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Immobilisation predicts adverse outcomes in patients in the surgical intensive care unit (SICU). Attempts to mobilise critically ill patients early after surgery are frequently restricted, but we tested whether early mobilisation leads to improved mobility, decreased SICU length of stay, and increased functional independence of patients at hospital discharge. We did a multicentre, international, parallel-group, assessor-blinded, randomised controlled trial in SICUs of five university hospitals in Austria (n=1), Germany (n=1), and the USA (n=3). Eligible patients (aged 18 years or older, who had been mechanically ventilated for <48 h, and were expected to require mechanical ventilation for ≥24 h) were randomly assigned (1:1) by use of a stratified block randomisation via restricted web platform to standard of care (control) or early, goal-directed mobilisation using an inter-professional approach of closed-loop communication and the SICU optimal mobilisation score (SOMS) algorithm (intervention), which describes patients’ mobilisation capacity on a numerical rating scale ranging from 0 (no mobilisation) to 4 (ambulation). We had three main outcomes hierarchically tested in a prespecified order: the mean SOMS level patients achieved during their SICU stay (primary outcome), and patient's length of stay on SICU and the mini-modified functional independence measure score (mmFIM) at hospital discharge (both secondary outcomes). This trial is registered with ClinicalTrials.gov (NCT01363102). Between July 1, 2011, and Nov 4, 2015, we randomly assigned 200 patients to receive standard treatment (control; n=96) or intervention (n=104). Intention-to-treat analysis showed that the intervention improved the mobilisation level (mean achieved SOMS 2·2 [SD 1·0] in intervention group vs 1·5 [0·8] in control group, p<0·0001), decreased SICU length of stay (mean 7 days [SD 5–12] in intervention group vs 10 days [6–15] in control group, p=0·0054), and improved functional mobility at hospital discharge (mmFIM score 8 [4–8] in intervention group vs 5 [2–8] in control group, p=0·0002). More adverse events were reported in the intervention group (25 cases [2·8%]) than in the control group (ten cases [0·8%]); no serious adverse events were observed. Before hospital discharge 25 patients died (17 [16%] in the intervention group, eight [8%] in the control group). 3 months after hospital discharge 36 patients died (21 [22%] in the intervention group, 15 [17%] in the control group). Early, goal-directed mobilisation improved patient mobilisation throughout SICU admission, shortened patient length of stay in the SICU, and improved patients’ functional mobility at hospital discharge. Jeffrey and Judy Buzen.

Background Gastrointestinal (GI) dysfunction is frequent in the critically ill but can be overlooked as a result of the lack of standardization of the diagnostic and therapeutic approaches. We aimed to develop a research agenda for GI dysfunction for future research. We systematically reviewed the current knowledge on a broad range of subtopics from a specific viewpoint of GI dysfunction, highlighting the remaining areas of uncertainty and suggesting future studies. Methods This systematic scoping review and research agenda was conducted following successive steps: (1) identify clinically important subtopics within the field of GI function which warrant further research; (2) systematically review the literature for each subtopic using PubMed, CENTRAL and Cochrane Database of Systematic Reviews; (3) summarize evidence for each subtopic; (4) identify areas of uncertainty; (5) formulate and refine study proposals that address these subtopics; and (6) prioritize study proposals via sequential voting rounds. Results Five major themes were identified: (1) monitoring, (2) associations between GI function and outcome, (3) GI function and nutrition, (4) management of GI dysfunction and (5) pathophysiological mechanisms. Searches on 17 subtopics were performed and evidence summarized. Several areas of uncertainty were identified, six of them needing consensus process. Study proposals ranked among the first ten included: prevention and management of diarrhoea; management of upper and lower feeding intolerance, including indications for post-pyloric feeding and opioid antagonists; acute gastrointestinal injury grading as a bedside tool; the role of intra-abdominal hypertension in the development and monitoring of GI dysfunction and in the development of non-occlusive mesenteric ischaemia; and the effect of proton pump inhibitors on the microbiome in critical illness. Conclusions Current evidence on GI dysfunction is scarce, partially due to the lack of precise definitions. The use of core sets of monitoring and outcomes are required to improve the consistency of future studies. We propose several areas for consensus process and outline future study projects.

