Explore the vast range of titles available.

Purpose To improve the outcome of the acute respiratory distress syndrome (ARDS), one needs to identify potentially modifiable factors associated with mortality. Methods The large observational study to understand the global impact of severe acute respiratory failure (LUNG SAFE) was an international, multicenter, prospective cohort study of patients with severe respiratory failure, conducted in the winter of 2014 in a convenience sample of 459 ICUs from 50 countries across five continents. A pre-specified secondary aim was to examine the factors associated with outcome. Analyses were restricted to patients (93.1 %) fulfilling ARDS criteria on day 1–2 who received invasive mechanical ventilation. Results 2377 patients were included in the analysis. Potentially modifiable factors associated with increased hospital mortality in multivariable analyses include lower PEEP, higher peak inspiratory, plateau, and driving pressures, and increased respiratory rate. The impact of tidal volume on outcome was unclear. Having fewer ICU beds was also associated with higher hospital mortality. Non-modifiable factors associated with worsened outcome from ARDS included older age, active neoplasm, hematologic neoplasm, and chronic liver failure. Severity of illness indices including lower pH, lower PaO 2 /FiO 2 ratio, and higher non-pulmonary SOFA score were associated with poorer outcome. Of the 578 (24.3 %) patients with a limitation of life-sustaining therapies or measures decision, 498 (86.0 %) died in hospital. Factors associated with increased likelihood of limitation of life-sustaining therapies or measures decision included older age, immunosuppression, neoplasia, lower pH and increased non-pulmonary SOFA scores. Conclusions Higher PEEP, lower peak, plateau, and driving pressures, and lower respiratory rate are associated with improved survival from ARDS. Trial Registration: ClinicalTrials.gov NCT02010073.

Introduction: Metabolic acidosis is a frequent pathophysiological condition in critically ill patients. It can be assessed using different physiological variables, but their prognostic value has not yet been well established. Objective:To evaluate the association between the variables that allow assessing the metabolic component of acid-base balance (ABB) and 28-day mortality in patients admitted to an intensive care unit (ICU) in Bogotá, D.C., Colombia. Materials and methods: Prospective cohort study conducted in 122 patients admitted to an ICU between January and June 2013 and with a stay >24 hours. On admission to the ICU, blood samples were taken, and an arterial blood gas test was performed in order to calculate the following variables: anion gap (AG), corrected anion gap (AGc), standard base excess (BEst), metabolic H+, base excess-unmeasurable anions (BEua), arterial pH, arterial lactate, standard HCO3-st, and strong ion difference (SID). APACHE II and SOFA scores were also calculated. A bivariate analysis was performed in which ORs and their respective 95%CI were calculated, and then a multivariate analysis was conducted using a logistic regression model to identify the variables associated with 28-day mortality; a significance level of p<0.05 was considered. Results: Out of the 122 patients, 33 (27.05%) died at 28 days and 51 (48.80%) were women. Participants’ mean age was 46.5 years (±15.7). The following variables were significantly associated with 28-day mortality in the bivariate analysis: SID (OR=1.150; p=0.008), BEua (OR=0.897; p=0.023), AG (OR=1.231; p=0.002), AGc (OR=1.232; p=0.003), blood pH (OR=0.001; p=0.023), APACHE II (OR=1.180; p=0.001), HCO3-st (OR=0.841; p=0.015). In the multivariate analysis, only the APACHE II score variable was significantly associated with 28-day mortality (OR=1.188; p=0.008). Conclusion: The physiological variables that allow assessing the metabolic component of ABB, both from the Henderson model and the Stewart model, were not significantly associated with 28-day mortality.

