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Intensive care survivors often experience post-intensive care sequelae, which are frequently gathered together under the term “post-intensive care syndrome” (PICS). The consequences of PICS on quality of life, health-related costs and hospital readmissions are real public health problems. In the present Viewpoint, we summarize current knowledge and gaps in our understanding of PICS and approaches to management.

Virtual reality (VR) and augmented reality (AR) are aspiring, new technologies with increasing use in critical care medicine. While VR fully immerses the user into a virtual three-dimensional space, AR adds overlaid virtual elements into a real-world environment. VR and AR offer great potential to improve critical care medicine for patients, relatives and health care providers. VR may help to ameliorate anxiety, stress, fear, and pain for the patient. It may assist patients in mobilisation and rehabilitation and can improve communication between all those involved in the patient’s care. AR can be an effective tool to support continuous education of intensive care medicine providers, and may complement traditional learning methods to acquire key practical competences such as central venous line placement, cardiopulmonary resuscitation, extracorporeal membrane oxygenation device management or endotracheal intubation. Currently, technical, human, and ethical challenges remain. The adaptation and integration of VR/AR modalities into useful clinical applications that can be used routinely on the ICU is challenging. Users may experience unwanted side effects (so-called “cybersickness”) during VR/AR sessions, which may limit its applicability. Furthermore, critically ill patients are one of the most vulnerable patient groups and warrant special ethical considerations if new technologies are to be introduced into their daily care. To date, most studies involving AR/VR in critical care medicine provide only a low level of evidence due to their research design. Here we summarise background information, current developments, and key considerations that should be taken into account for future scientific investigations in this field. Graphical abstract

Background  Circulatory failure is classified into four types of shock (obstructive, cardiogenic, distributive, and hypovolemic) that must be distinguished as each requires a different treatment. Point-of-care ultrasound (POCUS) is widely used in clinical practice for acute conditions, and several diagnostic protocols using POCUS for shock have been developed. This study aimed to evaluate the diagnostic accuracy of POCUS in identifying the etiology of shock. Methods We conducted a systematic literature search of MEDLINE, Cochrane Central Register of Controlled Trials, Embase, Web of Science, Clinicaltrial.gov, European Union Clinical Trials Register, WHO International Clinical Trials Registry Platform, and University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) until June 15, 2022. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and assessed study quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Meta-analysis was conducted to pool the diagnostic accuracy of POCUS for each type of shock. The study protocol was prospectively registered in UMIN-CTR (UMIN 000048025). Results Of the 1553 studies identified, 36 studies were full-text reviewed, and 12 studies with 1132 patients were included in the meta-analysis. Pooled sensitivity and specificity were 0.82 [95% confidence interval (CI) 0.68–0.91] and 0.98 [95% CI 0.92–0.99] for obstructive shock, 0.78 [95% CI 0.56–0.91] and 0.96 [95% CI 0.92–0.98] for cardiogenic shock, 0.90 [95% CI 0.84–0.94] and 0.92 [95% CI 0.88–0.95] for hypovolemic shock, and 0.79 [95% CI 0.71–0.85] and 0.96 [95% CI 0.91–0.98] for distributive shock, respectively. The area under the receiver operating characteristic curve for each type of shock was approximately 0.95. The positive likelihood ratios for each type of shock were all greater than 10, especially 40 [95% CI 11–105] for obstructive shock. The negative likelihood ratio for each type of shock was approximately 0.2. Conclusions  The identification of the etiology for each type of shock using POCUS was characterized by high sensitivity and positive likelihood ratios, especially for obstructive shock.

Background Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach—the Emergency Severity Index (ESI). Methods Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model. Results Of 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85–0.87] in the deep neural network vs 0.74 [95%CI 0.72–0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82–0.83] in the deep neural network vs 0.69 [95%CI 0.68–0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit—a larger number of appropriate triages considering a trade-off with over-triages—across the range of clinical thresholds. Conclusions Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians’ triage decision making, thereby achieving better clinical care and optimal resource utilization.