Critical care ultrasound (CCUS) is an essential component of intensive care practice. Although existing international guidelines have focused on training principles and determining competency in CCUS, few countries have managed to operationalize this guidance into an accessible, well-structured programme for clinicians training in multidisciplinary intensive care. We seek to update and reaffirm appropriate CCUS scope so that it may be integrated into the international Competency-based Training in Intensive Care Medicine. The resulting recommendations offer the most contemporary and evolved set of core CCUS competencies for an intensive care clinician yet described. Importantly, we discuss the rationale for inclusion but also exclusion of competencies listed. Background/aim Critical care ultrasound (CCUS) is an essential component of intensive care practice. The purpose of this consensus document is to determine those CCUS competencies that should be a mandatory part of training in multidisciplinary intensive care. Methods A three-round Delphi method followed by face-to-face meeting among 32 CCUS experts nominated by the European Society of Intensive Care Medicine. Agreement of at least 90% of experts was needed in order to enlist a competency as mandatory. Results The final list of competencies includes 15 echocardiographic, 5 thoracic, 4 abdominal, deep vein thrombosis diagnosis and central venous access aid. Conclusion The resulting recommendations offer the most contemporary and evolved set of core CCUS competencies for an intensive care clinician yet described.

Background The lack of a patent source of infection after 24 hours of management of shock considered septic is a common and disturbing scenario. We aimed to determine the prevalence and the causes of shock with no diagnosis 24 hours after its onset, and to compare the outcomes of patients with early-confirmed septic shock to those of others. Methods We conducted a pragmatic, prospective, multicenter observational cohort study in ten intensive care units (ICU) in France. We included all consecutive patients admitted to the ICU with suspected septic shock defined by clinical suspicion of infection leading to antibiotic prescription plus acute circulatory failure requiring vasopressor support. Results A total of 508 patients were admitted with suspected septic shock. Among them, 374 (74 %) had early-confirmed septic shock, while the 134 others (26 %) had no source of infection identified nor microbiological documentation retrieved 24 hours after shock onset. Among these, 37/134 (28 %) had late-confirmed septic shock diagnosed after 24 hours, 59/134 (44 %) had a condition mimicking septic (septic shock mimicker, mainly related to adverse drug reactions, acute mesenteric ischemia and malignancies) and 38/134 (28 %) had shock of unknown origin by the end of the ICU stay. There were no differences between patients with early-confirmed septic shock and the remainder in ICU mortality and the median duration of ICU stay, of tracheal intubation and of vasopressor support. The multivariable Cox model showed that the risk of day-60 mortality did not differ between patients with or without early-confirmed septic shock. A sensitivity analysis was performed in the subgroup ( n  = 369/508) of patients meeting the Sepsis-3 definition criteria and displayed consistent results. Conclusions One quarter of the patients admitted in the ICU with suspected septic shock had no infection identified 24 hours after its onset and almost half of them were eventually diagnosed with a septic shock mimicker. Outcome did not differ between patients with early-confirmed septic shock and other patients.

Acute liver failure is a rare but potentially devastating disease. Throughout the last few decades, acute liver failure outcomes have been improving in the context of the optimized overall management. This positive trend has been associated with the earlier recognition of this condition, the improvement of the intensive care unit management, and the developments in emergent liver transplantation. Accordingly, we aimed to review the current diagnostic and therapeutic approach to this syndrome, especially in the intensive care unit setting. •Outcomes of patients with acute liver failure have been improving, but morbidity and mortality are still of concern.•Earlier recognition and referral to the intensive care unit have been important to improve these patients' outcomes.•Intensive care management should take into consideration hepatic encephalopathy, cerebral edema and intracranial hypertension, hemodynamics, acute kidney injury, coagulopathy, and infection.•Selection of patients for liver transplantation has been based on prognostic scores (eg, Kings' College criteria) but limitations may apply.

Introduction This narrative review focusing on critical care echocardiography (CCE) has been written by a group of experts in the field, with the aim of outlining the state of the art in CCE in the 10 years after its official recognition and definition. Results In the last 10 years, CCE has become an essential branch of critical care ultrasonography and has gained general acceptance. Its use, both as a diagnostic tool and for hemodynamic monitoring, has increased markedly, influencing contemporary cardiorespiratory management. Recent studies suggest that the use of CCE may have a positive impact on outcomes. CCE may be used in critically ill patients in many different clinical situations, both in their early evaluation of in the emergency department and during intensive care unit (ICU) admission and stay. CCE has also proven its utility in perioperative settings, as well as in the management of mechanical circulatory support. CCE may be performed with very simple diagnostic objectives. This application, referred to as basic CCE, does not require a high level of training. Advanced CCE, on the other hand, uses ultrasonography for full evaluation of cardiac function and hemodynamics, and requires extensive training, with formal certification now available. Indeed, recent years have seen the creation of worldwide certification in advanced CCE. While transthoracic CCE remains the most commonly used method, the transesophageal route has gained importance, particularly for intubated and ventilated patients. Conclusion CCE is now widely accepted by the critical care community as a valuable tool in the ICU and emergency department, and in perioperative settings.

