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Study Objectives: To assess short-term and long-term complications of bronchoscopy-guided, percutaneous dilatational tracheostomy (PDT) and surgical tracheostomy (ST) and to report a complication of PDT that has not been described previously.Design: Prospective survey.Setting: University teaching hospital.Patients: Two hundred eleven critically ill patients in our ICU.Interventions: PDT was performed in 174 patients, under bronchoscopic guidance in most cases. ST was performed in 40 patients.Results: No procedure-related fatalities occurred during PDT or ST. The incidence of significant complications (eg, procedure-related transfusion of fresh-frozen plasma, RBCs, or platelets, malpositioning or kinking of the tracheal cannula, deterioration of respiratory parameters lasting for > 36 h following the procedure, or stomal infection) in patients undergoing PDT was 4.0% overall and 3.0% when bronchoscopic guidance was used. No cases of paratracheal insertion, pneumothorax, pneumomediastinum, tracheal laceration, or clinically significant tracheal stenosis occurred in patients undergoing PDT. We attribute this low rate of complications to procedural and organizational factors such as bronchoscopic guidance, performance by or supervision of all PDTs by physicians with extensive experience in this procedure, and airway management by physicians who were well-versed in (difficult) airway management. In addition, an ear-nose-throat surgeon participated in the procedure in case conversion of the procedure to an ST should become necessary. We observed a complication that, to our knowledge, has not been reported previously. Five patients developed intermittent respiratory difficulties 2 to 21 days (mean, 8 days) after undergoing PDT. The cause turned out to be the periodic obstruction of the tracheal cannula by hematoma and the swelling of the posterior tracheal wall, which had been caused by intermittent pressure and chafing of the cannula on the tracheal wall. In between the episodes of obstruction, the cannula was open and functioning normally, which made the diagnosis difficult to establish.Conclusions: Bronchoscopy-assisted PDT is a safe and effective procedure when performed by a team of experienced physicians under controlled circumstances. The intermittent obstruction of the cannula caused by swelling and irritation of the posterior tracheal wall should be considered in patients who develop unexplained paroxysmal respiratory problems some time after undergoing PDT or ST. [ABSTRACT FROM AUTHOR]

Purpose: During the initial phase of the pandemic, healthcare professionals faced difficulties due to the limited availability of comprehensive learning resources on managing patients affected with coronavirus disease 2019 (COVID-19). The COVID-19 Skills Preparation Course (C19&#95;SPACE) was tailored to meet the overwhelming demand for specialized training. The primary objective of this study was to assess the efficacy and impact of this program on enhancing clinical knowledge and to identify factors affecting this improvement., Methods: As part of the project, data were collected prospectively to measure the baseline knowledge. After the descriptive statistics, multiple and multivariate logistic regression models were executed to identify the factors associated with knowledge increase., Results: The final sample included 3140 medical doctors (MDs) and 3090 nurses (RNs). For the primary analysis, the mean value of the baseline knowledge test score of MDs was 62.41 (standard deviation, SD = 13.48), and it significantly (p < 0.001) increased to 84.65 (SD = 11.95). Factors influencing overall knowledge scores were female sex (AOR = 1.34 [1.04-1.73]), being a specialist qualified for intensive care medicine (adjusted odds ratio, AOR = 0.56, [0.33-0.96]), and performance on the pre-test (AOR = 0.91, [0.90-0.92]). As for the RNs, the mean value of the total knowledge score was 63.25 (SD = 13.53), which significantly (p < 0.001) increased to 81.51 (SD = 14.21). Factor associated with knowledge was performance on the pre-test (AOR = 0.92 [0.92-0.93])., Conclusions: C19&#95;SPACE effectively increased the clinical knowledge of doctors and nurses. The effect was more pronounced in the program's target group of healthcare workers with less experience in the intensive care unit (ICU). Other factors associated with knowledge enhancement were sex and being a specialist in intensive care., (© 2024. Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Incidence of central venous catheter (CVC)-related thrombosis in critically ill patients remains ambiguous and its association with potential hazardous sequelae unknown. The primary aim of the study was to evaluate the epidemiology of CVC-related thrombosis; secondary aims were to assess the association of catheter-related thrombosis with catheter-related infection, pulmonary embolism and mortality., Methods: This was a single-center, prospective observational study conducted at a tertiary intensive care unit (ICU) in the Netherlands. The study population consisted of CVC placements in adult ICU patients with a minimal indwelling time of 48 h. CVC-related thrombosis was diagnosed with ultrasonography. Primary outcomes were prevalence and incidence, incidence was reported as the number of cases per 1000 indwelling days., Results: 173 CVCs in 147 patients were included. Median age of patients was 64.0 [IQR: 52.0, 72.0] and 71.1 % were male. Prevalence of thrombosis was 0.56 (95 % CI: 0.49, 0.63) and incidence per 1000 indwelling days was 65.7 (95 % CI: 59.0, 72.3). No association with catheter-related infection was found (p = 0.566). There was a significant association with pulmonary embolism (p = 0.022). All 173 CVCs were included in the survival analysis. Catheter-related thrombosis was associated with a lower 28-day mortality risk (hazard ratio: 0.39, 95 % CI: 0.17, 0.87)., Conclusion: In critically ill patients, prevalence and incidence of catheter-related thrombosis were high. Catheter-related thrombosis was not associated with catheter-related infections, but was associated with pulmonary embolism and a decreased mortality risk., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Benchmarking intensive care units for audit and feedback is frequently based on comparing actual mortality versus predicted mortality. Traditionally, mortality prediction models rely on a limited number of input variables and significant manual data entry and curation. Using automatically extracted electronic health record data may be a promising alternative. However, adequate data on comparative performance between these approaches is currently lacking., Methods: The AmsterdamUMCdb intensive care database was used to construct a baseline APACHE IV in-hospital mortality model based on data typically available through manual data curation. Subsequently, new in-hospital mortality models were systematically developed and evaluated. New models differed with respect to the extent of automatic variable extraction, classification method, recalibration usage and the size of collection window., Results: A total of 13 models were developed based on data from 5,077 admissions divided into a train (80%) and test (20%) cohort. Adding variables or extending collection windows only marginally improved discrimination and calibration. An XGBoost model using only automatically extracted variables, and therefore no acute or chronic diagnoses, was the best performing automated model with an AUC of 0.89 and a Brier score of 0.10., Discussion: Performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data is similar. Importantly, our results suggest that variables typically requiring manual curation, such as diagnosis at admission and comorbidities, may not be necessary for accurate mortality prediction. These proof-of-concept results require replication using multi-centre data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Patients receiving mechanical ventilation in the intensive care unit (ICU) frequently develop contractile weakness of the diaphragm. Consequently, they may experience difficulty weaning from mechanical ventilation, which increases mortality and poses a high economic burden. Because of a lack of knowledge regarding the molecular changes in the diaphragm, no treatment is currently available to improve diaphragm contractility. We compared diaphragm biopsies from ventilated ICU patients ( N = 54) to those of non-ICU patients undergoing thoracic surgery ( N = 27). By integrating data from myofiber force measurements, x-ray diffraction experiments, and biochemical assays with clinical data, we found that in myofibers isolated from the diaphragm of ventilated ICU patients, myosin is trapped in an energy-sparing, super-relaxed state, which impairs the binding of myosin to actin during diaphragm contraction. Studies on quadriceps biopsies of ICU patients and on the diaphragm of previously healthy mechanically ventilated rats suggested that the super-relaxed myosins are specific to the diaphragm and not a result of critical illness. Exposing slow- and fast-twitch myofibers isolated from the diaphragm biopsies to small-molecule compounds activating troponin restored contractile force in vitro. These findings support the continued development of drugs that target sarcomere proteins to increase the calcium sensitivity of myofibers for the treatment of ICU-acquired diaphragm weakness.

