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.

Objective and subjective cognition in survivors of COVID-19 one year after ICU discharge: the role of demographic, clinical, and emotional factors.

BACKGROUND: Intensive Care Unit (ICU) COVID-19 survivors may present long-term cognitive and emotional difficulties after hospital discharge. This study aims to characterize the neuropsychological dysfunction of COVID-19 survivors 12 months after ICU discharge, and to study whether the use of a measure of perceived cognitive deficit allows the detection of objective cognitive impairment. We also explore the relationship between demographic, clinical and emotional factors, and both objective and subjective cognitive deficits. METHODS: Critically ill COVID-19 survivors from two medical ICUs underwent cognitive and emotional assessment one year after discharge. The perception of cognitive deficit and emotional state was screened through self-rated questionnaires (Perceived Deficits Questionnaire, Hospital Anxiety and Depression Scale and Davidson Trauma Scale), and a comprehensive neuropsychological evaluation was carried out. Demographic and clinical data from ICU admission were collected retrospectively. RESULTS: Out of eighty participants included in the final analysis, 31.3% were women, 61.3% received mechanical ventilation and the median age of patients was 60.73 years. Objective cognitive impairment was observed in 30% of COVID-19 survivors. The worst performance was detected in executive functions, processing speed and recognition memory. Almost one in three patients manifested cognitive complaints, and 22.5%, 26.3% and 27.5% reported anxiety, depression and post-traumatic stress disorder (PTSD) symptoms, respectively. No significant differences were found in the perception of cognitive deficit between patients with and without objective cognitive impairment. Gender and PTSD symptomatology were significantly associated with perceived cognitive deficit, and cognitive reserve with objective cognitive impairment. CONCLUSIONS: One-third of COVID-19 survivors suffered objective cognitive impairment with a frontal-subcortical dysfunction 12 months after ICU discharge. Emotional disturbances and perceived cognitive deficits were common. Female gender and PTSD symptoms emerged as predictive factors for perceiving worse cognitive performance. Cognitive reserve emerged as a protective factor for objective cognitive functioning. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04422444; June 9, 2021.

The Effect of Clusters of Double Triggering and Ineffective Efforts in Critically Ill Patients.

OBJECTIVES: To characterize clusters of double triggering and ineffective inspiratory efforts throughout mechanical ventilation and investigate their associations with mortality and duration of ICU stay and mechanical ventilation. DESIGN: Registry-based, real-world study. BACKGROUND: Asynchronies during invasive mechanical ventilation can occur as isolated events or in clusters and might be related to clinical outcomes. SUBJECTS: Adults requiring mechanical ventilation greater than 24 hours for whom greater than or equal to 70% of ventilator waveforms were available. INTERVENTIONS: We identified clusters of double triggering and ineffective inspiratory efforts and determined their power and duration. We used Fine-Gray's competing risk model to analyze their effects on mortality and generalized linear models to analyze their effects on duration of mechanical ventilation and ICU stay. MEASUREMENTS AND MAIN RESULTS: We analyzed 58,625,796 breaths from 180 patients. All patients had clusters (mean/d, 8.2 [5.4-10.6]; mean power, 54.5 [29.6-111.4]; mean duration, 20.3 min [12.2-34.9 min]). Clusters were less frequent during the first 48 hours (5.5 [2.5-10] vs 7.6 [4.4-9.9] in the remaining period [p = 0.027]). Total number of clusters/d was positively associated with the probability of being discharged alive considering the total period of mechanical ventilation (p = 0.001). Power and duration were similar in the two periods. Power was associated with the probability of being discharged dead (p = 0.03), longer mechanical ventilation (p < 0.001), and longer ICU stay (p = 0.035); cluster duration was associated with longer ICU stay (p = 0.027). CONCLUSIONS: Clusters of double triggering and ineffective inspiratory efforts are common. Although higher numbers of clusters might indicate better chances of survival, clusters with greater power and duration indicate a risk of worse clinical outcomes.

