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Background: Increased protein provision might ameliorate muscle wasting and improve long-term outcomes in critically ill patients. The aim of the PRECISe trial was to assess whether higher enteral protein provision (ie, 2·0 g/kg per day) would improve health-related quality of life and functional outcomes in critically ill patients who were mechanically ventilated compared with standard enteral protein provision (ie, 1·3 g/kg per day)., Methods: The PRECISe trial was an investigator-initiated, double-blinded, multicentre, parallel-group, randomised controlled trial in five Dutch hospitals and five Belgian hospitals. Inclusion criteria were initiation of invasive mechanical ventilation within 24 h of intensive care unit (ICU) admission and an expected duration of invasive ventilation of 3 days or longer. Exclusion criteria were contraindications for enteral nutrition, moribund condition, BMI less than 18 kg/m 2 , kidney failure with a no dialysis code, or hepatic encephalopathy. Patients were randomly assigned to one of four randomisation labels, corresponding with two study groups (ie, standard or high protein; two labels per group) in a 1:1:1:1 ratio through an interactive web-response system. Randomisation was done via random permuted-block randomisation in varying block sizes of eight and 12, stratified by centre. Participants, care providers, investigators, outcome assessors, data analysts, and the independent data safety monitoring board were all blinded to group allocation. Patients received isocaloric enteral feeds that contained 1·3 kcal/mL and 0·06 g of protein/mL (ie, standard protein) or 1·3 kcal/mL and 0·10 g of protein/mL (ie, high protein). The study-nutrition intervention was limited to the time period during the patient's ICU stay in which they required enteral feeding, with a maximum of 90 days. The primary outcome was EuroQoL 5-Dimension 5-level (EQ-5D-5L) health utility score at 30 days, 90 days, and 180 days after randomisation, adjusted for baseline EQ-5D-5L health utility score. This trial was registered with ClinicalTrials.gov (NCT04633421) and is closed to new participants., Findings: Between Nov 19, 2020, and April 14, 2023, 935 patients were randomly assigned. 335 (35·8%) of 935 patients were female and 600 (64·2%) were male. 465 (49·7%) of 935 were assigned to the standard protein group and 470 (50·3%) were assigned to the high protein group. 430 (92·5%) of 465 patients in the standard protein group and 419 (89·1%) of 470 patients in the high protein group were assessed for the primary outcome. The primary outcome, EQ-5D-5L health utility score during 180 days after randomisation (assessed at 30 days, 90 days, and 180 days), was lower in patients allocated to the high protein group than in those allocated to the standard protein group, with a mean difference of -0·05 (95% CI -0·10 to -0·01; p=0·031). Regarding safety outcomes, the probability of mortality during the entire follow-up was 0·38 (SE 0·02) in the standard protein group and 0·42 (0·02) in the high protein group (hazard ratio 1·14, 95% CI 0·92 to 1·40; p=0·22). There was a higher incidence of symptoms of gastrointestinal intolerance in patients in the high protein group (odds ratio 1·76, 95% CI 1·06 to 2·92; p=0·030). Incidence of other adverse events did not differ between groups., Interpretation: High enteral protein provision compared with standard enteral protein provision resulted in worse health-related quality of life in critically ill patients and did not improve functional outcomes during 180 days after ICU admission., Funding: Netherlands Organisation for Healthcare Research and Development and Belgian Health Care Knowledge Centre., Competing Interests: Declaration of interests EDW receives honoraria for scientific lectures from Baxter Healthcare, Nutricia–Danone, and Nestlé. ARHvZ receives grants from AOP Pharma, Nutricia–Danone, Fresenius Kabi, PAION, and Rousselot; receives consulting fees from AOP Pharma, Medcaptain, and PAION; receives honoraria for lectures from Abbott, AOP Pharma, Baxter, Nestlé, Nutricia–Danone, Fresenius Kabi, GE Healthcare, and Dutch Medical Food; receives support for travel from Nutricia–Danone and Dutch Medical Food; is a member of the adult intensive care unit patient nutrition guideline committee of the European Society for Clinical Nutrition and Metabolism, the executive team of SepsisNet Netherlands, and the executive team of the Netherlands Society for Parenteral and Enteral Nutrition; and is the European Society of Intensive Care Medicine Section Feeding, Rehabilitation Endocrinology and Metabolism chair. ZP receives grants from Fresenius Kabi, Nestlé, and Baxter; receives consulting fees from Fresenius Kabi, Nestlé, Baxter, Faraday Pharmaceuticals, and Bioage Pharmaceuticals; and receives honoraria for lectures from Baxter, Nestlé, Fresenius Kabi, and Nutricia. MCGvdP receives grants from the Netherlands Organisation of Health Research and Development and the Belgian Health Care Knowledge Centre; receives in-kind support from Nutricia; receives consulting fees from Nutricia and Nestlé; and receives honoraria for lectures and support for travel from Nutricia. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Electrical Impedance Tomography (EIT) is a non-invasive bedside imaging technique that provides real-time lung ventilation information on critically ill patients. EIT can potentially become a valuable tool for optimising mechanical ventilation, especially in patients with acute respiratory distress syndrome (ARDS). In addition, EIT has been shown to improve the understanding of ventilation distribution and lung aeration, which can help tailor ventilatory strategies according to patient needs. Evidence from critically ill patients shows that EIT can reduce the duration of mechanical ventilation and prevent lung injury due to overdistension or collapse. EIT can also identify the presence of lung collapse or recruitment during a recruitment manoeuvre, which may guide further therapy. Despite its potential benefits, EIT has not yet been widely used in clinical practice. This may, in part, be due to the challenges associated with its implementation, including the need for specialised equipment and trained personnel and further validation of its usefulness in clinical settings. Nevertheless, ongoing research focuses on improving mechanical ventilation and clinical outcomes in critically ill patients.

Objectives: In a recent scoping review, we identified 43 mortality prediction models for critically ill patients. We aimed to assess the performances of these models through external validation., Design: Multicenter study., Setting: External validation of models was performed in the Simple Intensive Care Studies-I (SICS-I) and the Finnish Acute Kidney Injury (FINNAKI) study., Patients: The SICS-I study consisted of 1,075 patients, and the FINNAKI study consisted of 2,901 critically ill patients., Measurements and Main Results: For each model, we assessed: 1) the original publications for the data needed for model reconstruction, 2) availability of the variables, 3) model performance in two independent cohorts, and 4) the effects of recalibration on model performance. The models were recalibrated using data of the SICS-I and subsequently validated using data of the FINNAKI study. We evaluated overall model performance using various indexes, including the (scaled) Brier score, discrimination (area under the curve of the receiver operating characteristics), calibration (intercepts and slopes), and decision curves. Eleven models (26%) could be externally validated. The Acute Physiology And Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS)-Reduced (SAPS-R)' and Simplified Mortality Score for the ICU models showed the best scaled Brier scores of 0.11' 0.10' 0.10' and 0.06' respectively. SAPS II, APACHE II, and APACHE IV discriminated best; overall discrimination of models ranged from area under the curve of the receiver operating characteristics of 0.63 (0.61-0.66) to 0.83 (0.81-0.85). We observed poor calibration in most models, which improved to at least moderate after recalibration of intercepts and slopes. The decision curve showed a positive net benefit in the 0-60% threshold probability range for APACHE IV and SAPS-R., Conclusions: In only 11 out of 43 available mortality prediction models, the performance could be studied using two cohorts of critically ill patients. External validation showed that the discriminative ability of APACHE II, APACHE IV, and SAPS II was acceptable to excellent, whereas calibration was poor., Competing Interests: Drs. Granholm and Møller were involved in the development of one of the prediction models included. Dr. Pleijhuis reports a minority stake in Evidencio B.V., an online platform offering free services regarding the creation, validation, and implementation of clinical prediction models. Evidencio was not involved in the development of any of the prediction models mentioned, nor is it expected to benefit financially by the publication of this manuscript. Dr. Granholm received funding from Sygeforsikringen “Danmark”; he disclosed that he was involved in the development of one of the prediction models assessed in the study. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Objectives: Interhospital transports of critically ill patients are high-risk medical interventions. Well-established parameters to quantify the quality of transports are currently lacking. We aimed to develop and cross-validate a score for interhospital transports., Setting: An expert panel developed a score for interhospital transport by a Mobile Intensive Care Unit (MICU), the QUality of Interhospital Transportation in the Euregion Meuse-Rhine (QUIT-EMR) score. The QUIT-EMR score is an overall sum score that includes component scores of monitoring and intervention variables of the neurological (proxy for airway patency), respiratory and circulatory organ systems, ranging from -12 to +12. A score of 0 or higher defines an adequate transport. The QUIT-EMR score was tested to help to quantify the quality of