When making critical treatment decisions, shared decision-making (SDM) between healthcare providers and patients is essential. SDM involves discussing care options, considering patient preferences, and ensuring decisions align with patient values and medical conditions. This process becomes challenging in life-threatening emergencies, where time constraints hinder thorough discussions and coordination among healthcare providers, potentially leading to inappropriate care. Two cases highlight these challenges. Patient A, a 76-year-old man with acute aortic dissection, underwent surgery without comprehensive SDM, resulting in unsuccessful outcomes and questioning the appropriateness of the intervention. Patient B, an 84-year-old man with heart failure and COPD, received palliative care following thorough SDM and multidisciplinary consultation, leading to a dignified end-of-life experience. We conclude that effective communication and multidisciplinary collaboration are crucial for SDM, even in acute settings. Recommendations include creating space for thorough discussions, involving all relevant healthcare providers, and integrating palliative care as a serious treatment option. This approach ensures patient-centered care and aligns medical interventions with the patient's values and needs.

Background: Reinforcement learning (RL) holds great promise for intensive care medicine given the abundant availability of data and frequent sequential decision-making. But despite the emergence of promising algorithms, RL driven bedside clinical decision support is still far from reality. Major challenges include trust and safety. To help address these issues, we introduce cross off-policy evaluation and policy restriction and show how detailed policy analysis may increase clinical interpretability. As an example, we apply these in the setting of RL to optimise ventilator settings in intubated covid-19 patients., Methods: With data from the Dutch ICU Data Warehouse and using an exhaustive hyperparameter grid search, we identified an optimal set of Dueling Double-Deep Q Network RL models. The state space comprised ventilator, medication, and clinical data. The action space focused on positive end-expiratory pressure (peep) and fraction of inspired oxygen (FiO2) concentration. We used gas exchange indices as interim rewards, and mortality and state duration as final rewards. We designed a novel evaluation method called cross off-policy evaluation (OPE) to assess the efficacy of models under varying weightings between the interim and terminal reward components. In addition, we implemented policy restriction to prevent potentially hazardous model actions. We introduce delta-Q to compare physician versus policy action quality and in-depth policy inspection using visualisations., Results: We created trajectories for 1118 intensive care unit (ICU) admissions and trained 69,120 models using 8 model architectures with 128 hyperparameter combinations. For each model, policy restrictions were applied. In the first evaluation step, 17,182/138,240 policies had good performance, but cross-OPE revealed suboptimal performance for 44% of those by varying the reward function used for evaluation. Clinical policy inspection facilitated assessment of action decisions for individual patients, including identification of action space regions that may benefit most from optimisation., Conclusion: Cross-OPE can serve as a robust evaluation framework for safe RL model implementation by identifying policies with good generalisability. Policy restriction helps prevent potentially unsafe model recommendations. Finally, the novel delta-Q metric can be used to operationalise RL models in clinical practice. Our findings offer a promising pathway towards application of RL in intensive care medicine and beyond., (© 2024. The Author(s).)

Objective: Reinforcement learning (RL) is a machine learning technique uniquely effective at sequential decision-making, which makes it potentially relevant to ICU treatment challenges. We set out to systematically review, assess level-of-readiness and meta-analyze the effect of RL on outcomes for critically ill patients., Data Sources: A systematic search was performed in PubMed, Embase.com, Clarivate Analytics/Web of Science Core Collection, Elsevier/SCOPUS and the Institute of Electrical and Electronics Engineers Xplore Digital Library from inception to March 25, 2022, with subsequent citation tracking., Data Extraction: Journal articles that used an RL technique in an ICU population and reported on patient health-related outcomes were included for full analysis. Conference papers were included for level-of-readiness assessment only. Descriptive statistics, characteristics of the models, outcome compared with clinician's policy and level-of-readiness were collected. RL-health risk of bias and applicability assessment was performed., Data Synthesis: A total of 1,033 articles were screened, of which 18 journal articles and 18 conference papers, were included. Thirty of those were prototyping or modeling articles and six were validation articles. All articles reported RL algorithms to outperform clinical decision-making by ICU professionals, but only in retrospective data. The modeling techniques for the state-space, action-space, reward function, RL model training, and evaluation varied widely. The risk of bias was high in all articles, mainly due to the evaluation procedure., Conclusion: In this first systematic review on the application of RL in intensive care medicine we found no studies that demonstrated improved patient outcomes from RL-based technologies. All studies reported that RL-agent policies outperformed clinician policies, but such assessments were all based on retrospective off-policy evaluation., Competing Interests: Dr. Dam’s institution received funding from ZonMW/Netherlands Organization for Health Research and Development (10430012010003); he received funding from Pacmed BV. Dr. Hengst received funding from ING Bank N.V. Dr. Hoogendoorn disclosed co-ownership of PersonalAIze B.V. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Micronutrient deficiencies can develop in critically ill patients, arising from factors such as decreased intake, increased losses, drug interactions, and hypermetabolism. These deficiencies may compromise important immune functions, with potential implications for patient outcomes. Alternatively, micronutrient blood levels may become low due to inflammation-driven redistribution rather than consumption. This explorative pilot study investigates blood micronutrient concentrations during the first three weeks of ICU stay in critically ill COVID-19 patients and evaluates the impact of additional micronutrient administration. Moreover, associations between inflammation, disease severity, and micronutrient status were explored. We measured weekly concentrations of vitamins A, B6, D, and E; iron; zinc; copper; selenium; and CRP as a marker of inflammation state and the SOFA score indicating disease severity in 20 critically ill COVID-19 patients during three weeks of ICU stay. Half of the patients received additional (intravenous) micronutrient administration. Data were analyzed with linear mixed models and Pearson's correlation coefficient. High deficiency rates of vitamins A, B6, and D; zinc; and selenium (50-100%) were found at ICU admission, along with low iron status. After three weeks, vitamins B6 and D deficiencies persisted, and iron status remained low. Plasma levels of vitamins A and E, zinc, and selenium improved. No significant differences in micronutrient levels were found between patient groups. Negative correlations were identified between the CRP level and levels of vitamins A and E, iron, transferrin, zinc, and selenium. SOFA scores negatively correlated with vitamin D and selenium levels. Our findings reveal high micronutrient deficiency rates at ICU admission. Additional micronutrient administration did not enhance levels or expedite their increase. Spontaneous increases in vitamins A and E, zinc, and selenium levels were associated with inflammation resolution, suggesting that observed low levels may be attributed, at least in part, to redistribution rather than true deficiencies.