The central nervous system during lung injury and mechanical ventilation: a narrative review.

Mechanical ventilation induces a number of systemic responses for which the brain plays an essential role. During the last decade, substantial evidence has emerged showing that the brain modifies pulmonary responses to physical and biological stimuli by various mechanisms, including the modulation of neuroinflammatory reflexes and the onset of abnormal breathing patterns. Afferent signals and circulating factors from injured peripheral tissues, including the lung, can induce neuronal reprogramming, potentially contributing to neurocognitive dysfunction and psychological alterations seen in critically ill patients. These impairments are ubiquitous in the presence of positive pressure ventilation. This narrative review summarises current evidence of lung-brain crosstalk in patients receiving mechanical ventilation and describes the clinical implications of this crosstalk. Further, it proposes directions for future research ranging from identifying mechanisms of multiorgan failure to mitigating long-term sequelae after critical illness.

Cognitive phenotypes 1 month after ICU discharge in mechanically ventilated patients: a prospective observational cohort study.

BACKGROUND: ICU patients undergoing invasive mechanical ventilation experience cognitive decline associated with their critical illness and its management. The early detection of different cognitive phenotypes might reveal the involvement of diverse pathophysiological mechanisms and help to clarify the role of the precipitating and predisposing factors. Our main objective is to identify cognitive phenotypes in critically ill survivors 1 month after ICU discharge using an unsupervised machine learning method, and to contrast them with the classical approach of cognitive impairment assessment. For descriptive purposes, precipitating and predisposing factors for cognitive impairment were explored. METHODS: A total of 156 mechanically ventilated critically ill patients from two medical/surgical ICUs were prospectively studied. Patients with previous cognitive impairment, neurological or psychiatric diagnosis were excluded. Clinical variables were registered during ICU stay, and 100 patients were cognitively assessed 1 month after ICU discharge. The unsupervised machine learning K-means clustering algorithm was applied to detect cognitive phenotypes. Exploratory analyses were used to study precipitating and predisposing factors for cognitive impairment. RESULTS: K-means testing identified three clusters (K) of patients with different cognitive phenotypes: K1 (n = 13), severe cognitive impairment in speed of processing (92%) and executive function (85%); K2 (n = 33), moderate-to-severe deficits in learning-memory (55%), memory retrieval (67%), speed of processing (36.4%) and executive function (33.3%); and K3 (n = 46), normal cognitive profile in 89% of patients. Using the classical approach, moderate-to-severe cognitive decline was recorded in 47% of patients, while the K-means method accurately classified 85.9%. The descriptive analysis showed significant differences in days (p = 0.016) and doses (p = 0.039) with opioid treatment in K1 vs. K2 and K3. In K2, there were more women, patients were older and had more comorbidities (p = 0.001) than in K1 or K3. Cognitive reserve was significantly (p = 0.001) higher in K3 than in K1 or K2. CONCLUSION: One month after ICU discharge, three groups of patients with different cognitive phenotypes were identified through an unsupervised machine learning method. This novel approach improved the classical classification of cognitive impairment in ICU survivors. In the exploratory analysis, gender, age and the level of cognitive reserve emerged as relevant predisposing factors for cognitive impairment in ICU patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier:NCT02390024; March 17,2015.

Effects of sedatives and opioids on trigger and cycling asynchronies throughout mechanical ventilation: an observational study in a large dataset from critically ill patients.