Background and objectives Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volume-responsive (VR) and volume-unresponsive (VU) AKI. Methods AKI patients with urine output < 0.5 ml/kg/h for the first 6 h after ICU admission and fluid intake > 5 l in the following 6 h in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-III)) were considered. Patients who received diuretics and renal replacement on day 1 were excluded. Two predictive models, using either machine learning extreme gradient boosting (XGBoost) or logistic regression, were developed to predict urine output > 0.65 ml/kg/h during 18 h succeeding the initial 6 h for assessing oliguria. Established models were assessed by using out-of-sample validation. The whole sample was split into training and testing samples by the ratio of 3:1. Main results Of the 6682 patients included in the analysis, 2456 (36.8%) patients were volume responsive with an increase in urine output after receiving > 5 l fluid. Urinary creatinine, blood urea nitrogen (BUN), age, and albumin were the important predictors of VR. The machine learning XGBoost model outperformed the traditional logistic regression model in differentiating between the VR and VU groups (AU-ROC, 0.860; 95% CI, 0.842 to 0.878 vs. 0.728; 95% CI 0.703 to 0.753, respectively). Conclusions The XGBoost model was able to differentiate between patients who would and would not respond to fluid intake in urine output better than a traditional logistic regression model. This result suggests that machine learning techniques have the potential to improve the development and validation of predictive modeling in critical care research.

Coronavirus disease 19 (COVID-19) has posed unprecedented healthcare system challenges, some of which will lead to transformative change. It is obvious to healthcare workers and policymakers alike that an effective critical care surge response must be nested within the overall care delivery model. The COVID-19 pandemic has highlighted key elements of emergency preparedness. These include having national or regional strategic reserves of personal protective equipment, intensive care unit (ICU) devices, consumables and pharmaceuticals, as well as effective supply chains and efficient utilization protocols. ICUs must also be prepared to accommodate surges of patients and ICU staffing models should allow for fluctuations in demand. Pre-existing ICU triage and end-of-life care principles should be established, implemented and updated. Daily workflow processes should be restructured to include remote connection with multidisciplinary healthcare workers and frequent communication with relatives. The pandemic has also demonstrated the benefits of digital transformation and the value of remote monitoring technologies, such as wireless monitoring. Finally, the pandemic has highlighted the value of pre-existing epidemiological registries and agile randomized controlled platform trials in generating fast, reliable data. The COVID-19 pandemic is a reminder that besides our duty to care, we are committed to improve. By meeting these challenges today, we will be able to provide better care to future patients.

BackgroundAntimicrobial de-escalation (ADE) is a strategy of antimicrobial stewardship, aiming at preventing the emergence of antimicrobial resistance (AMR) by decreasing the exposure to broad-spectrum antimicrobials. There is no high-quality research on ADE and its effects on AMR. Its definition varies and there is little evidence-based guidance for clinicians to use ADE in the intensive care unit (ICU).MethodsA task force of 16 international experts was formed in November 2016 to provide with guidelines for clinical practice to develop questions targeted at defining ADE, its effects on the ICU population and to provide clinical guidance. Groups of 2 experts were assigned 1–2 questions each within their field of expertise to provide draft statements and rationale. A Delphi method, with 3 rounds and an agreement threshold of 70% was required to reach consensus.ResultsWe present a comprehensive document with 13 statements, reviewing the evidence on the definition of ADE, its effects in the ICU population and providing guidance for clinicians in subsets of clinical scenarios where ADE may be considered.ConclusionADE remains a topic of controversy due to the complexity of clinical scenarios where it may be applied and the absence of evidence to the effects it may have on antimicrobial resistance.

Artificial Intelligence (AI) is rapidly transforming the landscape of critical care, offering opportunities for enhanced diagnostic precision and personalized patient management. However, its integration into ICU clinical practice presents significant challenges related to equity, transparency, and the patient-clinician relationship. To address these concerns, a multidisciplinary team of experts was established to assess the current state and future trajectory of AI in critical care. This consensus identified key challenges and proposed actionable recommendations to guide AI implementation in this high-stakes field. Here we present a call to action for the critical care community, to bridge the gap between AI advancements and the need for humanized, patient-centred care. Our goal is to ensure a smooth transition to personalized medicine while, (1) maintaining equitable and unbiased decision-making, (2) fostering the development of a collaborative research network across ICUs, emergency departments, and operating rooms to promote data sharing and harmonization, and (3) addressing the necessary educational and regulatory shifts required for responsible AI deployment. AI integration into critical care demands coordinated efforts among clinicians, patients, industry leaders, and regulators to ensure patient safety and maximize societal benefit. The recommendations outlined here provide a foundation for the ethical and effective implementation of AI in critical care medicine.