Background: Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave., Methods: This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs., Results: Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI - 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI - 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates., Conclusions: Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)., (© 2023. The Author(s).)

Introduction: With the advent of artificial intelligence, the secondary use of routinely collected medical data from electronic healthcare records (EHR) has become increasingly popular. However, different EHR systems typically use different names for the same medical concepts. This obviously hampers scalable model development and subsequent clinical implementation for decision support. Therefore, converting original parameter names to a so-called ontology, a standardized set of predefined concepts, is necessary but time-consuming and labor-intensive. We therefore propose an augmented intelligence approach to facilitate ontology alignment by predicting correct concepts based on parameter names from raw electronic health record data exports., Methods: We used the manually mapped parameter names from the multicenter "Dutch ICU data warehouse against COVID-19" sourced from three types of EHR systems to train machine learning models for concept mapping. Data from 29 intensive care units on 38,824 parameters mapped to 1,679 relevant and unique concepts and 38,069 parameters labeled as irrelevant were used for model development and validation. We used the Natural Language Toolkit (NLTK) to preprocess the parameter names based on WordNet cognitive synonyms transformed by term-frequency inverse document frequency (TF-IDF), yielding numeric features. We then trained linear classifiers using stochastic gradient descent for multi-class prediction. Finally, we fine-tuned these predictions using information on distributions of the data associated with each parameter name through similarity score and skewness comparisons., Results: The initial model, trained using data from one hospital organization for each of three EHR systems, scored an overall top 1 precision of 0.744, recall of 0.771, and F1-score of 0.737 on a total of 58,804 parameters. Leave-one-hospital-out analysis returned an average top 1 recall of 0.680 for relevant parameters, which increased to 0.905 for the top 5 predictions. When reducing the training dataset to only include relevant parameters, top 1 recall was 0.811 and top 5 recall was 0.914 for relevant parameters. Performance improvement based on similarity score or skewness comparisons affected at most 5.23% of numeric parameters., Conclusion: Augmented intelligence is a promising method to improve concept mapping of parameter names from raw electronic health record data exports. We propose a robust method for mapping data across various domains, facilitating the integration of diverse data sources. However, recall is not perfect, and therefore manual validation of mapping remains essential., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19., Methods: We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked., Results: The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype., Conclusions: Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.

Background: Monitoring and controlling lung stress and diaphragm effort has been hypothesized to limit lung injury and diaphragm injury. The occluded inspiratory airway pressure (Pocc) and the airway occlusion pressure at 100 ms (P0.1) have been used as noninvasive methods to assess lung stress and respiratory muscle effort, but comparative performance of these measures and their correlation to diaphragm effort is unknown. The authors hypothesized that Pocc and P0.1 correlate with diaphragm effort and lung stress and would have strong discriminative performance in identifying extremes of lung stress and diaphragm effort., Methods: Change in transdiaphragmatic pressure and transpulmonary pressure was obtained with double-balloon nasogastric catheters in critically ill patients (n = 38). Pocc and P0.1 were measured every 1 to 3 h. Correlations between Pocc and P0.1 with change in transdiaphragmatic pressure and transpulmonary pressure were computed from patients from the first cohort. Accuracy of Pocc and P0.1 to identify patients with extremes of lung stress (change in transpulmonary pressure > 20 cm H2O) and diaphragm effort (change in transdiaphragmatic pressure < 3 cm H2O and >12 cm H2O) in the preceding hour was assessed with area under receiver operating characteristic curves. Cutoffs were validated in patients from the second cohort (n = 13)., Results: Pocc and P0.1 correlate with change in transpulmonary pressure (R2 = 0.62 and 0.51, respectively) and change in transdiaphragmatic pressure (R2 = 0.53 and 0.22, respectively). Area under receiver operating characteristic curves to detect high lung stress is 0.90 (0.86 to 0.94) for Pocc and 0.88 (0.84 to 0.92) for P0.1. Area under receiver operating characteristic curves to detect low diaphragm effort is 0.97 (0.87 to 1.00) for Pocc and 0.93 (0.81 to 0.99) for P0.1. Area under receiver operating characteristic curves to detect high diaphragm effort is 0.86 (0.81 to 0.91) for Pocc and 0.73 (0.66 to 0.79) for P0.1. Performance was similar in the external dataset., Conclusions: Pocc and P0.1 correlate with lung stress and diaphragm effort in the preceding hour. Diagnostic performance of Pocc and P0.1 to detect extremes in these parameters is reasonable to excellent. Pocc is more accurate in detecting high diaphragm effort., (Copyright © 2023, the American Society of Anesthesiologists. All Rights Reserved.)

Background: Aneurysmal subarachnoid hemorrhage (aSAH) is an important indication for intensive care unit admission and may lead to significant morbidity and mortality. We assessed the ability of C-terminal proarginine vasopressin (CT-proAVP) to predict disease outcome, mortality, and delayed cerebral ischemia (DCI) in critically ill patients with aSAH compared with the World Federation of Neurological Surgeons (WFNS) score and Acute Physiological and Chronic Health Evaluation IV (APACHE IV) model., Methods: C-terminal proarginine vasopressin was collected on admission in this single-center, prospective, observational cohort study. The primary aim was to investigate the relationship between CT-proAVP and poor functional outcome at 1 year (Glasgow Outcome Scale score 1-3) in a multivariable logistic regression model adjusted for WFNS and APACHE IV scores. Secondary aims were mortality and DCI. The multivariable logistic regression model for DCI was also adjusted for the modified Fisher scale., Results: In 100 patients, the median CT-proAVP level was 24.9 pmol/L (interquartile range 11.5-53.8); 45 patients had a poor 1-year functional outcome, 19 patients died within 30 days, 25 patients died within 1 year, and DCI occurred in 28 patients. Receiver operating characteristics curves revealed high accuracy for CT-proAVP to identify patients with poor 1-year functional outcome (area under the curve [AUC] 0.84, 95% confidence interval [CI] 0.77-0.92, p < 0.001), 30-day mortality (AUC 0.84, 95% CI 0.76-0.93, p < 0.001), and 1-year mortality (AUC 0.79, 95% CI 0.69-0.89, p < 0.001). CT-proAVP had a low AUC for identifying patients with DCI (AUC 0.67, 95% CI 0.55-0.79, p 0.008). CT-proAVP ≥ 24.9 pmo/L proved to be a significant predictor for poor 1-year functional outcome (odds ratio [OR] 8.04, 95% CI 2.97-21.75, p < 0.001), and CT-proAVP ≥ 29.1 pmol/L and ≥ 27.7 pmol/L were significant predictors for 30-day and 1-year mortality (OR 9.31, 95% CI 1.55-56.07, p 0.015 and OR 5.15, 95% CI 1.48-17.93, p 0.010) in multivariable models with WFNS and APACHE IV scores. CT-proAVP ≥ 29.5 pmol/L was not a significant predictor for DCI in a multivariable model adjusted for the modified Fisher scale (p = 0.061)., Conclusions: C-terminal proarginine vasopressin was able to predict poor functional outcome and mortality in critically ill patients with aSAH. Its prognostic ability to predict DCI was low., Trial Registration: Nederlands Trial Register: NTR4118., (© 2022. The Author(s).)