Background\\nTo better understand the epidemiology and patterns of tracheostomy practice for patients with acute respiratory distress syndrome (ARDS), we investigated the current usage of tracheostomy in patients with ARDS recruited into the Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG-SAFE) study.\\nMethods\\nThis is a secondary analysis of LUNG-SAFE, an international, multicenter, prospective cohort study of patients receiving invasive or noninvasive ventilation in 50 countries spanning 5 continents. The study was carried out over 4 weeks consecutively in the winter of 2014, and 459 ICUs participated. We evaluated the clinical characteristics, management and outcomes of patients that received tracheostomy, in the cohort of patients that developed ARDS on day 1–2 of acute hypoxemic respiratory failure, and in a subsequent propensity-matched cohort.\\nResults\\nOf the 2377 patients with ARDS that fulfilled the inclusion criteria, 309 (13.0%) underwent tracheostomy during their ICU stay. Patients from high-income European countries (n = 198/1263) more frequently underwent tracheostomy compared to patients from non-European high-income countries (n = 63/649) or patients from middle-income countries (n = 48/465). Only 86/309 (27.8%) underwent tracheostomy on or before day 7, while the median timing of tracheostomy was 14 (Q1–Q3, 7–21) days after onset of ARDS. In the subsample matched by propensity score, ICU and hospital stay were longer in patients with tracheostomy. While patients with tracheostomy had the highest survival probability, there was no difference in 60-day or 90-day mortality in either the patient subgroup that survived for at least 5 days in ICU, or in the propensity-matched subsample.\\nConclusions\\nMost patients that receive tracheostomy do so after the first week of critical illness. Tracheostomy may prolong patient survival but does not reduce 60-day or 90-day mortality.

Vasopressors and fluids are the cornerstones for the treatment of shock. The current international guidelines on shock recommend norepinephrine as the first-line vasopressor and vasopressin as the second-line vasopressor. In clinical practice, due to drug availability, local practice variations, special settings, and ongoing research, several alternative vasoconstrictors and adjuncts are used in the absence of precise equivalent doses. Norepinephrine equivalence (NEE) is frequently used in clinical trials to overcome this heterogeneity and describe vasopressor support in a standardized manner. NEE quantifies the total amount of vasopressors, considering the potency of each such agent, which typically includes catecholamines, derivatives, and vasopressin. Intensive care studies use NEE as an eligibility criterion and also an outcome measure. On the other hand, NEE has several pitfalls which clinicians should know, important the lack of conversion of novel vasopressors such as angiotensin II and also adjuncts such as methylene blue, including a lack of high-quality data to support the equation and validate its predictive performance in all types of critical care practice. This review describes the history of NEE and suggests an updated formula incorporating novel vasopressors and adjuncts.

Background Propofol is one of the most widely used hypnotic agents in the world. Nonetheless, propofol might have detrimental effects on clinically relevant outcomes, possibly due to inhibition of other interventions' organ protective properties. We performed a systematic review and meta-analysis of randomized controlled trials to evaluate if propofol reduced survival compared to any other hypnotic agent in any clinical setting. Methods We searched eligible studies in PubMed, Google Scholar, and the Cochrane Register of Clinical Trials. The following inclusion criteria were used: random treatment allocation and comparison between propofol and any comparator in any clinical setting. The primary outcome was mortality at the longest follow-up available. We conducted a fixed-effects meta-analysis for the risk ratio (RR). Using this RR and 95% confidence interval, we estimated the probability of any harm (RR > 1) through Bayesian statistics. We registered this systematic review and meta-analysis in PROSPERO International Prospective Register of Systematic Reviews (CRD42022323143). Results We identified 252 randomized trials comprising 30,757 patients. Mortality was higher in the propofol group than in the comparator group (760/14,754 [5.2%] vs. 682/16,003 [4.3%]; RR = 1.10; 95% confidence interval, 1.01–1.20; p = 0.03; I 2  = 0%; number needed to harm = 235), corresponding to a 98.4% probability of any increase in mortality. A statistically significant mortality increase in the propofol group was confirmed in subgroups of cardiac surgery, adult patients, volatile agent as comparator, large studies, and studies with low mortality in the comparator arm. Conclusions Propofol may reduce survival in perioperative and critically ill patients. This needs careful assessment of the risk versus benefit of propofol compared to other agents while planning for large, pragmatic multicentric randomized controlled trials to provide a definitive answer. Graphical Abstract