The burden of traumatic brain injury (TBI) poses a large public health and societal problem, but the characteristics of patients and their care pathways in Europe are poorly understood. We aimed to characterise patient case-mix, care pathways, and outcomes of TBI. CENTER-TBI is a Europe-based, observational cohort study, consisting of a core study and a registry. Inclusion criteria for the core study were a clinical diagnosis of TBI, presentation fewer than 24 h after injury, and an indication for CT. Patients were differentiated by care pathway and assigned to the emergency room (ER) stratum (patients who were discharged from an emergency room), admission stratum (patients who were admitted to a hospital ward), or intensive care unit (ICU) stratum (patients who were admitted to the ICU). Neuroimages and biospecimens were stored in repositories and outcome was assessed at 6 months after injury. We used the IMPACT core model for estimating the expected mortality and proportion with unfavourable Glasgow Outcome Scale Extended (GOSE) outcomes in patients with moderate or severe TBI (Glasgow Coma Scale [GCS] score ≤12). The core study was registered with ClinicalTrials.gov, number NCT02210221, and with Resource Identification Portal (RRID: SCR_015582). Data from 4509 patients from 18 countries, collected between Dec 9, 2014, and Dec 17, 2017, were analysed in the core study and from 22 782 patients in the registry. In the core study, 848 (19%) patients were in the ER stratum, 1523 (34%) in the admission stratum, and 2138 (47%) in the ICU stratum. In the ICU stratum, 720 (36%) patients had mild TBI (GCS score 13–15). Compared with the core cohort, the registry had a higher proportion of patients in the ER (9839 [43%]) and admission (8571 [38%]) strata, with more than 95% of patients classified as having mild TBI. Patients in the core study were older than those in previous studies (median age 50 years [IQR 30–66], 1254 [28%] aged >65 years), 462 (11%) had serious comorbidities, 772 (18%) were taking anticoagulant or antiplatelet medication, and alcohol was contributory in 1054 (25%) TBIs. MRI and blood biomarker measurement enhanced characterisation of injury severity and type. Substantial inter-country differences existed in care pathways and practice. Incomplete recovery at 6 months (GOSE <8) was found in 207 (30%) patients in the ER stratum, 665 (53%) in the admission stratum, and 1547 (84%) in the ICU stratum. Among patients with moderate-to-severe TBI in the ICU stratum, 623 (55%) patients had unfavourable outcome at 6 months (GOSE <5), similar to the proportion predicted by the IMPACT prognostic model (observed to expected ratio 1·06 [95% CI 0·97–1·14]), but mortality was lower than expected (0·70 [0·62–0·76]). Patients with TBI who presented to European centres in the core study were older than were those in previous observational studies and often had comorbidities. Overall, most patients presented with mild TBI. The incomplete recovery of many patients should motivate precision medicine research and the identification of best practices to improve these outcomes. European Union 7th Framework Programme, the Hannelore Kohl Stiftung, OneMind, and Integra LifeSciences Corporation.

PurposeTo assess the independent association between ICU-acquired neuromuscular complications and 5-year mortality and morbidity. To explore the optimal threshold of the Medical Research Council (MRC) sum score, assessing weakness, for the prediction of 5-year outcomes.MethodsSub-analyses of a prospective, 5-year follow-up study including 883 EPaNIC patients (Early versus Late Parenteral Nutrition in Intensive Care) (Clinicaltrials.gov:NCT00512122), systematically screened in ICU for neuromuscular complications with MRC sum score (‘MRC-cohort’, N = 600), electrophysiology on day 8 ± 1 to quantify compound muscle action potential (‘CMAP-cohort’, N = 689), or both (‘MRC&CMAP-cohort’, N = 415). Associations between ICU-acquired neuromuscular complications and 5-year mortality, hand-grip strength (HGF, %predicted), 6-min-walk distance (6-MWD, %predicted) and physical function of the SF-36 quality-of-life questionnaire (PF-SF-36) at 5-years were assessed with Cox regression and linear regression, adjusted for confounders. The optimal threshold for MRC at ICU discharge to predict 5-year outcomes was determined by martingale residual plots (survival) and scatterplots (morbidity).ResultsBoth lower MRC sum score at ICU discharge, indicating less strength [HR, per-point-increase: 0.946 (95% CI 0.928–0.968), p = 0.001], and abnormal CMAP, indicating nerve/muscle dysfunction [HR: 1.568 (95% CI 1.165–2.186), p = 0.004], independently associated with increased 5-year mortality. In the MRC&CMAP-cohort, MRC [HR: 0.956 (95% CI 0.934–0.980), p = 0.001] but not CMAP [HR: 1.478 (95% CI 0.875–2.838), p = 0.088] independently associated with 5-year mortality. Among 205 survivors, low MRC independently associated with low HGF [0.866 (95% CI 0.237–1.527), p = 0.004], low 6-MWD [105.1 (95% CI 12.1–212.9), p = 0.043] and low PF-SF-36 [− 0.119 (95% CI − 0.186 to − 0.057), p = 0.002], whereas abnormal CMAP did not correlate with these morbidity endpoints. Exploratory analyses suggested that MRC ≤ 55 best predicted poor long-term morbidity and mortality. Both MRC ≤ 55 and abnormal CMAP independently associated with 5-year mortality.ConclusionsICU-acquired neuromuscular complications may impact 5-year morbidity and mortality. MRC sum score, even if slightly reduced, may affect long-term mortality, strength, functional capacity and physical function, whereas abnormal CMAP only related to long-term mortality.