Abstract: Background: Aims of this study were to investigate the prevalence and incidence of catheter-related infection, identify risk factors, and determine the relation of catheter-related infection with mortality in critically ill COVID-19 patients. Methods: This was a retrospective cohort study of central venous catheters (CVCs) in critically ill COVID-19 patients. Eligible CVC insertions required an indwelling time of at least 48 hours and were identified using a full-admission electronic health record database. Risk factors were identified using logistic regression. Differences in survival rates at day 28 of follow-up were assessed using a log-rank test and proportional hazard model. Results: In 538 patients, a total of 914 CVCs were included. Prevalence and incidence of suspected catheter-related infection were 7.9% and 9.4 infections per 1,000 catheter indwelling days, respectively. Prone ventilation for more than 5 days was associated with increased risk of suspected catheter-related infection; odds ratio, 5.05 (95% confidence interval 2.12-11.0). Risk of death was significantly higher in patients with suspected catheter-related infection (hazard ratio, 1.78; 95% confidence interval, 1.25-2.53). Conclusions: This study shows that in critically ill patients with COVID-19, prevalence and incidence of suspected catheter-related infection are high, prone ventilation is a risk factor, and mortality is higher in case of catheter-related infection., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 by the Shock Society.)

Objectives: To determine the diagnostic accuracy of lung ultrasound signs for both the diagnosis of interstitial syndrome and for the discrimination of noncardiogenic interstitial syndrome (NCIS) from cardiogenic pulmonary edema (CPE) in a mixed ICU population., Design: A prospective diagnostic accuracy study with derivation and validation cohorts., Setting: Three academic mixed ICUs in the Netherlands., Patients: Consecutive adult ICU patients that received a lung ultrasound examination., Interventions: None., Measurements and Main Result: The reference standard was the diagnosis of interstitial syndrome (NCIS or CPE) or noninterstitial syndromes (other pulmonary diagnoses and no pulmonary diagnoses) based on full post-hoc clinical chart review except lung ultrasound. The index test was a lung ultrasound examination performed and scored by a researcher blinded to clinical information. A total of 101 patients were included in the derivation and 122 in validation cohort. In the derivation cohort, patients with interstitial syndrome ( n = 56) were reliably discriminated from other patients based on the presence of a B-pattern (defined as greater than or equal to 3 B-lines in one frame) with an accuracy of 94.7% (sensitivity, 90.9%; specificity, 91.1%). For discrimination of NCIS ( n = 29) from CPE ( n = 27), the presence of bilateral pleural line abnormalities (at least two: fragmented, thickened or irregular) had the highest diagnostic accuracy (94.6%; sensitivity, 89.3%; specificity, 100%). A diagnostic algorithm (Bedside Lung Ultrasound for Interstitial Syndrome Hierarchy protocol) using B-pattern and bilateral pleural abnormalities had an accuracy of 0.86 (95% CI, 0.77-0.95) for diagnosis and discrimination of interstitial syndromes. In the validation cohort, which included 122 patients with interstitial syndrome, bilateral pleural line abnormalities discriminated NCIS ( n = 98) from CPE ( n = 24) with a sensitivity of 31% (95% CI, 21-40%) and a specificity of 100% (95% CI, 86-100%)., Conclusions: Lung ultrasound can diagnose and discriminate interstitial syndromes in ICU patients with moderate-to-good accuracy. Pleural line abnormalities are highly specific for NCIS, but sensitivity is limited., Competing Interests: Dr. Tuinman received support for article research from departmental funds. 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.)

Background: For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources., Methods: From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO 2 /FiO 2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO 2 /FiO 2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking., Results: The median duration of prone episodes was 17 h (11-20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO 2 /FiO 2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode., Conclusions: In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning., (© 2022. The Author(s).)

Background/objectives: Patients affected by obesity and Coronavirus disease 2019, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), appear to have a higher risk for intensive care (ICU) admission. A state of low-grade chronic inflammation in obesity has been suggested as one of the underlying mechanisms. We investigated whether obesity is associated with differences in new inflammatory biomarkers mid-regional proadrenomedullin (MR-proADM), C-terminal proendothelin-1 (CT-proET-1), and clinical outcomes in critically ill patients with SARS-CoV-2 pneumonia., Subjects/methods: A total of 105 critically ill patients with SARS-CoV-2 pneumonia were divided in patients with obesity (body mass index (BMI) ≥ 30 kg/m 2 , n = 42) and patients without obesity (BMI < 30 kg/m 2 , n = 63) and studied in a retrospective observational cohort study. MR-proADM, CT-proET-1 concentrations, and conventional markers of white blood count (WBC), C-reactive protein (CRP), and procalcitonin (PCT) were collected during the first 7 days., Results: BMI was 33.5 (32-36.1) and 26.2 (24.7-27.8) kg/m 2 in the group with and without obesity. There were no significant differences in concentrations MR-proADM, CT-proET-1, WBC, CRP, and PCT at baseline and the next 6 days between patients with and without obesity. Only MR-proADM changed significantly over time (p = 0.039). Also, BMI did not correlate with inflammatory biomarkers (MR-proADM rho = 0.150, p = 0.125, CT-proET-1 rho = 0.179, p = 0.067, WBC rho = -0.044, p = 0.654, CRP rho = 0.057, p = 0.564, PCT rho = 0.022, p = 0.842). Finally, no significant differences in time on a ventilator, ICU length of stay, and 28-day mortality between patients with or without obesity were observed., Conclusions: In critically ill patients with confirmed SARS-CoV-2 pneumonia, obesity was not associated with differences in MR-proADM, and CT-proET-1, or impaired outcome., Trial Registration: Netherlands Trial Register, NL8460., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Adequate antibiotic dosing may improve outcomes in critically ill patients but is challenging due to altered and variable pharmacokinetics. To address this challenge, AutoKinetics was developed, a decision support system for bedside, real-time, data-driven and personalised antibiotic dosing. This study evaluates the feasibility, safety and efficacy of its clinical implementation., Methods: In this two-centre randomised clinical trial, critically ill patients with sepsis or septic shock were randomised to AutoKinetics dosing or standard dosing for four antibiotics: vancomycin, ciprofloxacin, meropenem, and ceftriaxone. Adult patients with a confirmed or suspected infection and either lactate > 2 mmol/L or vasopressor requirement were eligible for inclusion. The primary outcome was pharmacokinetic target attainment in the first 24 h after randomisation. Clinical endpoints included mortality, ICU length of stay and incidence of acute kidney injury., Results: After inclusion of 252 patients, the study was stopped early due to the COVID-19 pandemic. In the ciprofloxacin intervention group, the primary outcome was obtained in 69% compared to 3% in the control group (OR 62.5, CI 11.4-1173.78, p < 0.001). Furthermore, target attainment was faster (26 h, CI 18-42 h, p < 0.001) and better (65% increase, CI 49-84%, p < 0.001). For the other antibiotics, AutoKinetics dosing did not improve target attainment. Clinical endpoints were not significantly different. Importantly, higher dosing did not lead to increased mortality or renal failure., Conclusions: In critically ill patients, personalised dosing was feasible, safe and significantly improved target attainment for ciprofloxacin., Trial Registration: The trial was prospectively registered at Netherlands Trial Register (NTR), NL6501/NTR6689 on 25 August 2017 and at the European Clinical Trials Database (EudraCT), 2017-002478-37 on 6 November 2017., (© 2022. The Author(s).)

