Biological basis of critical illness subclasses: from the bedside to the bench and back again.

Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.

A Systematic Review and Pooled Prevalence of Delirium in Critically Ill Children.

OBJECTIVES: Pediatric delirium is a neuropsychiatric disorder with disrupted cerebral functioning due to underlying disease and/or critical care treatment. Pediatric delirium can be classified as hypoactive, hyperactive, and mixed. This systematic review was conducted to estimate the pooled prevalence of pediatric delirium using validated assessment tools in children (Cornell Assessment of Pediatric Delirium, Pediatric Confusion Assessment Method for the ICU, PreSchool Confusion Assessment Method for the ICU, Pediatric Confusion Assessment Method for the ICU Severity Scale, and Sophia Observation Withdrawal Symptoms Pediatric Delirium scale), identify modifiable and nonmodifiable risk factors, and explore the association of pediatric delirium with clinical outcomes. DATA SOURCES: A systematic search of PubMed, EMBASE, and CINAHL databases was undertaken for full articles pertaining to pediatric delirium prevalence. STUDY SELECTION: No language or date barriers were set. Studies were included where the following eligibility criteria were met: study design aimed to estimate pediatric delirium prevalence arising from treatment in the intensive care setting, using a validated tool. Only randomized controlled trials, cross-sectional studies, or cohort studies allowing an estimate of the prevalence of pediatric delirium were included. DATA EXTRACTION: Data were extracted by the primary researcher (D.S.) and accuracy checked by coauthors. DATA SYNTHESIS: A narrative synthesis and pooled prevalence meta-analysis were undertaken. CONCLUSIONS: Pediatric delirium, as determined by the Cornell Assessment of Pediatric Delirium score, is estimated to occur in 34% of critical care admissions. Eight of 11 studies reporting on subtype identified hypoactive delirium as most prevalent (46-81%) with each of the three remaining reporting either hyperactive (44%), mixed (57%), or equal percentages of hypoactive and mixed delirium (43%) as most prevalent. The development of pediatric delirium is associated with cumulative doses of benzodiazepines, opioids, the number of sedative classes used, deep sedation, and cardiothoracic surgery. Increased time mechanically ventilated, length of stay, mortality, healthcare costs, and associations with decreased quality of life after discharge were also found. Multi-institutional and longitudinal studies are required to better determine the natural history, true prevalence, long-term outcomes, management strategies, and financial implications of pediatric delirium.

Diagnostic Performance of Hemophagocytic Lymphohistiocytosis Criteria and HScore in Critically Ill Patients With Severe Hemophagocytic Syndrome.

OBJECTIVES: To assess whether critically ill hematologic patients without diagnosis of hemophagocytic lymphohistiocytosis may have features mimicking hemophagocytic lymphohistiocytosis according to both diagnostic scores. DESIGN: A retrospective case-control study. SETTING: Hemophagocytic syndrome diagnosis was standardized and based on a consensual diagnosis by at least two experts of a university hospital which is a reference center for hemophagocytic syndrome. PATIENTS: Cases (hemophagocytic syndrome+) consisted in a group of consecutive patients (n = 150) admitted in our ICU between 2007 and 2018. Control group (hemophagocytic syndrome-) consisted in patients included in a prospective multicenter cohort of hematologic patients in whom three independent experts ruled out the diagnosis of hemophagocytic syndrome (n = 1011). MEASUREMENTS AND MAIN RESULTS: Overall, 1,161 patients were included. Hospital mortality was 45.8% in hemophagocytic syndrome- patients (n = 66) and 38.8% in control patients (n = 392; p = 0.126). Median HScore was 235 (205-262) in hemophagocytic syndrome+ and 42 (18-62) in hemophagocytic syndrome- patients (p < 0.001); number of hemophagocytic lymphohistiocytosis criteria was 4 (4-5) vs 1 (0-1), respectively (p < 0.001). Diagnostic performances of both scores were excellent with area under receiver operating characteristic curve of 0.99 (95% CI, 0.99-0.99) and 0.99 (95% CI, 0.99-0.99) for hemophagocytic lymphohistiocytosis and HScore, respectively. After propensity score matching (n = 144 × 2), the median HScore was 234 (205-262) in hemophagocytic syndrome+ patients versus 49 (18-71) in hemophagocytic syndrome- patients (p < 0.001). Median number of hemophagocytic lymphohistiocytosis criteria was 4 (4-5) in hemophagocytic syndrome+ and 1 (0-1) in hemophagocytic syndrome- patients (p < 0.001). Area under receiver operating characteristic curve was then of 0.98 (95% CI, 0.96-0.99) for hemophagocytic lymphohistiocytosis criteria and 0.99 (95% CI, 0.99-1) for HScore. CONCLUSIONS: In ICU patients, several conditions share some similarities with hemophagocytic syndrome, explaining the poor predictive value of isolated biological markers such as ferritin level. Despite these potential confounding factors, our study suggests HScore and hemophagocytic lymphohistiocytosis criteria to be highly discriminant identifying hemophagocytic syndrome in critically ill patients.