transport., Participants: One hundred adult patients were randomly included and the transport charts were independently reviewed and classified as adequate or inadequate by four transport experts (ie, anaesthetists/intensivists)., Outcome Measures: Subsequently, the level of agreement between the QUIT-EMR score and expert classification was calculated using Gwet's AC 1 ., Results: From April 2012 to May 2014, a total of 100 MICU transports were studied. The median (IQR) QUIT-EMR score was 1 (0-2). Experts classified six transports as inadequate. The percentage agreement between the QUIT-EMR score and experts' classification for adequate/inadequate transport ranged from 84% to 92% (Gwet's AC 1 0.81-0.91). The interobserver agreement between experts was 87% to 94% (Gwet's AC 1 0.89-0.98)., Conclusion: The QUIT-EMR score is a novel validated tool to score MICU transportation adequacy in future studies contributing to quality control and improvement., Trial Registration Number: NTR 4937., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Prognostic assessments of the mortality of critically ill patients are frequently performed in daily clinical practice and provide prognostic guidance in treatment decisions. In contrast to several sophisticated tools, prognostic estimations made by healthcare providers are always available and accessible, are performed daily, and might have an additive value to guide clinical decision-making. The aim of this study was to evaluate the accuracy of students', nurses', and physicians' estimations and the association of their combined estimations with in-hospital mortality and 6-month follow-up., Methods: The Simple Observational Critical Care Studies is a prospective observational single-center study in a tertiary teaching hospital in the Netherlands. All patients acutely admitted to the intensive care unit were included. Within 3 h of admission to the intensive care unit, a medical or nursing student, a nurse, and a physician independently predicted in-hospital and 6-month mortality. Logistic regression was used to assess the associations between predictions and the actual outcome; the area under the receiver operating characteristics (AUROC) was calculated to estimate the discriminative accuracy of the students, nurses, and physicians., Results: In 827 out of 1,010 patients, in-hospital mortality rates were predicted to be 11%, 15%, and 17% by medical students, nurses, and physicians, respectively. The estimations of students, nurses, and physicians were all associated with in-hospital mortality (OR 5.8, 95% CI [3.7, 9.2], OR 4.7, 95% CI [3.0, 7.3], and OR 7.7 95% CI [4.7, 12.8], respectively). Discriminative accuracy was moderate for all students, nurses, and physicians (between 0.58 and 0.68). When more estimations were of non-survival, the odds of non-survival increased (OR 2.4 95% CI [1.9, 3.1]) per additional estimate, AUROC 0.70 (0.65, 0.76). For 6-month mortality predictions, similar results were observed., Conclusions: Based on the initial examination, students, nurses, and physicians can only moderately predict in-hospital and 6-month mortality in critically ill patients. Combined estimations led to more accurate predictions and may serve as an example of the benefit of multidisciplinary clinical care and future research efforts., (© 2021. The Author(s).)

Critically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25-56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

Introduction: The course of the disease in SARS-CoV-2 infection in mechanically ventilated patients is unknown. To unravel the clinical heterogeneity of the SARS-CoV-2 infection in these patients, we designed the prospective observational Maastricht Intensive Care COVID cohort (MaastrICCht). We incorporated serial measurements that harbour aetiological, diagnostic and predictive information. The study aims to investigate the heterogeneity of the natural course of critically ill patients with a SARS-CoV-2 infection., Methods and Analysis: Mechanically ventilated patients admitted to the intensive care with a SARS-CoV-2 infection will be included. We will collect clinical variables, vital parameters, laboratory variables, mechanical ventilator settings, chest electrical impedance tomography, ECGs, echocardiography as well as other imaging modalities to assess heterogeneity of the course of a SARS-CoV-2 infection in critically ill patients. The MaastrICCht is also designed to foster various other studies and registries and intends to create an open-source database for investigators. Therefore, a major part of the data collection is aligned with an existing national intensive care data registry and two international COVID-19 data collection initiatives. Additionally, we create a flexible design, so that additional measures can be added during the ongoing study based on new knowledge obtained from the rapidly growing body of evidence. The spread of the COVID-19 pandemic requires the swift implementation of observational research to unravel heterogeneity of the natural course of the disease of SARS-CoV-2 