Background: Findings on the association between early high protein provision and mortality in ICU patients are inconsistent. The relation between early high protein provision and mortality in patients receiving CRRT remains unclear. The aim was to study the association between early high protein provision and hospital and ICU mortality and consistency in subgroups., Methods: A retrospective cohort study was conducted in 2618 ICU patients with a feeding tube and mechanically ventilated ≥48 h (2003-2016). The association between early high protein provision (≥1.2 g/kg/day at day 4 vs. <1.2 g/kg/day) and hospital and ICU mortality was assessed for the total group, for patients receiving CRRT, and for non-septic and septic patients, by Cox proportional hazards analysis. Adjustments were made for APACHE II score, energy provision, BMI, and age., Results: Mean protein provision at day 4 was 0.96 ± 0.48 g/kg/day. A significant association between early high protein provision and lower hospital mortality was found in the total group (HR 0.48, 95% CI 0.39-0.60, p = <0.001), CRRT-receiving patients (HR 0.62, 95% CI 0.39-0.99, p = 0.045) and non-septic patients (HR 0.56, 95% CI 0.44-0.71, p = <0.001). However, no association was found in septic patients (HR 0.71, 95% CI 0.39-1.29, p = 0.264). These associations were very similar for ICU mortality. In a sensitivity analysis for patients receiving a relative energy provision >50%, results remained robust in all groups except for patients receiving CRRT., Conclusions: Early high protein provision is associated with lower hospital and ICU mortality in ICU patients, including CRRT-receiving patients. There was no association for septic patients., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Ultrasonography of the diaphragm in the zone of apposition has become increasingly popular to evaluate muscle thickness and thickening fraction. However, measurements in this anatomical location are frequently hindered by factors that constrain physical accessibility or that alter diaphragm position. Therefore, other anatomical positions at the chest wall for transducer placement are used, but the variability in diaphragm thickness across the dome has not been systematically studied. The aim of this study was to evaluate anatomical variation of diaphragm thickness in 46 healthy volunteers on three ventrodorsal lines and two craniocaudal positions on these three lines. The intraclass correlation coefficient (ICC) for diaphragm thickness in the craniocaudal direction on the mid-axillary line was significantly higher than those on the posterior axillary and midclavicular lines, suggesting it had the lowest variability (ICC midaxillary  = .89, 95% confidence interval [CI]: 0.83-0.93, ICC posterior axillary  = 0.74, 95% CI: 0.62-0.85, ICC midclavicular  = 0.62, 95% CI: 0.43-0.47, p < 0.05). Average diaphragm thickness was comparable on the posterior axillary and midaxillary lines and substantially larger on the midclavicular line (1.24 mm [1.06-1.47], 1.27 mm [1.10-1.42] and 2.32 [1.97-2.70], p < 0.01). We conclude that the normal diaphragm has a large variability in thickness, especially in the ventrodorsal direction. Variability in craniocaudal position is the lowest at the midaxillary line, which therefore appears to be the preferred site for diaphragm thickness measurement., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

In mechanically ventilated patients with ARDS, prone positioning has demonstrated improvement, not only in oxygenation but also in survival. Whether early prone positioning of patients with mild hypoxemia and ARDS due to covid-19 improves outcome in terms of survival and prevention of the need for invasive ventilation was recently investigated in a RCT. Physiological reasoning would suggest a potential benefit. But no advantages of prone positioning were found in the study population of 250 patients. However, compliance to the intervention was very low in the intervention group making it practically impossible to assess the effects of the intervention. Therefore, clinicians should still rely on physiological reasoning and individual effects of prone positioning in deemed suitable patients.

Background: Given the long ventilation times of patients with COVID-19 that can cause atrophy and contractile weakness of respiratory muscle fibers, assessment of changes at the bedside would be interesting. As such, the aim of this study was to determine the evolution of respiratory muscle thickness assessed by ultrasound., Methods: Adult (> 18 y old) patients admitted to the ICU who tested positive for SARS-CoV-2 and were ventilated for < 24 h were consecutively included. The first ultrasound examination (diaphragm, rectus abdominis, and lateral abdominal wall muscles) was performed within 24 h of intubation and regarded as baseline measurement. After that, each following day an additional examination was performed, for a maximum of 8 examinations per subject., Results: In total, 30 subjects were included, of which 11 showed ≥ 10% decrease in diaphragm thickness from baseline; 10 showed < 10% change, and 9 showed ≥ 10% increase from baseline. Symptom duration before intubation was highest in the decrease group (12 [11-14] d, P = .03). Total time ventilated within the first week was lowest in the increase group (156 [129-172] h, P = .03). Average initial diaphragm thickness was 1.4 (1.1-1.6) mm and did not differ from final average thickness (1.3 [1.1-1.5] mm, P = .54). The rectus abdominis did not show statistically significant changes, whereas lateral abdominal wall thickness decreased from 14 [10-16] mm at baseline to 11 [9-13] mm on the last day of mechanical ventilation ( P = .08). Mixed-effect linear regression demonstrated an association of atrophy and neuromuscular-blocking agent (NMBA) use ( P = .01)., Conclusions: In ventilated subjects with COVID-19, overall no change in diaphragm thickness was observed. Subjects with decreased or unchanged thickness had a longer ventilation time than those with increased thickness. NMBA use was associated with decreased thickness. Rectus muscle thickness did not change over time, whereas lateral abdominal muscle thickness decreased but this change was not statistically significant., (Copyright © 2022 by Daedalus Enterprises.)