BACKGROUND: In critically ill patients, poor patient-ventilator interaction may worsen outcomes. Although sedatives are often administered to improve comfort and facilitate ventilation, they can be deleterious. Whether opioids improve asynchronies with fewer negative effects is unknown. We hypothesized that opioids alone would improve asynchronies and result in more wakeful patients than sedatives alone or sedatives-plus-opioids. METHODS: This prospective multicenter observational trial enrolled critically ill adults mechanically ventilated (MV) > 24 h. We compared asynchronies and sedation depth in patients receiving sedatives, opioids, or both. We recorded sedation level and doses of sedatives and opioids. BetterCare™ software continuously registered ineffective inspiratory efforts during expiration (IEE), double cycling (DC), and asynchrony index (AI) as well as MV modes. All variables were averaged per day. We used linear mixed-effects models to analyze the relationships between asynchronies, sedation level, and sedative and opioid doses. RESULTS: In 79 patients, 14,166,469 breaths were recorded during 579 days of MV. Overall asynchronies were not significantly different in days classified as sedatives-only, opioids-only, and sedatives-plus-opioids and were more prevalent in days classified as no-drugs than in those classified as sedatives-plus-opioids, irrespective of the ventilatory mode. Sedative doses were associated with sedation level and with reduced DC (p < 0.0001) in sedatives-only days. However, on days classified as sedatives-plus-opioids, higher sedative doses and deeper sedation had more IEE (p < 0.0001) and higher AI (p = 0.0004). Opioid dosing was inversely associated with overall asynchronies (p < 0.001) without worsening sedation levels into morbid ranges. CONCLUSIONS: Sedatives, whether alone or combined with opioids, do not result in better patient-ventilator interaction than opioids alone, in any ventilatory mode. Higher opioid dose (alone or with sedatives) was associated with lower AI without depressing consciousness. Higher sedative doses administered alone were associated only with less DC. TRIAL REGISTRATION: ClinicalTrial.gov, NCT03451461.

Double Cycling During Mechanical Ventilation: Frequency, Mechanisms, and Physiologic Implications.

OBJECTIVES: Double cycling generates larger than expected tidal volumes that contribute to lung injury. We analyzed the incidence, mechanisms, and physiologic implications of double cycling during volume- and pressure-targeted mechanical ventilation in critically ill patients. DESIGN: Prospective, observational study. SETTING: Three general ICUs in Spain. PATIENTS: Sixty-seven continuously monitored adult patients undergoing volume control-continuous mandatory ventilation with constant flow, volume control-continuous mandatory ventilation with decelerated flow, or pressure control-continuous mandatory mechanical ventilation for longer than 24 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 9,251 hours of mechanical ventilation corresponding to 9,694,573 breaths. Double cycling occurred in 0.6%. All patients had double cycling; however, the distribution of double cycling varied over time. The mean percentage (95% CI) of double cycling was higher in pressure control-continuous mandatory ventilation 0.54 (0.34-0.87) than in volume control-continuous mandatory ventilation with constant flow 0.27 (0.19-0.38) or volume control-continuous mandatory ventilation with decelerated flow 0.11 (0.06-0.20). Tidal volume in double-cycled breaths was higher in volume control-continuous mandatory ventilation with constant flow and volume control-continuous mandatory ventilation with decelerated flow than in pressure control-continuous mandatory ventilation. Double-cycled breaths were patient triggered in 65.4% and reverse triggered (diaphragmatic contraction stimulated by a previous passive ventilator breath) in 34.6% of cases; the difference was largest in volume control-continuous mandatory ventilation with decelerated flow (80.7% patient triggered and 19.3% reverse triggered). Peak pressure of the second stacked breath was highest in volume control-continuous mandatory ventilation with constant flow regardless of trigger type. Various physiologic factors, none mutually exclusive, were associated with double cycling. CONCLUSIONS: Double cycling is uncommon but occurs in all patients. Periods without double cycling alternate with periods with clusters of double cycling. The volume of the stacked breaths can double the set tidal volume in volume control-continuous mandatory ventilation with constant flow. Gas delivery must be tailored to neuroventilatory demand because interdependent ventilator setting-related physiologic factors can contribute to double cycling. One third of double-cycled breaths were reverse triggered, suggesting that repeated respiratory muscle activation after time-initiated ventilator breaths occurs more often than expected.