infection in mechanically ventilated patients. Our study design is expected to enhance aetiological, diagnostic and prognostic understanding of the disease. This paper describes the design of the MaastrICCht., Ethics and Dissemination: Ethical approval has been obtained from the medical ethics committee (Medisch Ethische Toetsingscommissie 2020-1565/3 00 523) of the Maastricht University Medical Centre+ (Maastricht UMC+), which will be performed based on the Declaration of Helsinki. During the pandemic, the board of directors of Maastricht UMC+ adopted a policy to inform patients and ask their consent to use the collected data and to store serum samples for COVID-19 research purposes. All study documentation will be stored securely for fifteen years after recruitment of the last patient. The results will be published in peer-reviewed academic journals, with a preference for open access journals, while particularly considering deposition of the manuscripts on a preprint server early., Trial Registration Number: The Netherlands Trial Register (NL8613)., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: The aim was to compare non-invasive blood pressure measurements with invasive blood pressure measurements in critically ill patients., Methods: Non-invasive blood pressure was measured via automated brachial cuff oscillometry, and simultaneously the radial arterial catheter-derived measurement was recorded as part of a prospective observational study. Measurements of systolic arterial pressure (SAP), diastolic arterial pressure (DAP), and mean arterial pressure (MAP) were compared using Bland-Altman and error grid analyses., Results: Paired measurements of blood pressure were available for 736 patients. Observed mean difference (±SD, 95% limits of agreement) between oscillometrically and invasively measured blood pressure was 0.8 mmHg (±15.7 mmHg, -30.2 to 31.7 mmHg) for SAP, -2.9 mmHg (±11.0 mmHg, -24.5 to 18.6 mmHg) for DAP, and -1.0 mmHg (±10.2 mmHg, -21.0 to 18.9 mmHg) for MAP. Error grid analysis showed that the proportions of measurements in risk zones A to E were 78.3%, 20.7%, 1.0%, 0%, and 0.1% for MAP., Conclusion: Non-invasive blood pressure measurements using brachial cuff oscillometry showed large limits of agreement compared to invasive measurements in critically ill patients. Error grid analysis showed that measurement differences between oscillometry and the arterial catheter would potentially have triggered at least low-risk treatment decisions in one in five patients., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients., Methods: Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication., Results: In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R 2 (4.7%)., Conclusions: Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations., (© 2019 The Authors. Acta Anaesthesiologica Scandinavica published by John Wiley & Sons Ltd on behalf of Acta Anaesthesiologica Scandinavica Foundation.)

Background: Mortality rates associated with acute kidney injury (AKI) vary among critically ill patients. Outcomes are often not corrected for severity or duration of AKI. Our objective was to analyse whether a new variable, AKI burden, would outperform 1) presence of AKI, 2) highest AKI stage, or 3) AKI duration in predicting 90-day mortality., Methods: Kidney Diseases: Improving Global Outcomes (KDIGO) criteria using creatinine, urine output and renal replacement therapy were used to diagnose AKI. AKI burden was defined as AKI stage multiplied with the number of days that each stage was present (maximum five), divided by the maximum possible score yielding a proportion. The AKI burden as a predictor of 90-day mortality was assessed in two independent cohorts (Finnish Acute Kidney Injury, FINNAKI and Simple Intensive Care Studies I, SICS-I) by comparing four multivariate logistic regression models that respectively incorporated either the presence of AKI, the highest AKI stage, the duration of AKI, or the AKI burden., Results: In the FINNAKI cohort 1096 of 2809 patients (39%) had AKI and 90-day mortality of the cohort was 23%. Median AKI burden was 0.17 (IQR 0.07-0.50), 1.0 being the maximum. The model including AKI burden (area under the receiver operator curve (AUROC) 0.78, 0.76-0.80) outperformed the models using AKI presence (AUROC 0.77, 0.75-0.79, p = 0.026) or AKI severity (AUROC 0.77, 0.75-0.79, p = 0.012), but not AKI duration (AUROC 0.77, 0.75-0.79, p = 0.06). In the SICS-I, 603 of 1075 patients (56%) had AKI and 90-day mortality was 28%. Median AKI burden was 0.19 (IQR 0.08-0.46). The model using AKI burden performed better (AUROC 0.77, 0.74-0.80) than the models using AKI presence (AUROC 0.75, 0.71-0.78, p = 0.001), AKI severity (AUROC 0.76, 0.72-0.79. p = 0.008) or AKI duration (AUROC 0.76, 0.73-0.79, p = 0.009)., Conclusion: AKI burden, which appreciates both severity and duration of AKI, was superior to using only presence or the highest stage of AKI in predicting 90-day mortality. Using AKI burden or other more granular methods may be helpful in future epidemiological studies of AKI.