Purpose: To accommodate the unprecedented number of critically ill patients with pneumonia caused by coronavirus disease 2019 (COVID-19) expansion of the capacity of intensive care unit (ICU) to clinical areas not previously used for critical care was necessary. We describe the global burden of COVID-19 admissions and the clinical and organizational characteristics associated with outcomes in critically ill COVID-19 patients., Methods: Multicenter, international, point prevalence study, including adult patients with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and a diagnosis of COVID-19 admitted to ICU between February 15th and May 15th, 2020., Results: 4994 patients from 280 ICUs in 46 countries were included. Included ICUs increased their total capacity from 4931 to 7630 beds, deploying personnel from other areas. Overall, 1986 (39.8%) patients were admitted to surge capacity beds. Invasive ventilation at admission was present in 2325 (46.5%) patients and was required during ICU stay in 85.8% of patients. 60-day mortality was 33.9% (IQR across units: 20%-50%) and ICU mortality 32.7%. Older age, invasive mechanical ventilation, and acute kidney injury (AKI) were associated with increased mortality. These associations were also confirmed specifically in mechanically ventilated patients. Admission to surge capacity beds was not associated with mortality, even after controlling for other factors., Conclusions: ICUs responded to the increase in COVID-19 patients by increasing bed availability and staff, admitting up to 40% of patients in surge capacity beds. Although mortality in this population was high, admission to a surge capacity bed was not associated with increased mortality. Older age, invasive mechanical ventilation, and AKI were identified as the strongest predictors of mortality., (© 2022. The Author(s).)

Objectives: To determine the diagnostic accuracy of extended lung ultrasonographic assessment, including evaluation of dynamic air bronchograms and color Doppler imaging to differentiate pneumonia and atelectasis in patients with consolidation on chest radiograph. Compare this approach to the Simplified Clinical Pulmonary Infection Score, Lung Ultrasound Clinical Pulmonary Infection Score, and the Bedside Lung Ultrasound in Emergency protocol., Design: Prospective diagnostic accuracy study., Setting: Adult ICU applying selective digestive decontamination., Patients: Adult patients that underwent a chest radiograph for any indication at any time during admission. Patients with acute respiratory distress syndrome, coronavirus disease 2019, severe thoracic trauma, and infectious isolation measures were excluded., Interventions: None., Measurements and Main Results: Lung ultrasound was performed within 24 hours of chest radiograph. Consolidated tissue was assessed for presence of dynamic air bronchograms and with color Doppler imaging for presence of flow. Clinical data were recorded after ultrasonographic assessment. The primary outcome was diagnostic accuracy of dynamic air bronchogram and color Doppler imaging alone and within a decision tree to differentiate pneumonia from atelectasis. Of 120 patients included, 51 (42.5%) were diagnosed with pneumonia. The dynamic air bronchogram had a 45% (95% CI, 31-60%) sensitivity and 99% (95% CI, 92-100%) specificity. Color Doppler imaging had a 90% (95% CI, 79-97%) sensitivity and 68% (95% CI, 56-79%) specificity. The combined decision tree had an 86% (95% CI, 74-94%) sensitivity and an 86% (95% CI, 75-93%) specificity. The Bedside Lung Ultrasound in Emergency protocol had a 100% (95% CI, 93-100%) sensitivity and 0% (95% CI, 0-5%) specificity, while the Simplified Clinical Pulmonary Infection Score and Lung Ultrasound Clinical Pulmonary Infection Score had a 41% (95% CI, 28-56%) sensitivity, 84% (95% CI, 73-92%) specificity and 68% (95% CI, 54-81%) sensitivity, 81% (95% CI, 70-90%) specificity, respectively., Conclusions: In critically ill patients with pulmonary consolidation on chest radiograph, an extended lung ultrasound protocol is an accurate and directly bedside available tool to differentiate pneumonia from atelectasis. It outperforms standard lung ultrasound and clinical scores., Competing Interests: Drs. Jonkman and Heunks received funding from Liberate Medical. Dr. Heunks also received funding from Getinge Critical Care and Fisher and Paykel. Dr. Tuinman disclosed departmental work. 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: Lung- and diaphragm-protective ventilation is a novel concept that aims to limit the detrimental effects of mechanical ventilation on the diaphragm while remaining within limits of lung-protective ventilation. The premise is that low breathing effort under mechanical ventilation causes diaphragm atrophy, whereas excessive breathing effort induces diaphragm and lung injury. In a proof-of-concept study, we aimed to assess whether titration of inspiratory support based on diaphragm effort increases the time that patients have effort in a predefined "diaphragm-protective" range, without compromising lung-protective ventilation., Design: Randomized clinical trial., Setting: Mixed medical-surgical ICU in a tertiary academic hospital in the Netherlands., Patients: Patients (n = 40) with respiratory failure ventilated in a partially-supported mode., Interventions: In the intervention group, inspiratory support was titrated hourly to obtain transdiaphragmatic pressure swings in the predefined "diaphragm-protective" range (3-12 cm H2O). The control group received standard-of-care., Measurements and Main Results: Transdiaphragmatic pressure, transpulmonary pressure, and tidal volume were monitored continuously for 24 hours in both groups. In the intervention group, more breaths were within "diaphragm-protective" range compared with the control group (median 81%; interquartile range [64-86%] vs 35% [16-60%], respectively; p < 0.001). Dynamic transpulmonary pressures (20.5 ± 7.1 vs 18.5 ± 7.0 cm H2O; p = 0.321) and tidal volumes (7.56 ± 1.47 vs 7.54 ± 1.22 mL/kg; p = 0.961) were not different in the intervention and control group, respectively., Conclusions: Titration of inspiratory support based on patient breathing effort greatly increased the time that patients had diaphragm effort in the predefined "diaphragm-protective" range without compromising tidal volumes and transpulmonary pressures. This study provides a strong rationale for further studies powered on patient-centered outcomes., Competing Interests: Drs. de Vries’ and Heunks’ institutions received funding from Amsterdam Cardiovascular Sciences. Dr. de Vries has received speaker fees from the Dutch Ultrasound Center (the Netherlands) and travel and speaker fees from the Chinese Organization of Rehabilitation Medicine (China). Dr. Jonkman has received personal fees from Liberate Medical (United States). Dr. Heunks received research support from Liberate Medical (United States), Fisher and Paykel, and Orion Pharma (Finland), and speakers fee from Getinge (Sweden). Dr. de Man disclosed the off-label product use of oxidation-reduction potential measurement with the RedoxSYS System from Aytu Biosciences. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc.)