Impact of Recruitment on Static and Dynamic Lung Strain in Acute Respiratory Distress Syndrome.

BACKGROUND: Lung strain, defined as the ratio between end-inspiratory volume and functional residual capacity, is a marker of the mechanical load during ventilation. However, changes in lung volumes in response to pressures may occur in injured lungs and modify strain values. The objective of this study was to clarify the role of recruitment in strain measurements. METHODS: Six oleic acid-injured pigs were ventilated at positive end-expiratory pressure (PEEP) 0 and 10 cm H2O before and after a recruitment maneuver (PEEP = 20 cm H2O). Lung volumes were measured by helium dilution and inductance plethysmography. In addition, six patients with moderate-to-severe acute respiratory distress syndrome were ventilated with three strategies (peak inspiratory pressure/PEEP: 20/8, 32/8, and 32/20 cm H2O). Lung volumes were measured in computed tomography slices acquired at end-expiration and end-inspiration. From both series, recruited volume and lung strain (total, dynamic, and static) were computed. RESULTS: In the animal model, recruitment caused a significant decrease in dynamic strain (from [mean ± SD] 0.4 ± 0.12 to 0.25 ± 0.07, P < 0.01), while increasing the static component. In patients, total strain remained constant for the three ventilatory settings (0.35 ± 0.1, 0.37 ± 0.11, and 0.32 ± 0.1, respectively). Increases in tidal volume had no significant effects. Increasing PEEP constantly decreased dynamic strain (0.35 ± 0.1, 0.32 ± 0.1, and 0.04+0.03, P < 0.05) and increased static strain (0, 0.06 ± 0.06, and 0.28 ± 0.11, P < 0.05). The changes in dynamic and total strain among patients were correlated to the amount of recruited volume. An analysis restricted to the changes in normally aerated lung yielded similar results. CONCLUSION: Recruitment causes a shift from dynamic to static strain in early acute respiratory distress syndrome.

Automatic detection of ventilatory modes during invasive mechanical ventilation.

BACKGROUND: Expert systems can help alleviate problems related to the shortage of human resources in critical care, offering expert advice in complex situations. Expert systems use contextual information to provide advice to staff. In mechanical ventilation, it is crucial for an expert system to be able to determine the ventilatory mode in use. Different manufacturers have assigned different names to similar or even identical ventilatory modes so an expert system should be able to detect the ventilatory mode. The aim of this study is to evaluate the accuracy of an algorithm to detect the ventilatory mode in use. METHODS: We compared the results of a two-step algorithm designed to identify seven ventilatory modes. The algorithm was built into a software platform (BetterCare® system, Better Care SL; Barcelona, Spain) that acquires ventilatory signals through the data port of mechanical ventilators. The sample analyzed compared data from consecutive adult patients who underwent >24 h of mechanical ventilation in intensive care units (ICUs) at two hospitals. We used Cohen's kappa statistics to analyze the agreement between the results obtained with the algorithm and those recorded by ICU staff. RESULTS: We analyzed 486 records from 73 patients. The algorithm correctly labeled the ventilatory mode in 433 (89 %). We found an unweighted Cohen's kappa index of 84.5 % [CI (95 %) = (80.5 %: 88.4 %)]. CONCLUSIONS: The computerized algorithm can reliably identify ventilatory mode.

Early activation of pro-fibrotic WNT5A in sepsis-induced acute lung injury.

INTRODUCTION: The mechanisms of lung repair and fibrosis in the acute respiratory distress syndrome (ARDS) are poorly known. Since the role of WNT/ß-catenin signaling appears to be central to lung healing and fibrosis, we hypothesized that this pathway is activated very early in the lungs after sepsis. METHODS: We tested our hypothesis using a three-step experimental design: (1) in vitro lung cell injury model with human bronchial epithelial BEAS-2B and lung fibroblasts (MRC-5) cells exposed to endotoxin for 18 hours; (2) an animal model of sepsis-induced ARDS induced by cecal ligation and perforation, and (3) lung biopsies from patients who died within the first 24 hours of septic ARDS. We examined changes in protein levels of target genes involved in the Wnt pathway, including WNT5A, non-phospho (Ser33/37/Thr41) ß-catenin, matrix metalloproteinase-7 (MMP7), cyclin D1, and vascular endothelial growth factor (VEGF) by Western blotting and immunohistochemistry. Finally, we validated the main gene targets of this pathway in experimental animals and human lungs. RESULTS: Protein levels of WNT5A, non-phospho (Ser33/37/Thr41) ß-catenin, total ß-catenin, MMP7, cyclin D1, and VEGF increased after endotoxin stimulation in BEAS-2B and MRC-5 cells. Lungs from septic animals and from septic humans demonstrated acute lung inflammation, collagen deposition, and marked increase of WNT5A and MMP7 protein levels. CONCLUSIONS: Our findings suggest that the WNT/ß-catenin signaling pathway is activated very early in sepsis-induced ARDS and could play an important role in lung repair and fibrosis. Modulation of this pathway might represent a potential target for treatment for septic and ARDS patients.

Occurrence, co-occurrence and persistence of symptoms of depression and post-traumatic stress disorder in survivors of COVID-19 critical illness.

Background: Intensive care unit (ICU) admission and invasive mechanical ventilation (IMV) are associated with psychological distress and trauma. The COVID-19 pandemic brought with it a series of additional long-lasting stressful and traumatic experiences. However, little is known about comorbid depression and post-traumatic stress disorder (PTSD).Objective: To examine the occurrence, co-occurrence, and persistence of clinically significant symptoms of depression and PTSD, and their predictive factors, in COVID-19 critical illness survivors.Method: Single-centre prospective observational study in adult survivors of COVID-19 with ≥24 h of ICU admission. Patients were assessed one and 12 months after ICU discharge using the depression subscale of the Hospital Anxiety and Depression Scale and the Davidson Trauma Scale. Differences in isolated and comorbid symptoms of depression and PTSD between patients with and without IMV and predictors of the occurrence and persistence of symptoms of these mental disorders were analysed.Results: Eighty-nine patients (42 with IMV) completed the 1-month follow-up and 71 (34 with IMV) completed the 12-month follow-up. One month after discharge, 29.2% of patients had symptoms of depression and 36% had symptoms of PTSD; after one year, the respective figures were 32.4% and 31%. Coexistence of depressive and PTSD symptoms accounted for approximately half of all symptomatic cases. Isolated PTSD symptoms were more frequent in patients with IMV (p≤.014). The need for IMV was associated with the occurrence at one month (OR = 6.098, p = .005) and persistence at 12 months (OR = 3.271, p = .030) of symptoms of either of these two mental disorders.Conclusions: Comorbid depressive and PTSD symptoms were highly frequent in our cohort of COVID-19 critical illness survivors. The need for IMV predicted short-term occurrence and long-term persistence of symptoms of these mental disorders, especially PTSD symptoms. The specific role of dyspnea in the association between IMV and post-ICU mental disorders deserves further investigation.Trial registration: ClinicalTrials.gov identifier: NCT04422444.

Clinically significant depressive and post-traumatic stress disorder symptoms in survivors of COVID-19 critical illness, especially in patients who had undergone invasive mechanical ventilation, were highly frequent, occurred soon after discharge, and persisted over the long term.

Organ crosstalk during acute lung injury, acute respiratory distress syndrome, and mechanical ventilation.

