator interaction. A pneumatic model of the mechanical ventilation system is established to determine the mathematical relationship among the pressure, the volumetric flow, and the tidal volume. Each ventilation cycle is divided into four phases according to the different respiratory characteristics of patients, namely, the triggering phase, the inhalation phase, the switching phase, and the exhalation phase. The control parameters of the ventilator are adjusted by analyzing the interaction between the patient and the ventilator at different phases. A novel fuzzy control method of the ventilator support pressure is proposed in the pressure support ventilation mode. According to the fuzzy rules in this research, the plateau pressure can be obtained by the trigger sensitivity and the patient's inspiratory effort. An experiment prototype of the ventilator is established to verify the accuracy of the pneumatic model and the validity of the mechanical ventilation strategies proposed in this article. In addition, through the discussion of the patient-ventilator asynchrony, the strategies for mechanical ventilation can be adjusted accordingly. The results of this research are meaningful for the clinical operation of mechanical ventilation. Besides, these results provide a theoretical basis for the future research on the intelligent control of ventilator and the automation of weaning process.

Identifying and managing patient-ventilator asynchrony: An international survey.

OBJECTIVE: To describe the main factors associated with proper recognition and management of patient-ventilator asynchrony (PVA). DESIGN: An analytical cross-sectional study was carried out. SETTING: An international study conducted in 20 countries through an online survey. PARTICIPANTS: Physicians, respiratory therapists, nurses and physiotherapists currently working in the Intensive Care Unit (ICU). MAIN VARIABLES OF INTEREST: Univariate and multivariate logistic regression models were used to establish associations between all variables (profession, training in mechanical ventilation, type of training program, years of experience and ICU characteristics) and the ability of HCPs to correctly identify and manage 6 PVA. RESULTS: A total of 431 healthcare professionals answered a validated survey. The main factors associated to proper recognition of PVA were: specific training program in mechanical ventilation (MV) (OR 2.27; 95%CI 1.14-4.52; p=0.019), courses with more than 100h completed (OR 2.28; 95%CI 1.29-4.03; p=0.005), and the number of ICU beds (OR 1.037; 95%CI 1.01-1.06; p=0.005). The main factor influencing the management of PVA was the correct recognition of 6 PVAs (OR 118.98; 95%CI 35.25-401.58; p<0.001). CONCLUSION: Identifying and managing PVA using ventilator waveform analysis is influenced by many factors, including specific training programs in MV, the number of ICU beds, and the number of recognized PVAs.

Impacto de las asincronías en el pronóstico del paciente bajo ventilación mecánica invasiva / Prognosis impact of the asynchronies in the mechanical ventilated patient / Impacto das assincronias no prognóstico do paciente com ventilação mecânica invasiva

Resumen: La ventilación mecánica es común en pacientes críticos. La asincronía paciente-ventilador existe cuando las fases de la respiración administradas por el ventilador no coinciden con las del paciente. Las asincronías son frecuentes e infradiagnosticadas, éstas se han asociado con desenlaces desfavorables como son: mayor duración de ventilación mecánica, estancia en la unidad de terapia intensiva, mortalidad, incomodidad del paciente, alteraciones del sueño y disfunción diafragmática. Esta revisión describe los desenlaces adversos reportados que se han asociado a la presencia de asincronías en pacientes adultos bajo ventilación mecánica invasiva. La evidencia actual sugiere que el mejor enfoque para manejar las asincronías es ajustar la configuración del ventilador y mejorar su detección. Si bien la mayoría de la evidencia proviene de estudios observacionales y ensayos clínicos aleatorizados realizados en poblaciones heterogéneas y con un número limitado de pacientes, los resultados sugieren desenlaces desfavorables clínicamente significativos en los pacientes que experimentan un índice de asincronía elevado. Por lo anterior, es necesario generar mayor evidencia en este tópico.

Abstract: Mechanical ventilation is common in critically ill patients. Patient-ventilator asynchrony exists when the breathing phases administered by the ventilator do not match those of the patient. They are frequent but underdiagnosed, and have been associated with worse outcomes because they negatively affect patient comfort, length of mechanical ventilation, length of stay in the intensive care unit and mortality. This review describes the negative outcomes associated with the presence of asynchronies in adult patients with invasive mechanical ventilation. Current evidence suggests that the best approach to handle asynchronies is to adjust the fan settings and improve the quality of detection. While most of this evidence comes from observational studies and randomized clinical trials which were done with heterogeneous populations and a limited number of patients, the results suggest less favorable clinically significant outcomes in patients with asynchronies. So it is necessary to generate more evidence in this topic.

Resumo: A ventilação mecânica é comum em pacientes críticos. A assincronia paciente-ventilador existe quando as fases da respiração fornecida pelo ventilador não coincidem com as do paciente. As assincronas são frequentes e subdiagnosticadas, tendo sido associadas a desfechos desfavoráveis como: maior tempo de ventilação mecânica, permanência em unidade de terapia intensiva, mortalidade, desconforto do paciente, distúrbios do sono e disfunção diafragmática. Esta revisão descreve os resultados adversos relatados que foram associados à presença de assincronia em pacientes adultos sob ventilação mecânica invasiva. A evidência atual sugere que a melhor abordagem para gerenciar assincronias é ajustar as configurações do ventilador e melhorar a detecção do ventilador. Embora a maioria das evidências provenha de estudos observacionais e ensaios clínicos randomizados conduzidos em populações heterogêneas e com um número limitado de pacientes, os resultados sugerem resultados clinicamente desfavoráveis significativos em pacientes que apresentam uma alta taxa de assincronia. Portanto, é necessário gerar mais evidências sobre este tema.