Objectives: Caregivers use clinical examination to timely recognize deterioration of a patient, yet data on the prognostic value of clinical examination are inconsistent. In the Simple Intensive Care Studies-I, we evaluated the association of clinical examination findings with 90-day mortality in critically ill patients., Design: Prospective single-center cohort study., Setting: ICU of a single tertiary care level hospital between March 27, 2015, and July 22, 2017., Patients: All consecutive adults acutely admitted to the ICU and expected to stay for at least 24 hours., Interventions: A protocolized clinical examination of 19 clinical signs conducted within 24 hours of admission., Measurements Main Results: Independent predictors of 90-day mortality were identified using multivariable logistic regression analyses. Model performance was compared with established prognostic risk scores using area under the receiver operating characteristic curves. Robustness of our findings was tested by internal bootstrap validation and adjustment of the threshold for statistical significance. A total of 1,075 patients were included, of whom 298 patients (28%) had died at 90-day follow-up. Multivariable analyses adjusted for age and norepinephrine infusion rate demonstrated that the combination of higher respiratory rate, higher systolic blood pressure, lower central temperature, altered consciousness, and decreased urine output was independently associated with 90-day mortality (area under the receiver operating characteristic curves = 0.74; 95% CI, 0.71-0.78). Clinical examination had a similar discriminative value as compared with the Simplified Acute Physiology Score-II (area under the receiver operating characteristic curves = 0.76; 95% CI, 0.73-0.79; p = 0.29) and Acute Physiology and Chronic Health Evaluation-IV (using area under the receiver operating characteristic curves = 0.77; 95% CI, 0.74-0.80; p = 0.16) and was significantly better than the Sequential Organ Failure Assessment (using area under the receiver operating characteristic curves = 0.67; 95% CI, 0.64-0.71; p < 0.001)., Conclusions: Clinical examination has reasonable discriminative value for assessing 90-day mortality in acutely admitted ICU patients. In our study population, a single, protocolized clinical examination had similar prognostic abilities compared with the Simplified Acute Physiology Score-II and Acute Physiology and Chronic Health Evaluation-IV and outperformed the Sequential Organ Failure Assessment score.

Background: Dopamine has been used in patients with cardiac dysfunction for more than five decades. Yet, no systematic review has assessed the effects of dopamine in critically ill patients with cardiac dysfunction., Methods: This systematic review was conducted following The Cochrane Handbook for Systematic Reviews of Interventions. We searched for trials including patients with observed cardiac dysfunction published until 19 April 2018. Risk of bias was evaluated and Trial Sequential Analyses were conducted. The primary outcome was all-cause mortality at longest follow-up. Secondary outcomes were serious adverse events, myocardial infarction, arrhythmias, and renal replacement therapy. We used GRADE to assess the certainty of the evidence., Results: We identified 17 trials randomising 1218 participants. All trials were at high risk of bias and only one trial used placebo. Dopamine compared with any control treatment was not significantly associated with relative risk of mortality (60/457 [13%] vs 90/581 [15%]; RR 0.91; 95% confidence interval 0.68-1.21) or any other patient-centred outcomes. Trial Sequential Analyses of all outcomes showed that there was insufficient information to confirm or reject our anticipated intervention effects. There were also no statistically significant associations for any of the outcomes in subgroup analyses by type of comparator (inactive compared to potentially active), dopamine dose (low compared to moderate dose), or setting (cardiac surgery compared to heart failure)., Conclusion: Evidence for dopamine in critically ill patients with cardiac dysfunction is sparse, of low quality, and inconclusive. The use of dopamine for cardiac dysfunction can neither be recommended nor refuted., (© 2018 The Authors. Acta Anaesthesiologica Scandinavica published by John Wiley & Sons Ltd on behalf of Acta Anaesthesiologica Scandinavica Foundation.)