Background Acute kidney injury (AKI) is a common complication of critical illness and is associated with worse outcomes. However, the influence of deterioration or improvement in renal function on clinical outcomes is unclear. Using a large international database, we evaluated the prevalence and evolution of AKI over a 7-day period and its effects on clinical outcomes in septic and non-septic critically ill patients worldwide. Methods From the 10,069 adult intensive care unit (ICU) patients in the Intensive Care Over Nations database, all those with creatinine and urine output data were included in this substudy. Patients who developed sepsis during the ICU stay (≥ 2 days after admission) were excluded. AKI was evaluated within 72 hours after admission and before discharge/death up to day 7 according to the Acute Kidney Injury Network (AKIN) criteria. Results A total of 7970 patients were included, 59% of whom met AKIN criteria for AKI within the first 72 hours of the ICU stay. Twenty-four per cent of patients had sepsis on admission, of whom 68% had AKI, compared to 57% of those without sepsis on admission ( p < 0.001). AKIN stage 3 (40% vs 24%, p < 0.001) and use of renal replacement therapy (20% vs 5%, p < 0.0001) were more prevalent in patients with sepsis. Patients with sepsis and AKIN stage 3 were less likely to improve to a lower stage during the 7-day follow-up period than non-septic patients with AKIN stage 3 (21% vs 32%, p < 0.0001). In-hospital mortality was related to severity of AKI and was reduced in patients in whom AKI improved compared to those who remained stable or deteriorated, but remained higher than in patients without AKI, even if there was apparent full recovery at day 7. Conclusion These findings illustrate the different kinetics of AKI in septic and non-septic ICU patients and emphasize the important impact of AKI on mortality rates even when there is apparent full renal recovery at day 7 .

Prior studies have demonstrated that the cardiac intensive care unit (CICU) patient population has evolved over time. We sought to describe the temporal changes in comorbidities, illness severity, diagnoses, procedures and adjusted mortality within our CICU practice in recent years. We retrospectively reviewed unique CICU admissions at the Mayo Clinic from January 2007 to April 2018. Comorbidities, severity of illness scores, discharge diagnosis codes and CICU procedures and therapies were recorded, and temporal trends were assessed using linear regression and Cochran-Armitage trend tests. Trends in adjusted hospital mortality over time were assessed using multivariable logistic regression. We included 12,418 patients with a mean age of 67.6 years (including 37.7% females). Temporal trends in the prevalence of several comorbidities and discharge diagnoses were observed, reflecting an increase in the prevalence of non-coronary cardiovascular diseases, critical care diagnoses, and organ failure (all P ≪ .05). The use of several CICU therapies and procedures increased over time, including mechanical ventilation, invasive lines and vasoactive drugs (all P ≪ .05). A temporal decrease in adjusted hospital mortality was observed among the subgroup of patients with (adjusted OR per year 0.97, 95% CI 0.94–0.99, P = .023) and without (adjusted OR per year 0.91, 95% CI 0.85–0.96, P = .002) a critical care discharge diagnosis. We observed an increasing prevalence of critical care and organ failure diagnoses as well as increased utilization of critical care therapies in this CICU cohort, associated with a decrease in risk-adjusted hospital mortality over time.