Outcomes of left ventricular assist device implantation for advanced heart failure in critically ill patients (INTERMACS 1 and 2): A retrospective study.

The use of left ventricular assist devices (LVADs) for advanced heart failure is becoming increasingly common. However, optimal timing and patient selection remain controversial. The aim of this study was to investigate outcomes of LVAD implantation for advanced heart failure in critically ill patients (INTERMACS 1 and 2). Between August 2010 and January 2020, 207 consecutive patients underwent LVAD implantation. Overall survival, major adverse events, and laboratory parameters were compared between patients in INTERMACS 1-2 (n = 107) and INTERMACS 3-5 (n = 100). Preoperative white blood cells, C-reactive protein, procalcitonin, bilirubin, alanine transaminase, and lactate dehydrogenase were all significantly higher in INTERMACS 1-2 when compared to INTERMACS 3-5 (P < .05). During hospitalization following LVAD implantation, patients in INTERMACS 1-2 were more likely to develop major infections (41.1% vs. 23.0%, P = .005), respiratory failure (57.9% vs. 25.0%, P < .001), mild (20.6% vs. 8.0%, P = .010), and moderate (31.8% vs. 7.0%, P < .001) right heart failure, and acute renal dysfunction (56.1% vs. 6.0%, P < .001). During a median follow-up of 2.00 years (interquartile range (IQR) 0.24-3.39 years), they had a higher incidence of thoracic (15.9% vs. 4.0%, P = .005) and gastrointestinal bleeding (21.5% vs. 11.0%, P = .042), as well as right heart failure (18.7% vs. 1%, P < .001). Risk of death was significantly higher in the INTERMACS 1-2 group (hazards ratio (HR) 1.64, 95% CI 1.12-2.40, P = .011). LVAD implantation in critically ill patients is associated with increased morbidity and mortality. Our results suggest that decision for LVAD should be not be delayed until INTERMACS 1 and 2 levels whenever possible.

Invasive aspergillosis in critically ill patients: Review of definitions and diagnostic approaches.

Invasive aspergillosis (IA) is an increasingly recognised phenomenon in critically ill patients in the intensive care unit, including in patients with severe influenza and severe coronavirus disease 2019 (COVID-19) infection. To date, there are no consensus criteria on how to define IA in the ICU population, although several criteria are used, including the AspICU criteria and new consensus criteria to categorise COVID-19-associated pulmonary aspergillosis (CAPA). In this review, we describe the epidemiology of IA in critically ill patients, most common definitions used to define IA in this population, and most common clinical specimens obtained for establishing a mycological diagnosis of IA in the critically ill. We also review the most common diagnostic tests used to diagnose IA in this population, and lastly discuss the most common clinical presentation and imaging findings of IA in the critically ill and discuss areas of further needed investigation.

Effect of Low-Normal vs High-Normal Oxygenation Targets on Organ Dysfunction in Critically Ill Patients: A Randomized Clinical Trial.