Sepsis is a potentially fatal condition caused by dysregulation of the body's immune response to an infection. Sepsis-induced liver injury is considered a strong independent prognosticator of death in the critical care unit, and there is anatomic and accumulating epidemiologic evidence that demonstrates intimate cross talk between the gut and the liver. Intestinal barrier disruption and gut microbiota dysbiosis during sepsis result in translocation of intestinal pathogen-associated molecular patterns and damage-associated molecular patterns into the liver and systemic circulation. The liver is essential for regulating immune defense during systemic infections via mechanisms such as bacterial clearance, lipopolysaccharide detoxification, cytokine and acute-phase protein release, and inflammation metabolic regulation. When an inappropriate immune response or overwhelming inflammation occurs in the liver, the impaired capacity for pathogen clearance and hepatic metabolic disturbance can result in further impairment of the intestinal barrier and increased disruption of the composition and diversity of the gut microbiota. Therefore, interaction between the gut and liver is a potential therapeutic target. This review outlines the intimate gut–liver cross talk (gut–liver axis) in sepsis.

Thrombotic complications and coagulopathy frequently occur in COVID-19. However, the characteristics of COVID-19-associated coagulopathy (CAC) are distinct from those seen with bacterial sepsis-induced coagulopathy (SIC) and disseminated intravascular coagulation (DIC), with CAC usually showing increased D-dimer and fibrinogen levels but initially minimal abnormalities in prothrombin time and platelet count. Venous thromboembolism and arterial thrombosis are more frequent in CAC compared to SIC/DIC. Clinical and laboratory features of CAC overlap somewhat with a hemophagocytic syndrome, antiphospholipid syndrome, and thrombotic microangiopathy. We summarize the key characteristics of representative coagulopathies, discussing similarities and differences so as to define the unique character of CAC.

Background Cardiac output (CO) monitoring is a valuable tool for the diagnosis and management of critically ill patients. In the critical care setting, few studies have evaluated the level of agreement between CO estimated by transthoracic echocardiography (CO-TTE) and that measured by the reference method, pulmonary artery catheter (CO-PAC). The objective of the present study was to evaluate the precision and accuracy of CO-TTE relative to CO-PAC and the ability of transthoracic echocardiography to track variations in CO, in critically ill mechanically ventilated patients. Methods Thirty-eight mechanically ventilated patients fitted with a PAC were included in a prospective observational study performed in a 16-bed university hospital ICU. CO-PAC was measured via intermittent thermodilution. Simultaneously, a second investigator used standard-view TTE to estimate CO-TTE as the product of stroke volume and the heart rate obtained during the measurement of the subaortic velocity time integral. Results Sixty-four pairs of CO-PAC and CO-TTE measurements were compared. The two measurements were significantly correlated ( r  = 0.95; p  < 0.0001). The median bias was 0.2 L/min, the limits of agreement (LOAs) were –1.3 and 1.8 L/min, and the percentage error was 25%. The precision was 8% for CO-PAC and 9% for CO-TTE. Twenty-six pairs of ΔCO measurements were compared. There was a significant correlation between ΔCO-PAC and ΔCO-TTE ( r  = 0.92; p  < 0.0001). The median bias was –0.1 L/min and the LOAs were –1.3 and +1.2 L/min. With a 15% exclusion zone, the four-quadrant plot had a concordance rate of 94%. With a 0.5 L/min exclusion zone, the polar plot had a mean polar angle of 1.0° and a percentage error LOAs of –26.8 to 28.8°. The concordance rate was 100% between 30 and –30°. When using CO-TTE to detect an increase in ΔCO-PAC of more than 10%, the area under the receiving operating characteristic curve (95% CI) was 0.82 (0.62–0.94) ( p  < 0.001). A ΔCO-TTE of more than 8% yielded a sensitivity of 88% and specificity of 66% for detecting a ΔCO-PAC of more than 10%. Conclusion In critically ill mechanically ventilated patients, CO-TTE is an accurate and precise method for estimating CO. Furthermore, CO-TTE can accurately track variations in CO.