Critical illness can be lethal and devastating to survivors. Improvements in acute care have increased the number of intensive care unit (ICU) survivors. These survivors confront a range of new or worsened health states that collectively are commonly denominated post-intensive care syndrome (PICS). These problems include physical, cognitive, psychological, and existential aspects, among others. Burgeoning interest in improving long-term outcomes for ICU survivors has driven an array of potential interventions to improve outcomes associated with PICS. To date, the most promising interventions appear to relate to very early physical rehabilitation. Late interventions within aftercare and recovery clinics have yielded mixed results, although experience in heart failure programs suggests the possibility that very early case management interventions may help improve intermediate-term outcomes, including mortality and hospital readmission. Predictive models have tended to underperform, complicating study design and clinical referral. The complexity of the health states associated with PICS suggests that careful and rigorous evaluation of multidisciplinary, multimodality interventions-tied to the specific conditions of interest-will be required to address these important problems.

Background The use of composite outcome measures (COM) in clinical trials is increasing. Whilst their use is associated with benefits, several limitations have been highlighted and there is limited literature exploring their use within critical care. The primary aim of this study was to evaluate the use of COM in high-impact critical care trials, and compare study parameters (including sample size, statistical significance, and consistency of effect estimates) in trials using composite versus non-composite outcomes. Methods A systematic review of 16 high-impact journals was conducted. Randomised controlled trials published between 2012 and 2022 reporting a patient important outcome and involving critical care patients, were included. Results 8271 trials were screened, and 194 included. 39.1% of all trials used a COM and this increased over time. Of those using a COM, only 52.6% explicitly described the outcome as composite. The median number of components was 2 (IQR 2–3). Trials using a COM recruited fewer participants (409 (198.8–851.5) vs 584 (300–1566, p  = 0.004), and their use was not associated with increased rates of statistical significance (19.7% vs 17.8%, p  = 0.380). Predicted effect sizes were overestimated in all but 6 trials. For studies using a COM the effect estimates were consistent across all components in 43.4% of trials. 93% of COM included components that were not patient important. Conclusions COM are increasingly used in critical care trials; however effect estimates are frequently inconsistent across COM components confounding outcome interpretations. The use of COM was associated with smaller sample sizes, and no increased likelihood of statistically significant results. Many of the limitations inherent to the use of COM are relevant to critical care research.

While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage. To examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches. Prognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018. The outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning-based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models' prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information. Of 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds. Machine learning-based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.

Immunosuppressed patients, particularly those with cancer, represent a momentous and increasing portion of the population, especially as cancer incidence rises with population growth and aging. These patients are at a heightened risk of developing severe infections, including sepsis and septic shock, due to multiple immunologic defects such as neutropenia, lymphopenia, and T and B-cell impairment. The diverse and complex nature of these immunologic profiles, compounded by the concomitant use of immunosuppressive therapies (e.g., corticosteroids, cytotoxic drugs, and immunotherapy), superimposed by the breakage of natural protective barriers (e.g., mucosal damage, chronic indwelling catheters, and alterations of anatomical structures), increases the risk of various infections. These and other conditions that mimic sepsis pose substantial diagnostic and therapeutic challenges. Factors that elevate the risk of progression to septic shock in these patients include advanced age, pre-existing comorbidities, frailty, type of cancer, the severity of immunosuppression, hypoalbuminemia, hypophosphatemia, Gram-negative bacteremia, and type and timing of responses to initial treatment. The management of vulnerable cancer patients with sepsis or septic shock varies due to biased clinical practices that may result in delayed access to intensive care and worse outcomes. While septic shock is typically associated with poor outcomes in patients with malignancies, survival has significantly improved over time. Therefore, understanding and addressing the unique needs of cancer patients through a new paradigm, which includes the integration of innovative technologies into our healthcare system (e.g., wireless technologies, medical informatics, precision medicine), targeted management strategies, and robust clinical practices, including early identification and diagnosis, coupled with prompt admission to high-level care facilities that promote a multidisciplinary approach, is crucial for improving their prognosis and overall survival rates. Graphical abstract