PURPOSE OF REVIEW: Multiple organ failure is the main cause of morbidity and mortality in acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) patients. Moreover, survivors of both ALI and ARDS often show significant neurocognitive decline at discharge. These data suggest a deleterious organ crosstalk between lungs and distal organs. This article reviews the recent literature concerning the role of this organ crosstalk during ALI, ARDS, and mechanical ventilation, especially focusing on brain-lung communication. RECENT FINDINGS: Numerous pulmonary and extrapulmonary disorders could predispose critically ill patients to ALI and ARDS. Mechanical ventilation, although a lifesaving intervention, could contribute by modulating the mechanisms involved in the pathophysiology of lung damage and their impact on remote organs. Emerging clinical and experimental evidence supports the hypothesis of a multidirectional organ crosstalk between lungs and distal organs. SUMMARY: Organ crosstalk is an emerging area of research in lung disease in critically ill patients. The findings of these studies are clinically relevant and show the importance of an integrative approach in the management of critical patients. However, further studies are necessary to understand the complex interactions concurring in these pathologies.

Injurious mechanical ventilation affects neuronal activation in ventilated rats.

INTRODUCTION: Survivors of critical illness often have significant long-term brain dysfunction, and routine clinical procedures like mechanical ventilation (MV) may affect long-term brain outcome. We aimed to investigate the effect of the increase of tidal volume (Vt) on brain activation in a rat model. METHODS: Male Sprague Dawley rats were randomized to three groups: 1) Basal: anesthetized unventilated animals, 2) low Vt (LVt): MV for three hours with Vt 8 ml/kg and zero positive end-expiratory pressure (ZEEP), and 3) high Vt (HVt) MV for three hours with Vt 30 ml/kg and ZEEP. We measured lung mechanics, mean arterial pressure (MAP), arterial blood gases, and plasma and lung levels of cytokines. We used immunohistochemistry to examine c-fos as a marker of neuronal activation. An additional group of spontaneously breathing rats was added to discriminate the effect of surgical procedure and anesthesia in the brain. RESULTS: After three hours on LVt, PaO2 decreased and PaCO2 increased significantly. MAP and compliance remained stable in MV groups. Systemic and pulmonary inflammation was higher in MV rats than in unventilated rats. Plasma TNFα was significantly higher in HVt than in LVt. Immunopositive cells to c-fos in the retrosplenial cortex and thalamus increased significantly in HVt rats but not in LVt or unventilated rats. CONCLUSIONS: MV promoted brain activation. The intensity of the response was higher in HVt animals, suggesting an iatrogenic effect of MV on the brain. These findings suggest that this novel cross-talking mechanism between the lung and the brain should be explored in patients undergoing MV.

Longitudinal Changes in Patient-Ventilator Asynchronies and Respiratory System Mechanics Before and After Tracheostomy.

BACKGROUND: This was a pilot study to analyze the effects of tracheostomy on patient-ventilator asynchronies and respiratory system mechanics. Data were extracted from an ongoing prospective, real-world database that stores continuous output from ventilators and bedside monitors. Twenty adult subjects were on mechanical ventilation and were tracheostomized during an ICU stay: 55% were admitted to the ICU for respiratory failure and 35% for neurologic conditions; the median duration of mechanical ventilation before tracheostomy was 12 d; and the median duration of mechanical ventilation was 16 d. METHODS: We compared patient-ventilator asynchronies (the overall asynchrony index and the rates of specific asynchronies) and respiratory system mechanics (respiratory-system compliance and airway resistance) during the 24 h before tracheostomy versus the 24 h after tracheostomy. We analyzed possible differences in these variables among the subjects who underwent surgical versus percutaneous tracheostomy. To compare longitudinal changes in the variables, we used linear mixed-effects models for repeated measures along time in different observation periods. A total of 920 h of mechanical ventilation were analyzed. RESULTS: Respiratory mechanics and asynchronies did not differ significantly between the 24-h periods before and after tracheostomy: compliance of the respiratory system median (IQR) (47.9 [41.3 - 54.6] mL/cm H2O vs 47.6 [40.9 - 54.3] mL/cm H2O; P = .94), airway resistance (9.3 [7.5 - 11.1] cm H2O/L/s vs 7.0 [5.2 - 8.8] cm H2O/L/s; P = .07), asynchrony index (2.0% [1.1 - 3.6%] vs 4.1% [2.3 - 7.6%]; P = .09), ineffective expiratory efforts (0.9% [0.4 - 1.8%] vs 2.2% [1.0 - 4.4%]; P = .08), double cycling (0.5% [0.3 - 1.0%] vs 0.9% [0.5 - 1.9%]; P = .24), and percentage of air trapping (7.6% [4.2 - 13.8%] vs 10.6% [5.9 - 19.2%]; P = .43). No differences in respiratory mechanics or patient-ventilator asynchronies were observed between percutaneous and surgical procedures. CONCLUSIONS: Tracheostomy did not affect patient-ventilator asynchronies or respiratory mechanics within 24 h before and after the procedure.