Pressure Support Ventilation (PSV) versus Neurally Adjusted Ventilatory Assist (NAVA) in difficult to wean pediatric ARDS patients: a physiologic crossover study.

BACKGROUND: Neurally adjusted ventilatory assist (NAVA) is an innovative mode for assisted ventilation that improves patient-ventilator interaction in children. The aim of this study was to assess the effects of patient-ventilator interaction comparing NAVA with pressure support ventilation (PSV) in patients difficult to wean from mechanical ventilation after moderate pediatric acute respiratory distress syndrome (PARDS). METHODS: In this physiological crossover study, 12 patients admitted in the Pediatric Intensive Care Unit (PICU) with moderate PARDS failing up to 3 spontaneous breathing trials in less than 7 days, were enrolled. Patients underwent three study conditions lasting 1 h each: PSV1, NAVA and PSV2. RESULTS: The Asynchrony Index (AI) was significantly reduced during the NAVA trial compared to both the PSV1 and PSV2 trials (p = 0.001). During the NAVA trial, the inspiratory and expiratory trigger delays were significantly shorter compared to those obtained during PSV1 and PSV2 trials (Delaytrinspp < 0.001, Delaytrexpp = 0.013). These results explain the significantly longer Timesync observed during the NAVA trial (p < 0.001). In terms of gas exchanges, PaO2 value significantly improved in the NAVA trial with respect to the PSV trials (p < 0.02). The PaO2/FiO2 ratio showed a significant improvement during the NAVA trial compared to both the PSV1 and PSV2 trials (p = 0.004). CONCLUSIONS: In this specific PICU population, presenting difficulty in weaning after PARDS, NAVA was associated with a reduction of the AI and a significant improvement in oxygenation compared to PSV mode. TRIAL REGISTRATION: ClinicalTrial.gov Identifier: NCT04360590 "Retrospectively registered".

Feasibility of neurally synchronized and proportional negative pressure ventilation in a small animal model.

RATIONALE: Synchronized positive pressure ventilation is possible using diaphragm electrical activity (EAdi) to control the ventilator. It is unknown whether EAdi can be used to control negative pressure ventilation. AIM: To evaluate the feasibility of using EAdi to control negative pressure ventilation. METHODS: Fourteen anesthetized rats were studied (380-590 g) during control, resistive breathing, acute lung injury or CO2 rebreathing. Positive pressure continuous neurally adjusted ventilatory assist (cNAVAP+ ) was applied via intubation. Negative pressure cNAVA (cNAVAP- ) was applied with the animal placed in a sealed box. In part 1, automatic stepwise increments in cNAVA level by 0.2 cmH2 O/µV every 30 s was applied for cNAVAP+ , cNAVAP- , and a 50/50 combination of the two (cNAVAP± ). In part 2: During 5-min ventilation with cNAVAP+ or cNAVAP- we measured circuit, box, and esophageal (Pes) pressure, EAdi, blood pressure, and arterial blood gases. RESULTS: Part 1: During cNAVAP+ , pressure in the circuit increased with increasing cNAVA levels, reaching a plateau, and similarly for cNAVAP- , albeit reversed in sign. This was associated with downregulation of the EAdi. Pes swings became less negative with cNAVAP+ but, in contrast, Pes swings were more negative during increasing cNAVAP- levels. Increasing the cNAVA level during cNAVAP± resulted in an intermediate response. Part 2: no significant differences were observed for box/circuit pressures, EAdi, blood pressure, or arterial blood gases. Pes swings during cNAVAP- were significantly more negative than during cNAVAP+ . CONCLUSION: Negative pressure ventilation synchronized and proportional to the diaphragm activity is feasible in small animals.

Patient-Ventilator Interaction With Noninvasive Proportional Assist Ventilation in Subjects With COPD.

BACKGROUND: To investigate patient-ventilator interaction during different levels of noninvasive proportional assist ventilation (PAV) compared with noninvasive pressure support ventilation (PSV). METHODS: Fifteen subjects with severe COPD and hypercapnia were consecutively recruited. After the baseline assessment of unassisted spontaneous breathing, 3 levels of ventilatory support were applied. The proportional assist (PA) and pressure support (PS) levels were set by subject comfort. PA-, PS- or PA+, PS+ were set at 25% more or less of PA or PS (PA- = 75% PA, PA+ = 125% PA, PS- = 75% PS, PS+ = 125% PS). Each level lasted at least 20 min. To demonstrate the patient-ventilator interaction, the neural respiratory drive, respiratory muscle effort, flow signal, and airway pressure were simultaneously monitored. RESULTS: The expiratory cycle delay (time between the termination of the diaphragm electromyogram [EMGdi] signal and the end of the inspiratory flow) progressively increased with increasing assist level in both modes. However, compared with PSV, the expiratory cycle delay was significantly longer in each assist level during noninvasive PAV. The runaway phenomenon was observed in PA+. The time between the peak EMGdi signal and the maximum value of the flow signal and the time difference between the peak EMGdi signal and the maximum value of inspiratory pressure were significantly increased with the increasing assist level of PAV. CONCLUSIONS: The expiratory cycle delay of noninvasive PAV was significantly longer than that of noninvasive PSV in the subjects with COPD with respiratory failure. During the levels of PAV, the lag time between neural respiratory drive and airway pressurization was significantly increased and the "runaway" phenomenon may be observed. (ClinicalTrials.gov registration NCT01782768.).