Objectives: The optimal targeted temperature in patients with shockable rhythm is unclear, and current guidelines recommend targeted temperature management with a correspondingly wide range between 32°C and 36°C. Our aim was to study survival and neurologic outcome associated with targeted temperature management strategy in postarrest patients with initial shockable rhythm., Design: Observational substudy of the Coronary Angiography after Cardiac Arrest without ST-segment Elevation trial., Setting: Nineteen hospitals in The Netherlands., Patients: The Coronary Angiography after Cardiac Arrest trial randomized successfully resuscitated patients with shockable rhythm and absence of ST-segment elevation to a strategy of immediate or delayed coronary angiography. In this substudy, 459 patients treated with mild therapeutic hypothermia (32.0-34.0°C) or targeted normothermia (36.0-37.0°C) were included. Allocation to targeted temperature management strategy was at the discretion of the physician., Interventions: None., Measurements and Main Results: After 90 days, 171 patients (63.6%) in the mild therapeutic hypothermia group and 129 (67.9%) in the targeted normothermia group were alive (hazard ratio, 0.86 [95% CI, 0.62-1.18]; log-rank p = 0.35; adjusted odds ratio, 0.89; 95% CI, 0.45-1.72). Patients in the mild therapeutic hypothermia group had longer ICU stay (4 d [3-7 d] vs 3 d [2-5 d]; ratio of geometric means, 1.32; 95% CI, 1.15-1.51), lower blood pressures, higher lactate levels, and increased need for inotropic support. Cerebral Performance Category scores at ICU discharge and 90-day follow-up and patient-reported Mental and Physical Health Scores at 1 year were similar in the two groups., Conclusions: In the context of out-of-hospital cardiac arrest with shockable rhythm and no ST-elevation, treatment with mild therapeutic hypothermia was not associated with improved 90-day survival compared with targeted normothermia. Neurologic outcomes at 90 days as well as patient-reported Mental and Physical Health Scores at 1 year did not differ between the groups., Competing Interests: Dr. Lemkes received funding from The Netherlands Heart Institute (NHLI) and Biotronik. Drs. Lemkes and Vlachojannis received funding from AstraZeneca. Dr. Rijpstra’s institution received funding from Principle Investigator. Dr. Vlachojannis’ institution received funding from MicroPort and Daiichi Sankyo; he received funding from Abbott. Dr. Vlachojannis reports receiving grant support from MicroPort Orthopedics and Daiichi Sankyo. Dr. van Royen’s institution received funding from Biotronik, AstraZeneca, the NHLI, Abbott, and Medtronic; he received funding from Novartis, MicroPort, Castor, Rainmed, Biotronik, Abbott, Medtronic, and Philips; he received support for article research from the NLHI. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Objective: To investigate the incidence, clinical characteristics and outcomes of early hyperbilirubinemia in critically ill patients., Design and Setting: This is a post hoc analysis of a prospective multicenter cohort study., Patients: Patients with measured bilirubin levels within the first 2 days after ICU admission were eligible. Patients with liver cirrhosis were excluded., Endpoints: The primary endpoint was the incidence of early hyperbilirubinemia, defined as bilirubin ≥33 μmol/L within 2 days after ICU admission. Secondary endpoints included clinical characteristics of patients with versus patients without early hyperbilirubinemia, and outcomes up to day 30., Results: Of 4,836 patients, 559 (11.6%) patients had early hyperbilirubinemia. Compared to patients without early hyperbilirubinemia, patients with early hyperbilirubinemia presented with higher severity of illness scores, and higher incidences of sepsis and organ failure. After adjustment for confounding variables, early hyperbilirubinemia remained associated with mortality at day 30 (odds ratio, 1.31 [95%-confidence interval 1.06-1.60]; P = 0.018). Patients with early hyperbilirubinemia and thrombocytopenia (interaction P-value = 0.005) had a higher likelihood of death within 30 days (odds ratio, 2.61 [95%-confidence interval 2.08-3.27]; P < 0.001) than patients with early hyperbilirubinemia and a normal platelet count (odds ratio, 1.09 [95%-confidence interval 0.75-1.55]; P = 0.655)., Conclusions: Early hyperbilirubinemia occurs frequently in the critically ill, and these patients present with higher disease severity and more often with sepsis and organ failures. Early hyperbilirubinemia has an association with mortality, albeit this association was only found in patients with concomitant thrombocytopenia., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society.)

Background: Lung ultrasound has established itself as an accurate diagnostic tool in different clinical settings. However, its effects on clinical-decision making are insufficiently described. This systematic review aims to investigate the impact of lung ultrasound, exclusively or as part of an integrated thoracic ultrasound examination, on clinical-decision making in different departments, especially the emergency department (ED), intensive care unit (ICU), and general ward (GW)., Methods: This systematic review was registered at PROSPERO (CRD42021242977). PubMed, EMBASE, and Web of Science were searched for original studies reporting changes in clinical-decision making (e.g. diagnosis, management, or therapy) after using lung ultrasound. Inclusion criteria were a recorded change of management (in percentage of cases) and with a clinical presentation to the ED, ICU, or GW. Studies were excluded if examinations were beyond the scope of thoracic ultrasound or to guide procedures. Mean changes with range (%) in clinical-decision making were reported. Methodological data on lung ultrasound were also collected. Study quality was scored using the Newcastle-Ottawa scale., Results: A total of 13 studies were included: five studies on the ED (546 patients), five studies on the ICU (504 patients), two studies on the GW (1150 patients), and one study across all three wards (41 patients). Lung ultrasound changed the diagnosis in mean 33% (15-44%) and 44% (34-58%) of patients in the ED and ICU, respectively. Lung ultrasound changed the management in mean 48% (20-80%), 42% (30-68%) and 48% (48-48%) of patients in the ED, in the ICU and in the GW, respectively. Changes in management were non-invasive in 92% and 51% of patients in the ED and ICU, respectively. Lung ultrasound methodology was heterogeneous across studies. Risk of bias was moderate to high in all studies., Conclusions: Lung ultrasound, exclusively or as a part of thoracic ultrasound, has substantial impact on clinical-decision making by changing diagnosis and management in the EDs, ICUs, and GWs. The current evidence level and methodological heterogeneity underline the necessity for well-designed trials and standardization of methodology., (© 2022. The Author(s).)

Introduction: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19., Methods: We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots., Results: A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure., Conclusion: The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records., (© 2021. The Author(s).)