Virtual Reality-Based Early Neurocognitive Stimulation in Critically Ill Patients: A Pilot Randomized Clinical Trial.

This study focuses on the application of a non-immersive virtual reality (VR)-based neurocognitive intervention in critically ill patients. Our aim was to assess the feasibility of direct outcome measures to detect the impact of this digital therapy on patients' cognitive and emotional outcomes. Seventy-two mechanically ventilated adult patients were randomly assigned to the "treatment as usual" (TAU, n = 38) or the "early neurocognitive stimulation" (ENRIC, n = 34) groups. All patients received standard intensive care unit (ICU) care. Patients in the ENRIC group also received adjuvant neurocognitive stimulation during the ICU stay. Outcome measures were a full neuropsychological battery and two mental health questionnaires. A total of 42 patients (21 ENRIC) completed assessment one month after ICU discharge, and 24 (10 ENRIC) one year later. At one-month follow-up, ENRIC patients had better working memory scores (p = 0.009, d = 0.363) and showed up to 50% less non-specific anxiety (11.8% vs. 21.1%) and depression (5.9% vs. 10.5%) than TAU patients. A general linear model of repeated measures reported a main effect of group, but not of time or group-time interaction, on working memory, with ENRIC patients outperforming TAU patients (p = 0.008, ηp2 = 0.282). Our results suggest that non-immersive VR-based neurocognitive stimulation may help improve short-term working memory outcomes in survivors of critical illness. Moreover, this advantage could be maintained in the long term. An efficacy trial in a larger sample of participants is feasible and must be conducted.

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation.

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Monitoring Asynchrony During Invasive Mechanical Ventilation.

Mechanical ventilation in critically ill patients must effectively unload inspiratory muscles and provide safe ventilation (ie, enhancing gas exchange, protect the lungs and the diaphragm). To do that, the ventilator should be in synchrony with patient's respiratory rhythm. The complexity of such interplay leads to several concerning issues that clinicians should be able to recognize. Asynchrony between the patient and the ventilator may induce several deleterious effects that require a proper physiological understanding to recognize and manage them. Different tools have been developed and proposed beyond the careful analysis of the ventilator waveforms to help clinicians in the decision-making process. Moreover, appropriate handling of asynchrony requires clinical skills, physiological knowledge, and suitable medication management. New technologies and devices are changing our daily practice, from automated real-time recognition of asynchronies and their distribution during mechanical ventilation, to smart alarms and artificial intelligence algorithms based on physiological big data and personalized medicine. Our goal as clinicians is to provide care of patients based on the most accurate and current knowledge, and to incorporate new technological methods to facilitate and improve the care of the critically ill.

Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation.

Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm's performance was compared versus the gold standard (the ventilator's waveform recordings for CP-VI were scored visually by three experts; Fleiss' kappa = 0.90 (0.87-0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient's own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78-0.86) and 0.78 (0.78-0.85), and accuracies of 0.93 (0.89-0.93) and 0.89 (0.89-0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.