Importance: Hyperoxemia may increase organ dysfunction in critically ill patients, but optimal oxygenation targets are unknown. Objective: To determine whether a low-normal Pao2 target compared with a high-normal target reduces organ dysfunction in critically ill patients with systemic inflammatory response syndrome (SIRS). Design, Setting, and Participants: Multicenter randomized clinical trial in 4 intensive care units in the Netherlands. Enrollment was from February 2015 to October 2018, with end of follow-up to January 2019, and included adult patients admitted with 2 or more SIRS criteria and expected stay of longer than 48 hours. A total of 9925 patients were screened for eligibility, of whom 574 fulfilled the enrollment criteria and were randomized. Interventions: Target Pao2 ranges were 8 to 12 kPa (low-normal, n = 205) and 14 to 18 kPa (high-normal, n = 195). An inspired oxygen fraction greater than 0.60 was applied only when clinically indicated. Main Outcomes and Measures: Primary end point was SOFARANK, a ranked outcome of nonrespiratory organ failure quantified by the nonrespiratory components of the Sequential Organ Failure Assessment (SOFA) score, summed over the first 14 study days. Participants were ranked from fastest organ failure improvement (lowest scores) to worsening organ failure or death (highest scores). Secondary end points were duration of mechanical ventilation, in-hospital mortality, and hypoxemic measurements. Results: Among the 574 patients who were randomized, 400 (70%) were enrolled within 24 hours (median age, 68 years; 140 women [35%]), all of whom completed the trial. The median Pao2 difference between the groups was -1.93 kPa (95% CI, -2.12 to -1.74; P < .001). The median SOFARANK score was -35 points in the low-normal Pao2 group vs -40 in the high-normal Pao2 group (median difference, 10 [95% CI, 0 to 21]; P = .06). There was no significant difference in median duration of mechanical ventilation (3.4 vs 3.1 days; median difference, -0.15 [95% CI, -0.88 to 0.47]; P = .59) and in-hospital mortality (32% vs 31%; odds ratio, 1.04 [95% CI, 0.67 to 1.63]; P = .91). Mild hypoxemic measurements occurred more often in the low-normal group (1.9% vs 1.2%; median difference, 0.73 [95% CI, 0.30 to 1.20]; P < .001). Acute kidney failure developed in 20 patients (10%) in the low-normal Pao2 group and 21 patients (11%) in the high-normal Pao2 group, and acute myocardial infarction in 6 patients (2.9%) in the low-normal Pao2 group and 7 patients (3.6%) in the high-normal Pao2 group. Conclusions and Relevance: Among critically ill patients with 2 or more SIRS criteria, treatment with a low-normal Pao2 target compared with a high-normal Pao2 target did not result in a statistically significant reduction in organ dysfunction. However, the study may have had limited power to detect a smaller treatment effect than was hypothesized. Trial Registration: ClinicalTrials.gov Identifier: NCT02321072.

The circular RNA landscape in specific peripheral blood mononuclear cells of critically ill patients with sepsis.

BACKGROUND: Dysregulation of the host immune response is a pathognomonic feature of sepsis. Abnormal physiological conditions are understood to shift efficient linear splicing of protein-coding RNA towards non-canonical splicing, characterized by the accumulation of non-coding circularized (circ)RNA. CircRNAs remain unexplored in specific peripheral blood mononuclear cells (PBMCs) during sepsis. We here sought to identify and characterize circRNA expression in specific PBMCs of patients with sepsis due to community-acquired pneumonia (CAP) relative to healthy subjects. METHODS: The study comprised a discovery cohort of six critically ill patients diagnosed with sepsis due to community-acquired pneumonia and four (age, gender matched) healthy subjects. PBMCs were isolated, and fluorescence-activated cell sorting was used to purify CD14+ monocytes, CD4+, CD8+ T cells, and CD19+ B cells for RNA sequencing. CD14+ monocytes from independent six healthy volunteers were purified, and total RNA was treated with or without RNase R. RESULTS: RNA sequencing of sorted CD14+ monocytes, CD4+, CD8+ T cells, and CD19+ B cells from CAP patients and healthy subjects identified various circRNAs with predominantly cell-specific expression patterns. CircRNAs were expressed to a larger extent in monocytes than in CD4+, CD8+ T cells, or B cells. Cells from CAP patients produced significantly higher levels of circRNA as compared to healthy subjects. Considering adjusted p values, circVCAN (chr5:83519349-83522309) and circCHD2 (chr15:93000512-93014909) levels in monocytes were significantly altered in sepsis. Functional inference per cell-type uncovered pathways mainly attuned to cell proliferation and cytokine production. In addition, our data does not support a role for these circRNAs in microRNA sequestration. Quantitative PCR analysis in purified monocytes from an independent group of healthy volunteers confirmed the existence of circVCAN and circCHD2. CONCLUSIONS: We provide a benchmark map of circRNA expression dynamics in specific immune cell subsets of sepsis patients secondary to CAP. CircRNAs were more abundant in immune cells of sepsis patients relative to healthy subjects. Further studies evaluating circRNA expression in larger cohorts of sepsis patients are warranted.

Defining persistent critical illness based on growth trajectories in patients with sepsis.

BACKGROUND: Persistent critical illness is common in critically ill patients and is associated with vast medical resource use and poor clinical outcomes. This study aimed to define when patients with sepsis would be stabilized and transitioned to persistent critical illness, and whether such transition time varies between latent classes of patients. METHODS: This was a retrospective cohort study involving sepsis patients in the eICU Collaborative Research Database. Persistent critical illness was defined at the time when acute physiological characteristics were no longer more predictive of in-hospital mortality (i.e., vital status at hospital discharge) than antecedent characteristics. Latent growth mixture modeling was used to identify distinct trajectory classes by using Sequential Organ Failure Assessment score measured during intensive care unit stay as the outcome, and persistent critical illness transition time was explored in each latent class. RESULTS: The mortality was 16.7% (3828/22,868) in the study cohort. Acute physiological model was no longer more predictive of in-hospital mortality than antecedent characteristics at 15 days after intensive care unit admission in the overall population. Only a minority of the study subjects (n = 643, 2.8%) developed persistent critical illness, but they accounted for 19% (15,834/83,125) and 10% (19,975/198,833) of the total intensive care unit and hospital bed-days, respectively. Five latent classes were identified. Classes 1 and 2 showed increasing Sequential Organ Failure Assessment score over time and transition to persistent critical illness occurred at 16 and 27 days, respectively. The remaining classes showed a steady decline in Sequential Organ Failure Assessment scores and the transition to persistent critical illness occurred between 6 and 8 days. Elevated urea-to-creatinine ratio was a good biochemical signature of persistent critical illness. CONCLUSIONS: While persistent critical illness occurred in a minority of patients with sepsis, it consumed vast medical resources. The transition time differs substantially across latent classes, indicating that the allocation of medical resources should be tailored to different classes of patients.

Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study.

BACKGROUND: To develop a mathematical model to estimate daily evolution of disease severity using routinely available parameters in patients admitted to the intensive care unit (ICU). METHODS: Over a 3-year period, we prospectively enrolled consecutive adults with sepsis and categorized patients as (1) being at risk for developing (more severe) organ dysfunction, (2) having (potentially still reversible) limited organ failure, or (3) having multiple-organ failure. Daily probabilities for transitions between these disease states, and to death or discharge, during the first 2 weeks in ICU were calculated using a multi-state model that was updated every 2 days using both baseline and time-varying information. The model was validated in independent patients. RESULTS: We studied 1371 sepsis admissions in 1251 patients. Upon presentation, 53 (4%) were classed at risk, 1151 (84%) had limited organ failure, and 167 (12%) had multiple-organ failure. Among patients with limited organ failure, 197 (17%) evolved to multiple-organ failure or died and 809 (70%) improved or were discharged alive within 14 days. Among patients with multiple-organ failure, 67 (40%) died and 91 (54%) improved or were discharged. Treatment response could be predicted with reasonable accuracy (c-statistic ranging from 0.55 to 0.81 for individual disease states, and 0.67 overall). Model performance in the validation cohort was similar. CONCLUSIONS: This prediction model that estimates daily evolution of disease severity during sepsis may eventually support clinicians in making better informed treatment decisions and could be used to evaluate prognostic biomarkers or perform in silico modeling of novel sepsis therapies during trial design. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT01905033.

More comprehensively measuring quality of life in life-threatening illness: the McGill Quality of Life Questionnaire - Expanded.

BACKGROUND: Domains other than those commonly measured (physical, psychological, social, and sometimes existential/spiritual) are important to the quality of life of people with life-threatening illness. The McGill Quality of Life Questionnaire (MQOL) - Revised measures the four common domains. The aim of this study was to create a psychometrically sound instrument, MQOL - Expanded, to comprehensively measure quality of life by adding to MQOL-Revised the domains of cognition, healthcare, environment, (feeling like a) burden, and possibly, finance. METHODS: Confirmatory factor analyses were conducted on three datasets to ascertain whether seven new items belonged with existing MQOL-Revised domains, whether good model fit was obtained with their addition as five separate domains to MQOL-Revised, and whether a second-order factor representing overall quality of life was present. People with life-threatening illnesses (mainly cancer) or aged > 80 were recruited from 15 healthcare sites in seven Canadian provinces. Settings included: palliative home care and inpatient units; acute care units; oncology outpatient clinics. RESULTS: Good model fit was obtained when adding each of the five domains separately to MQOL-Revised and for the nine correlated domains. Fit was acceptable for a second-order factor model. The financial domain was removed because of low importance. The resulting MQOL-Expanded is a 21-item instrument with eight domains (fit of eight correlated domains: Comparative Fit Index = .96; Root Mean Square Error of Approximation = .033). CONCLUSIONS: MQOL-Expanded builds on MQOL-Revised to more comprehensively measure the quality of life of people with life-threatening illness. Our analyses provide validity evidence for the MQOL-Expanded domain and summary scores; the need for further validation research is discussed. Use of MQOL-Expanded will enable a more holistic understanding of the quality of life of people with a life-threatening illness and the impact of treatments and interventions upon it. It will allow for a better understanding of less commonly assessed but important life domains (cognition, healthcare, environment, feeling like a burden) and their relationship to the more commonly assessed domains (physical, psychological, social, existential/spiritual).

Early Lactate Dynamics in Critically Ill Non-Traumatic Patients in a Resuscitation Room of a German Emergency Department (OBSERvE-Lactate-Study).

BACKGROUND: Management of critically ill non-trauma patients in the resuscitation room of an emergency department (ED) is very challenging, and it is difficult to identify patients with a higher risk of death. Previous studies have shown that lactate indices can predict survival for selected diseases and syndromes. OBJECTIVE: As reported for other patient populations, we set out to determine whether admission lactate or lactate dynamics (LD) within 24 h can predict 30-day mortality in unselected critically ill non-traumatic patients. METHODS: In this retrospective study over a 1-year period, admission lactate, time weighted average lactate (LacTW) and LD of all critically ill adult patients admitted from ED to intensive care unit were analyzed. A linear regression model was implemented to estimate lactate data 1 h after admission. RESULTS: The admission lactate, LacTW, and LD within 24 h were analyzed from 392 critically ill patients. The overall 30-day mortality rate was around 29%. Admission lactate (4.1 ± 4.0 mmol/L vs. 6.6 ± 6.1 mmol/L; p < 0.01) and LacTW (1.8 ± 1.7 mmol/L vs. 4.1 ± 4.8 mmol/L; p < 0.01) were different between survivors and non-survivors. LD between survivors and non-survivors did not differ at 1 h, 6 h, 12 h, or 24 h. After excluding patients with out-of-hospital or in-hospital cardiac arrest during resuscitation room management, admission lactate and LD between survivors and non-survivors did not differ at 1 h, 12 h, and 24 h. LD at 6 h (44% ± 42% vs. 33% ± 58%; p = 0.042) and LacTW (1.7 ± 1.6 mmol/L vs. 2.6 ± 3.0 mmol/L; p < 0.01) did differ. CONCLUSIONS: In critically ill ED patients initially requiring treatment in a resuscitation room setting, LD at 6 h and LacTW may predict their survival beyond 30 days. These findings need to be confirmed in a prospective study design.

The National Early Warning Score predicts mortality in hospital ward patients with deviating vital signs: A retrospective medical record review study.

AIMS AND OBJECTIVES: To evaluate whether the scale used for assessment of hospital ward patients could predict in-hospital and 30-day mortality amongst those with deviating vital signs; that is, that patients classified as medium or high risk would have increased risk of in-hospital and 30-day mortality compared to patients with low risk. BACKGROUND: The National Early Warning Score (NEWS) is a widely adopted scale for assessing deviating vital signs. A clinical risk scale that comes with the NEWS divides the risk for critical illness into three risk categories, low, medium and high. DESIGN: Retrospective analysis of vital sign data. METHODS: Logistic regression models for age-adjusted in-hospital and 30-day mortality were used for analyses of 1,107 patients with deviating vital signs. RESULTS: Patients classified as medium or high risk by NEWS experienced a 2.11 or 3.40 increase, respectively, in odds of in-hospital death (95% CI: 1.27-3.51, p = 0.004% and 95% CI: 1.90-6.01, p < 0.001) compared to low-risk patients. Moreover, those with NEWS medium or high risk were associated with a 1.98 or 3.19 increase, respectively, in odds of 30-day mortality (95% CI: 1.32-2.97, p = 0.001% and 95% CI: 1.97-5.18, p < 0.001). CONCLUSION: The NEWS risk classification seems to be a reliable predictor of mortality on patients in hospital wards. RELEVANCE TO CLINICAL PRACTICE: The NEWS risk classification offers a simple way to identify deteriorating patients and can aid the healthcare staff to prioritise amongst patients.

Severity of Illness Scores May Misclassify Critically Ill Obese Patients.

OBJECTIVE: Severity of illness scores rest on the assumption that patients have normal physiologic values at baseline and that patients with similar severity of illness scores have the same degree of deviation from their usual state. Prior studies have reported differences in baseline physiology, including laboratory markers, between obese and normal weight individuals, but these differences have not been analyzed in the ICU. We compared deviation from baseline of pertinent ICU laboratory test results between obese and normal weight patients, adjusted for the severity of illness. DESIGN: Retrospective cohort study in a large ICU database. SETTING: Tertiary teaching hospital. PATIENTS: Obese and normal weight patients who had laboratory results documented between 3 days and 1 year prior to hospital admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Seven hundred sixty-nine normal weight patients were compared with 1,258 obese patients. After adjusting for the severity of illness score, age, comorbidity index, baseline laboratory result, and ICU type, the following deviations were found to be statistically significant: WBC 0.80 (95% CI, 0.27-1.33) × 10/L; p = 0.003; log (blood urea nitrogen) 0.01 (95% CI, 0.00-0.02); p = 0.014; log (creatinine) 0.03 (95% CI, 0.02-0.05), p < 0.001; with all deviations higher in obese patients. A logistic regression analysis suggested that after adjusting for age and severity of illness at least one of these deviations had a statistically significant effect on hospital mortality (p = 0.009). CONCLUSIONS: Among patients with the same severity of illness score, we detected clinically small but significant deviations in WBC, creatinine, and blood urea nitrogen from baseline in obese compared with normal weight patients. These small deviations are likely to be increasingly important as bigger data are analyzed in increasingly precise ways. Recognition of the extent to which all critically ill patients may deviate from their own baseline may improve the objectivity, precision, and generalizability of ICU mortality prediction and severity adjustment models.

Inclusion and definition of acute renal dysfunction in critically ill patients in randomized controlled trials: a systematic review.

BACKGROUND: In evidence-based medicine, multicenter, prospective, randomized controlled trials (RCTs) are the gold standard for evaluating treatment benefits and ensuring the effectiveness of interventions. Patient-centered outcomes, such as mortality, are most often the preferred evaluated outcomes. While there is currently agreement on how to classify renal dysfunction in critically ill patients , the application frequency of this new classification system in RCTs has not previously been evaluated. In this study, we aim to assess the definition of renal dysfunction in multicenter RCTs involving critically ill patients that included mortality as a primary endpoint. METHODS: A comprehensive search was conducted for publications reporting multicenter randomized controlled trials (RCTs) involving adult patients in intensive care units (ICUs) that included mortality as a primary outcome. MEDLINE and PUBMED were queried for relevant articles in core clinical journals published between May 2004 and December 2017. RESULTS: Of 418 articles reviewed, 46 multicenter RCTs with a primary endpoint related to mortality were included. Thirty-six (78.3%) of the trial reports provided information on renal function in the participants. Only seven articles (15.2%) included mean or median serum creatinine levels, mean creatinine clearance or estimated glomerular filtration rates. Sequential organ failure assessment (SOFA) score was the most commonly used definition of renal dysfunction (20 studies; 43.5%). Risk, Injury, Failure, Loss, End-stage renal disease (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease Improving Global Outcomes (KDIGO) criteria were used in five (10.9%) trials. In thirteen trials (28.3%), no renal dysfunction criteria were reported. Only one trial excluded patients with renal dysfunction, and it used urinary output or need for renal replacement therapy (RRT) as criteria for this diagnosis. CONCLUSION: The presence of renal dysfunction was included as a baseline patient characteristic in most RCTs. The RIFLE, AKIN and KDIGO classification systems were infrequently used; renal dysfunction was generally defined using the SOFA score.

[Benefits of medical emergency teams : Mortality on normal wards and readmission to intensive care wards]. / Nutzen von "medical emergency teams" : Sterblichkeit auf Normalstationen und Rückverlegungsrate auf Intensivstationen.

Medical emergency teams (MET) were implemented in many hospitals worldwide in order to identify patients at risk on normal wards and to initiate diagnostics and therapy without delay. Ideally, the implementation leads to prevention of cardiac arrests and unexpected deaths on normal wards, reduced rates of admissions to intensive care units and hospital mortality. Various track and trigger systems are available to identify such patients and for them to be assessed and treated within 30-45 min by the MET. The ideal personnel composition of METs has not yet been established. Whether the implementation of an MET generally leads to an improvement of treatment on normal wards or to a reduction in mortality in hospitals has not been finally clarified. Mortality and morbitidy (M&M) conferences can help to analyze if an individual clinic is likely to profit from the introduction of a MET.

[Hypertensive crisis]. / Les crises hypertensives.

Hypertensive crisis has fortunately become rarer due to a better diagnosis and management of arterial hypertension. However, its development needs urgent management with adapted therapy according to the severity of the blood pressure levels and the associated clinical signs. After confirmation of severe hypertension (blood pressure above or equal to 180/120 mmHg), target organ lesions have to be looked for and according to their pre-sence, an urgent hospitalization has to be immediately organized. Starting active drug therapy often occurs in intensive units with the intravenous route of administration.

La crise hypertensive est devenue moins fréquente qu'auparavant, grâce au dépistage de l'hypertension et à sa prise en charge thérapeutique. Cependant, sa survenue expose le patient à un risque vital considérable. Il faut donc ne pas rater le diagnostic et la mise au point pour démarrer rapidement un traitement adapté selon le niveau de pression artérielle et les signes cliniques associés. Après avoir confirmé l'existence d'une hypertension sévère (pression supérieure ou égale à 180/120 mmHg), il faut rechercher la présence d'une défaillance viscérale. Si celle-ci est observée, une hospitalisation urgente s'impose avec un traitement sans attendre, souvent réalisé aux soins intensifs et par voie intraveineuse.

Incidence, timing and outcome of AKI in critically ill patients varies with the definition used and the addition of urine output criteria.

BACKGROUND: Acute kidney injury (AKI) is a serious complication of critical illness with both attributed morbidity and mortality at short-term and long-term. The incidence of AKI reported in critically ill patients varies substantially with the population evaluated and the definitions used. We aimed to assess which of the AKI definitions (RIFLE, AKIN or KDIGO) with or without urine output criteria recognizes AKI most frequently and quickest. Additionally, we conducted a review on the comparison of incidence proportions of varying AKI definitions in populations of critically ill patients. METHODS: We included all patients with index admissions to our intensive care unit (ICU) from January 1st, 2014 until June 11th, 2014 to determine the incidence and onset of AKI by RIFLE, AKIN and KDIGO during the first 7 days of ICU admission. We conducted a sensitive search using PubMed evaluating the comparison of RIFLE, AKIN and KDIGO in critically ill patients RESULTS: AKI incidence proportions were 15, 21 and 20% respectively using serum creatinine criteria of RIFLE, AKIN and KDIGO. Adding urine output criteria increased AKI incidence proportions to 35, 38 and 38% using RIFLE, AKIN and KDIGO definitions. Urine output criteria detected AKI in patients without AKI at ICU admission in a median of 13 h (IQR 7-22 h; using RIFLE definition) after admission compared to a median of 24 h using serum creatinine criteria (IQR24-48 h). In the literature a large heterogeneity exists in patients included, AKI definition used, reference or baseline serum creatinine used, and whether urine output in the staging of AKI is used. CONCLUSION: AKIN and KDIGO criteria detect more patients with AKI compared to RIFLE criteria. Addition of urine output criteria detect patients with AKI 11 h earlier than serum creatinine criteria and may double AKI incidences in critically ill patients. This could explain the large heterogeneity observed in literature.

Rasch Analysis of a Behavioral Checklist for the Assessment of Pain in Critically Ill Adults.

Patients hospitalized in the intensive care unit (ICU) are often unable to report their pain, which is a problem since untreated pain is associated with negative health outcomes. The use of behavioral pain scales are recommended for the detection of the presence of pain in this vulnerable population. Previous validation studies have used classical techniques, and several psychometrics properties remain unknown. In this paper, data obtained from a behavioral checklist of dichotomized items was utilized to evaluate the instrument's dimensionality, its construct validity and its capacity to distinguish between levels of pain by using Rasch measurement. A sample of 239 ICU patients was used to collect the data. Results showed that, while unidimensionality was acceptable, concerns remained about the local independence and item fit indices. A third of the items showed misfit. Finally, while items had a great reliability (0.97), persons' measures had a rather low reliability (0.62) and only 1.28 strata of pain could be distinguished. The narrow range of pain levels in the sample could explain this poor performance and further study is needed, with a sample exhibiting a wider range of pain levels.

[Clinical significance of acute gastrointestinal injury grades in inflammatory response of critically ill patients].

Objective: To explore the value of acute gastrointestinal injury (AGI) grades in inflammatory response of critically ill patients. Methods: Ptients with AGI severity from Ⅰ to Ⅳ were randomly selected(20 for each)from July 2014 to June 2015 in ICU of Yantai Yuhuangding Hospital. The expression of NF-κB were detected by immunofluorescence. The expression of TNF-α and IL-6 were detected by enzyme linked immunosorbent assay (ELISA). Procalcitonin (PCT) and C reactive protein (CRP) were measured. Statistical analysis was carried out. Results: For AGI grade Ⅰ and AGI grade Ⅱ patients, NF- κB p65 were located mainly in cytoplasm. For AGI grade Ⅲ and AGI grade Ⅳ patients, NF-κB p65 were mainly located in the nucleus, indicating that inflammatory stimulation induces nuclear translocation of NF-κB. With the higher grade of AGI, TNF-α and IL-6 secretion increased significantly.For AGI grade Ⅰ to grade Ⅳ patients, TNF-α were expressed as (89.76±19.78)ng/L, (130.54±23.18)ng/L, (224.65±39.02)ng/L, (293.17±36.79)ng/L, and the difference was statistically significant (P<0.05) respectively. IL-6 were expressed as (45.96±9.62)ng/L, (89.26±12.77)ng/L, (203.71±58.26)ng/L, (331.18±64.28) ng/L, the difference was statistically significant (P<0.05). With the higher grade of AGI, PCT and CRP levels were significantly increased.For AGI grade Ⅰ to grade Ⅳ patients, PCT levels were (2.65±1.78) µg/L, (3.92±2.14) µg/L, (9.92±3.89) µg/L, (27.34±8.45) µg/L, and the difference was statistically significant (P<0.05). CRP levels were (13.82±4.93) mg/L, (32.14±8.97) mg/L, (93.49±25.72) mg/L, (183.05±51.36) mg/L, and the difference between each group was statistically significant (P<0.05). Conclusions: There is a certain correlation between AGI classification and inflammatory markers in critically ill patients, which shows that gastrointestinal dysfunction may be the promoter and stimulating factor in systemic inflammatory response syndrome (SIRS) and multiple organ dysfunction syndrome (MODS). This provides a clinical basis for judging the severity of inflammatory response in critically ill patients according to AGI grades.

A prospective multicenter cohort study of frailty in younger critically ill patients.

BACKGROUND: Frailty is a multidimensional syndrome characterized by loss of physiologic and cognitive reserve that heightens vulnerability. Frailty has been well described among elderly patients (i.e., 65 years of age or older), but few studies have evaluated frailty in nonelderly patients with critical illness. We aimed to describe the prevalence, correlates, and outcomes associated with frailty among younger critically ill patients. METHODS: We conducted a prospective cohort study of 197 consecutive critically ill patients aged 50-64.9 years admitted to intensive care units (ICUs) at six hospitals across Alberta, Canada. Frailty was defined as a score ≥5 on the Clinical Frailty Scale before hospitalization. Multivariable analyses were used to evaluate factors independently associated with frailty before ICU admission and the independent association between frailty and outcome. RESULTS: In the 197 patients in the study, mean (SD) age was 58.5 (4.1) years, 37 % were female, 73 % had three or more comorbid illnesses, and 28 % (n = 55; 95 % CI 22-35) were frail. Factors independently associated with frailty included not being completely independent (adjusted OR [aOR] 4.4, 95 % CI 1.8-11.1), connective tissue disease (aOR 6.0, 95 % CI 2.1-17.0), and hospitalization within the preceding year (aOR 3.3, 95 % CI 1.3-8.1). There were no significant differences between frail and nonfrail patients in reason for admission, Acute Physiology and Chronic Health Evaluation II score, preference for life support, or treatment intensity. Younger frail patients did not have significantly longer (median [interquartile range]) hospital stay (26 [9-68] days vs. 19 [10-43] days; p = 0.4), but they had greater 1-year rehospitalization rates (61 % vs. 40 %; p = 0.02) and higher 1-year mortality (33 % vs. 20 %; adjusted HR 1.8, 95 % CI 1.0-3.3; p = 0.039). CONCLUSIONS: Prehospital frailty is common among younger critically ill patients, and in this study it was associated with higher rates of mortality at 1 year and with rehospitalization. Our data suggest that frailty should be considered in younger adults admitted to the ICU, not just in the elderly. Additional research is needed to further characterize frailty in younger critically ill patients, along with the ideal instruments for identification.