Purpose: We assessed the ability of mid-regional proadrenomedullin (MR-proADM) and C-terminal proendothelin-1 (CT-proET-1) to predict 28-day mortality in critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia., Methods: Biomarkers were collected during the first seven days in this prospective observational cohort study. We investigated the relationship between biomarkers and mortality in a multivariable Cox regression model adjusted for age and SOFA score., Results: In 105 critically ill patients with confirmed SARS-CoV-2 pneumonia 28-day mortality was 28.6%. MR-proADM and CT-proET-1 were significantly higher in 28-day non-survivors at baseline and over time. ROC curves revealed high accuracy to identify non-survivors for baseline MR-proADM and CT-proET-1, AUC 0.84, (95% CI 0.76-0.92), p < 0.001 and 0.79, (95% CI 0.69-0.89), p < 0.001, respectively. The AUC for prediction of 28-day mortality for MR-proADM and CT-proET-1 remained high over time. MR-proADM ≥1.57 nmol/L and CT-proET-1 ≥ 111 pmol/L at baseline were significant predictors for 28-day mortality (HR 6.80, 95% CI 3.12-14.84, p < 0.001 and HR 3.72, 95% CI 1.71-8.08, p 0.01)., Conclusion: Baseline and serial MR-proADM and CT-proET-1 had good ability to predict 28-day mortality in critically ill patients with SARS-CoV-2 pneumonia., Trial Registration: NEDERLANDS TRIAL REGISTER, NL8460., Competing Interests: Declaration of Competing Interest The authors declare that they did not have any financial support. The manufacturer provided free MR-proADM and CT-proET-1 laboratory kits for this study. The authors declare that they have no conflict of interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objectives: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients., Design: Multicenter retrospective cohort study., Setting: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020., Patients: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded., Measurements and Main Results: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio 2 , set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao 2 /Fio 2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant ( p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves., Conclusions: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Background: Evidence from previous studies comparing lung ultrasound to thoracic computed tomography (CT) in intensive care unit (ICU) patients is limited due to multiple methodologic weaknesses. While addressing methodologic weaknesses of previous studies, the primary aim of this study is to investigate the diagnostic accuracy of lung ultrasound in a tertiary ICU population., Methods: This is a single-center, prospective diagnostic accuracy study conducted at a tertiary ICU in the Netherlands. Critically ill patients undergoing thoracic CT for any clinical indication were included. Patients were excluded if time between the index and reference test was over eight hours. Index test and reference test consisted of 6-zone lung ultrasound and thoracic CT, respectively. Hemithoraces were classified by the index and reference test as follows: consolidation, interstitial syndrome, pneumothorax and pleural effusion. Sensitivity, specificity, positive and negative likelihood ratio were estimated., Results: In total, 87 patients were included of which eight exceeded the time limit and were subsequently excluded. In total, there were 147 respiratory conditions in 79 patients. The estimated sensitivity and specificity to detect consolidation were 0.76 (95%CI: 0.68 to 0.82) and 0.92 (0.87 to 0.96), respectively. For interstitial syndrome they were 0.60 (95%CI: 0.48 to 0.71) and 0.69 (95%CI: 0.58 to 0.79). For pneumothorax they were 0.59 (95%CI: 0.33 to 0.82) and 0.97 (95%CI: 0.93 to 0.99). For pleural effusion they were 0.85 (95%CI: 0.77 to 0.91) and 0.77 (95%CI: 0.62 to 0.88)., Conclusions: In conclusion, lung ultrasound is an adequate diagnostic modality in a tertiary ICU population to detect consolidations, interstitial syndrome, pneumothorax and pleural effusion. Moreover, one should be careful not to interpret lung ultrasound results in deterministic fashion as multiple respiratory conditions can be present in one patient. Trial registration This study was retrospectively registered at Netherlands Trial Register on March 17, 2021, with registration number NL9344., (© 2021. The Author(s).)

Background: Older patients have a less pronounced immune response to infection, which may also influence infection biomarkers. There is currently insufficient data regarding clinical effects of procalcitonin (PCT) to guide antibiotic treatment in older patients., Objective and Design: We performed an individual patient data meta-analysis to investigate the association of age on effects of PCT-guided antibiotic stewardship regarding antibiotic use and outcome., Subjects and Methods: We had access to 9,421 individual infection patients from 28 randomized controlled trials comparing PCT-guided antibiotic therapy (intervention group) or standard care. We stratified patients according to age in four groups (<75 years [n = 7,079], 75-80 years [n = 1,034], 81-85 years [n = 803] and >85 years [n = 505]). The primary endpoint was the duration of antibiotic treatment and the secondary endpoints were 30-day mortality and length of stay., Results: Compared to control patients, mean duration of antibiotic therapy in PCT-guided patients was significantly reduced by 24, 22, 26 and 24% in the four age groups corresponding to adjusted differences in antibiotic days of -1.99 (95% confidence interval [CI] -2.36 to -1.62), -1.98 (95% CI -2.94 to -1.02), -2.20 (95% CI -3.15 to -1.25) and - 2.10 (95% CI -3.29 to -0.91) with no differences among age groups. There was no increase in the risk for mortality in any of the age groups. Effects were similar in subgroups by infection type, blood culture result and clinical setting (P interaction >0.05)., Conclusions: This large individual patient data meta-analysis confirms that, similar to younger patients, PCT-guided antibiotic treatment in older patients is associated with significantly reduced antibiotic exposures and no increase in mortality., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Unexpected ICU readmission is associated with longer length of stay and increased mortality. To prevent ICU readmission and death after ICU discharge, our team of intensivists and data scientists aimed to use AmsterdamUMCdb to develop an explainable machine learning-based real-time bedside decision support tool., Derivation Cohort: Data from patients admitted to a mixed surgical-medical academic medical center ICU from 2004 to 2016., Validation Cohort: Data from 2016 to 2019 from the same center., Prediction Model: Patient characteristics, clinical observations, physiologic measurements, laboratory studies, and treatment data were considered as model features. Different supervised learning algorithms were trained to predict ICU readmission and/or death, both within 7 days from ICU discharge, using 10-fold cross-validation. Feature importance was determined using SHapley Additive exPlanations, and readmission probability-time curves were constructed to identify subgroups. Explainability was established by presenting individualized risk trends and feature importance., Results: Our final derivation dataset included 14,105 admissions. The combined readmission/mortality rate within 7 days of ICU discharge was 5.3%. Using Gradient Boosting, the model achieved an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.75-0.81) and an area under the precision-recall curve of 0.19 on the validation cohort ( n = 3,929). The most predictive features included common physiologic parameters but also less apparent variables like nutritional support. At a 6% risk threshold, the model showed a sensitivity (recall) of 0.72, specificity of 0.70, and a positive predictive value (precision) of 0.15. Impact analysis using probability-time curves and the 6% risk threshold identified specific patient groups at risk and the potential of a change in discharge management to reduce relative risk by 14%., Conclusions: We developed an explainable machine learning model that may aid in identifying patients at high risk for readmission and mortality after ICU discharge using the first freely available European critical care database, AmsterdamUMCdb. Impact analysis showed that a relative risk reduction of 14% could be achievable, which might have significant impact on patients and society. ICU data sharing facilitates collaboration between intensivists and data scientists to accelerate model development., Competing Interests: Amsterdam University Medical Centers is entitled to royalties from Pacmed. Mr. Hovenkamp is cofounder of Pacmed, a Dutch technology company in health care. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Background: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients., Methods: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers., Results: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive., Conclusions: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine., (© 2021. The Author(s).)