Purpose: Clinical examination is often the first step to diagnose shock and estimate cardiac index. In the Simple Intensive Care Studies-I, we assessed the association and diagnostic performance of clinical signs for estimation of cardiac index in critically ill patients., Methods: In this prospective, single-centre cohort study, we included all acutely ill patients admitted to the ICU and expected to stay > 24 h. We conducted a protocolised clinical examination of 19 clinical signs followed by critical care ultrasonography for cardiac index measurement. Clinical signs were associated with cardiac index and a low cardiac index (< 2.2 L min -1  m 2 ) in multivariable analyses. Diagnostic test accuracies were also assessed., Results: We included 1075 patients, of whom 783 (73%) had a validated cardiac index measurement. In multivariable regression, respiratory rate, heart rate and rhythm, systolic and diastolic blood pressure, central-to-peripheral temperature difference, and capillary refill time were statistically independently associated with cardiac index, with an overall R 2 of 0.30 (98.5% CI 0.25-0.35). A low cardiac index was observed in 280 (36%) patients. Sensitivities and positive and negative predictive values were below 90% for all signs. Specificities above 90% were observed only for 110/280 patients, who had atrial fibrillation, systolic blood pressures < 90 mmHg, altered consciousness, capillary refill times > 4.5 s, or skin mottling over the knee., Conclusions: Seven out of 19 clinical examination findings were independently associated with cardiac index. For estimation of cardiac index, clinical examination was found to be insufficient in multivariable analyses and in diagnostic accuracy tests. Additional measurements such as critical care ultrasonography remain necessary.

Background: Prophylaxis for gastrointestinal stress ulceration is frequently given to patients in the intensive care unit (ICU), but its risks and benefits are unclear., Methods: In this European, multicenter, parallel-group, blinded trial, we randomly assigned adults who had been admitted to the ICU for an acute condition (i.e., an unplanned admission) and who were at risk for gastrointestinal bleeding to receive 40 mg of intravenous pantoprazole (a proton-pump inhibitor) or placebo daily during the ICU stay. The primary outcome was death by 90 days after randomization., Results: A total of 3298 patients were enrolled; 1645 were randomly assigned to the pantoprazole group and 1653 to the placebo group. Data on the primary outcome were available for 3282 patients (99.5%). At 90 days, 510 patients (31.1%) in the pantoprazole group and 499 (30.4%) in the placebo group had died (relative risk, 1.02; 95% confidence interval [CI], 0.91 to 1.13; P=0.76). During the ICU stay, at least one clinically important event (a composite of clinically important gastrointestinal bleeding, pneumonia, Clostridium difficile infection, or myocardial ischemia) had occurred in 21.9% of patients assigned to pantoprazole and 22.6% of those assigned to placebo (relative risk, 0.96; 95% CI, 0.83 to 1.11). In the pantoprazole group, 2.5% of patients had clinically important gastrointestinal bleeding, as compared with 4.2% in the placebo group. The number of patients with infections or serious adverse reactions and the percentage of days alive without life support within 90 days were similar in the two groups., Conclusions: Among adult patients in the ICU who were at risk for gastrointestinal bleeding, mortality at 90 days and the number of clinically important events were similar in those assigned to pantoprazole and those assigned to placebo. (Funded by Innovation Fund Denmark and others; SUP-ICU ClinicalTrials.gov number, NCT02467621 .).

Purpose: In the Simple Intensive Care Studies-I (SICS-I), we aim to unravel the value of clinical and haemodynamic variables obtained by physical examination and critical care ultrasound (CCUS) that currently guide daily practice in critically ill patients. We intend to (1) measure all available clinical and haemodynamic variables, (2) train novices in obtaining values for advanced variables based on CCUS in the intensive care unit (ICU) and (3) create an infrastructure for a registry with the flexibility of temporarily incorporating specific (haemodynamic) research questions and variables. The overall purpose is to investigate the diagnostic and prognostic value of clinical and haemodynamic variables., Participants: The SICS-I includes all patients acutely admitted to the ICU of a tertiary teaching hospital in the Netherlands with an ICU stay expected to last beyond 24 hours. Inclusion started on 27 March 2015., Findings to Date: On 31 December 2016, 791 eligible patients fulfilled our inclusion criteria of whom 704 were included. So far 11 substudies with additional variables have been designed, of which six were feasible to implement in the basic study, and two are planned and awaiting initiation. All researchers received focused training for obtaining specific CCUS images. An independent Core laboratory judged that 632 patients had CCUS images of sufficient quality., Future Plans: We intend to optimise the set of variables for assessment of the haemodynamic status of the critically ill patient used for guiding diagnostics, prognosis and interventions. Repeated evaluations of these sets of variables are needed for continuous improvement of the diagnostic and prognostic models. Future plans include: (1) more advanced imaging; (2) repeated clinical and haemodynamic measurements; (3) expansion of the registry to other departments or centres; and (4) exploring possibilities of integration of a randomised clinical trial superimposed on the registry., Study Registration Number: NCT02912624; Pre-results., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)