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.

Effect of a low versus intermediate tidal volume strategy on pulmonary complications in patients at risk of acute respiratory distress syndrome-a randomized clinical trial.

Introduction: There is no consensus on whether invasive ventilation should use low tidal volumes (VT) to prevent lung complications in patients at risk of acute respiratory distress syndrome (ARDS). The purpose of this study is to determine if a low VT strategy is more effective than an intermediate VT strategy in preventing pulmonary complications. Methods: A randomized clinical trial was conducted in invasively ventilated patients with a lung injury prediction score (LIPS) of >4 performed in the intensive care units of 10 hospitals in Spain and one in the United States of America (USA) from 3 November 2014 to 30 August 2016. Patients were randomized to invasive ventilation using low VT (≤ 6 mL/kg predicted body weight, PBW) (N = 50) or intermediate VT (> 8 mL/kg PBW) (N = 48). The primary endpoint was the development of ARDS during the first 7 days after the initiation of invasive ventilation. Secondary endpoints included the development of pneumonia and severe atelectases; the length of intensive care unit (ICU) and hospital stay; and ICU, hospital, 28- and 90-day mortality. Results: In total, 98 patients [67.3% male], with a median age of 65.5 years [interquartile range 55-73], were enrolled until the study was prematurely stopped because of slow recruitment and loss of equipoise caused by recent study reports. On day 7, five (11.9%) patients in the low VT group and four (9.1%) patients in the intermediate VT group had developed ARDS (risk ratio, 1.16 [95% CI, 0.62-2.17]; p = 0.735). The incidence of pneumonia and severe atelectasis was also not different between the two groups. The use of a low VT strategy did neither affect the length of ICU and hospital stay nor mortality rates. Conclusions: In patients at risk for ARDS, a low VT strategy did not result in a lower incidence of ARDS than an intermediate VT strategy.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02070666.

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.

Early Tracheostomy for Managing ICU Capacity During the COVID-19 Outbreak: A Propensity-Matched Cohort Study.

BACKGROUND: During the first wave of the COVID-19 pandemic, shortages of ventilators and ICU beds overwhelmed health care systems. Whether early tracheostomy reduces the duration of mechanical ventilation and ICU stay is controversial. RESEARCH QUESTION: Can failure-free day outcomes focused on ICU resources help to decide the optimal timing of tracheostomy in overburdened health care systems during viral epidemics? STUDY DESIGN AND METHODS: This retrospective cohort study included consecutive patients with COVID-19 pneumonia who had undergone tracheostomy in 15 Spanish ICUs during the surge, when ICU occupancy modified clinician criteria to perform tracheostomy in Patients with COVID-19. We compared ventilator-free days at 28 and 60 days and ICU- and hospital bed-free days at 28 and 60 days in propensity score-matched cohorts who underwent tracheostomy at different timings (≤ 7 days, 8-10 days, and 11-14 days after intubation). RESULTS: Of 1,939 patients admitted with COVID-19 pneumonia, 682 (35.2%) underwent tracheostomy, 382 (56%) within 14 days. Earlier tracheostomy was associated with more ventilator-free days at 28 days (≤ 7 days vs > 7 days [116 patients included in the analysis]: median, 9 days [interquartile range (IQR), 0-15 days] vs 3 days [IQR, 0-7 days]; difference between groups, 4.5 days; 95% CI, 2.3-6.7 days; 8-10 days vs > 10 days [222 patients analyzed]: 6 days [IQR, 0-10 days] vs 0 days [IQR, 0-6 days]; difference, 3.1 days; 95% CI, 1.7-4.5 days; 11-14 days vs > 14 days [318 patients analyzed]: 4 days [IQR, 0-9 days] vs 0 days [IQR, 0-2 days]; difference, 3 days; 95% CI, 2.1-3.9 days). Except hospital bed-free days at 28 days, all other end points were better with early tracheostomy. INTERPRETATION: Optimal timing of tracheostomy may improve patient outcomes and may alleviate ICU capacity strain during the COVID-19 pandemic without increasing mortality. Tracheostomy within the first work on a ventilator in particular may improve ICU availability.

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.

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.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Integrated 3D printing solution to mitigate shortages of airway consumables and personal protective equipment during the COVID-19 pandemic.

BACKGROUND: To cope with shortages of equipment during the COVID-19 pandemic, we established a nonprofit end-to-end system to identify, validate, regulate, manufacture, and distribute 3D-printed medical equipment. Here we describe the local and global impact of this system. METHODS: Together with critical care experts, we identified potentially lacking medical equipment and proposed solutions based on 3D printing. Validation was based on the ISO 13485 quality standard for the manufacturing of customized medical devices. We posted the design files for each device on our website together with their technical and printing specifications and created a supply chain so that hospitals from our region could request them. We analyzed the number/type of items, petitioners, manufacturers, and catalogue views. RESULTS: Among 33 devices analyzed, 26 (78·8%) were validated. Of these, 23 (88·5%) were airway consumables and 3 (11·5%) were personal protective equipment. Orders came from 19 (76%) hospitals and 6 (24%) other healthcare institutions. Peak production was reached 10 days after the catalogue was published. A total of 22,135 items were manufactured by 59 companies in 18 sectors; 19,212 items were distributed to requesting sites during the busiest days of the pandemic. Our online catalogue was also viewed by 27,861 individuals from 113 countries. CONCLUSIONS: 3D printing helped mitigate shortages of medical devices due to problems in the global supply chain.

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.

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.

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.

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.

Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities.

BACKGROUND: Mechanical ventilation is common in critically ill patients. This life-saving treatment can cause complications and is also associated with long-term sequelae. Patient-ventilator asynchronies are frequent but underdiagnosed, and they have been associated with worse outcomes. MAIN BODY: Asynchronies occur when ventilator assistance does not match the patient's demand. Ventilatory overassistance or underassistance translates to different types of asynchronies with different effects on patients. Underassistance can result in an excessive load on respiratory muscles, air hunger, or lung injury due to excessive tidal volumes. Overassistance can result in lower patient inspiratory drive and can lead to reverse triggering, which can also worsen lung injury. Identifying the type of asynchrony and its causes is crucial for effective treatment. Mechanical ventilation and asynchronies can affect hemodynamics. An increase in intrathoracic pressure during ventilation modifies ventricular preload and afterload of ventricles, thereby affecting cardiac output and hemodynamic status. Ineffective efforts can decrease intrathoracic pressure, but double cycling can increase it. Thus, asynchronies can lower the predictive accuracy of some hemodynamic parameters of fluid responsiveness. New research is also exploring the psychological effects of asynchronies. Anxiety and depression are common in survivors of critical illness long after discharge. Patients on mechanical ventilation feel anxiety, fear, agony, and insecurity, which can worsen in the presence of asynchronies. Asynchronies have been associated with worse overall prognosis, but the direct causal relation between poor patient-ventilator interaction and worse outcomes has yet to be clearly demonstrated. Critical care patients generate huge volumes of data that are vastly underexploited. New monitoring systems can analyze waveforms together with other inputs, helping us to detect, analyze, and even predict asynchronies. Big data approaches promise to help us understand asynchronies better and improve their diagnosis and management. CONCLUSIONS: Although our understanding of asynchronies has increased in recent years, many questions remain to be answered. Evolving concepts in asynchronies, lung crosstalk with other organs, and the difficulties of data management make more efforts necessary in this field.

Trends in the incidence and mortality of patients with community-acquired septic shock 2003-2016.

PURPOSE: To evaluate the incidence and mortality of adult patients with community-acquired septic shock (CASS) and the influence of source control (SC) and other risk factors on the outcome. MATERIAL AND METHODS: The study included patients with CASS admitted to the ICU at a university hospital (2003-2016). Multivariate analyses were performed to identify risk factors of ICU mortality. RESULTS: A total of 625 patients were included. The incidence showed an average annual increase of 4.9% and the mortality an average annual decrease of 1.4%. The patients who required SC showed a lower mortality (20.4%) than patients who did not require SC (31.3%) (p = 0.002). However, the evolution in mortality was different: Mortality decreased in patients who did not require SC (from 56.3% to 20%; p = 0.02), but did not differ in those who required SC (from 21.4% to 27.6%; p = 0.43). In the multivariate analysis, severity at admission, age, alcoholism, cirrhosis, ARDS, neutropenia and thrombocytopenia were associated with worse outcome, whereas appropriate antibiotic treatment and adequate SC were independently associated with better survival. CONCLUSIONS: The incidence of CASS increased and the ICU mortality decreased during the study period. The mortality was mainly due to a decrease in mortality in infections not requiring SC.

Outcomes of Patients Presenting with Mild Acute Respiratory Distress Syndrome: Insights from the LUNG SAFE Study.

BACKGROUND: Patients with initial mild acute respiratory distress syndrome are often underrecognized and mistakenly considered to have low disease severity and favorable outcomes. They represent a relatively poorly characterized population that was only classified as having acute respiratory distress syndrome in the most recent definition. Our primary objective was to describe the natural course and the factors associated with worsening and mortality in this population. METHODS: This study analyzed patients from the international prospective Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE) who had initial mild acute respiratory distress syndrome in the first day of inclusion. This study defined three groups based on the evolution of severity in the first week: "worsening" if moderate or severe acute respiratory distress syndrome criteria were met, "persisting" if mild acute respiratory distress syndrome criteria were the most severe category, and "improving" if patients did not fulfill acute respiratory distress syndrome criteria any more from day 2. RESULTS: Among 580 patients with initial mild acute respiratory distress syndrome, 18% (103 of 580) continuously improved, 36% (210 of 580) had persisting mild acute respiratory distress syndrome, and 46% (267 of 580) worsened in the first week after acute respiratory distress syndrome onset. Global in-hospital mortality was 30% (172 of 576; specifically 10% [10 of 101], 30% [63 of 210], and 37% [99 of 265] for patients with improving, persisting, and worsening acute respiratory distress syndrome, respectively), and the median (interquartile range) duration of mechanical ventilation was 7 (4, 14) days (specifically 3 [2, 5], 7 [4, 14], and 11 [6, 18] days for patients with improving, persisting, and worsening acute respiratory distress syndrome, respectively). Admissions for trauma or pneumonia, higher nonpulmonary sequential organ failure assessment score, lower partial pressure of alveolar oxygen/fraction of inspired oxygen, and higher peak inspiratory pressure were independently associated with worsening. CONCLUSIONS: Most patients with initial mild acute respiratory distress syndrome continue to fulfill acute respiratory distress syndrome criteria in the first week, and nearly half worsen in severity. Their mortality is high, particularly in patients with worsening acute respiratory distress syndrome, emphasizing the need for close attention to this patient population.

Predicting Patient-ventilator Asynchronies with Hidden Markov Models.

In mechanical ventilation, it is paramount to ensure the patient's ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) - z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction.