[Shared decision-making in acute life-threatening situations: two cases]. / Gezamenlijke besluitvorming bij acuut levensbedreigende situaties.

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.

Incidence of catheter-related thrombosis and its association with outcome in critically ill patients: A prospective observational study.

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.

Reinforcement learning for intensive care medicine: actionable clinical insights from novel approaches to reward shaping and off-policy model evaluation.

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.

Micronutrient Status of Critically Ill Patients with COVID-19 Pneumonia.

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.

Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment.

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.

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study.

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).

Augmented intelligence facilitates concept mapping across different electronic health records.

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.

Age-related changes in plasma biomarkers and their association with mortality in COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19)-induced mortality occurs predominantly in older patients. Several immunomodulating therapies seem less beneficial in these patients. The biological substrate behind these observations is unknown. The aim of this study was to obtain insight into the association between ageing, the host response and mortality in patients with COVID-19. METHODS: We determined 43 biomarkers reflective of alterations in four pathophysiological domains: endothelial cell and coagulation activation, inflammation and organ damage, and cytokine and chemokine release. We used mediation analysis to associate ageing-driven alterations in the host response with 30-day mortality. Biomarkers associated with both ageing and mortality were validated in an intensive care unit and external cohort. RESULTS: 464 general ward patients with COVID-19 were stratified according to age decades. Increasing age was an independent risk factor for 30-day mortality. Ageing was associated with alterations in each of the host response domains, characterised by greater activation of the endothelium and coagulation system and stronger elevation of inflammation and organ damage markers, which was independent of an increase in age-related comorbidities. Soluble tumour necrosis factor receptor 1, soluble triggering receptor expressed on myeloid cells 1 and soluble thrombomodulin showed the strongest correlation with ageing and explained part of the ageing-driven increase in 30-day mortality (proportion mediated: 13.0%, 12.9% and 12.6%, respectively). CONCLUSIONS: Ageing is associated with a strong and broad modification of the host response to COVID-19, and specific immune changes likely contribute to increased mortality in older patients. These results may provide insight into potential age-specific immunomodulatory targets in COVID-19.

Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.

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.

Concise Versus Extended Lung Ultrasound Score to Monitor Critically Ill Patients With COVID-19.

BACKGROUND: Lung ultrasound (LUS) can be used to monitor critically ill patients with COVID-19, but the optimal number of examined lung zones is disputed. METHODS: This was a prospective observational study. The objective was to investigate whether concise (6 zones) and extended (12 zones) LUS scoring protocols are clinically equivalent in critically ill ICU subjects with COVID-19. The primary outcome of this study was (statistical) agreement between concise and extended LUS score index evaluated in both supine and prone position. Agreement was determined using correlation coefficients and Bland-Altman plots to detect systematic differences between protocols. Secondary outcomes were difference between LUS score index in supine and prone position using similar methods. RESULTS: We included 130 LUS examinations in 40 subjects (mean age 69.0 ± 8.5y, 75% male). Agreement between concise and extended LUS score index had no clinically relevant constant or proportional bias and limits of agreement were below the smallest detectable change. Across position changes, supine LUS score index was 8% higher than prone LUS score index and had limits above the smallest detectable change, indicating true LUS score index differences between protocols may occur due to the position change itself. Lastly, inter-rater and intra-rater agreement were very good. CONCLUSIONS: Concise LUS was equally informative as extended LUS for monitoring critically ill subjects with COVID-19 in supine or prone position. Clinicians can monitor patients undergoing position changes but must be wary that LUS score index alterations may result from the position change itself rather than disease progression or clinical improvement.

Performance of Noninvasive Airway Occlusion Maneuvers to Assess Lung Stress and Diaphragm Effort in Mechanically Ventilated Critically Ill Patients.

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.

Effects of nurse delivered thoracic ultrasound on management of adult intensive care unit patients: A prospective observational study.

Background: Thoracic ultrasound is a valuable tool that helps diagnose cardiopulmonary disorders and guide management in intensive care unit patients. Intensive care unit nurses were trained to perform thoracic ultrasound examinations, after which they were named 'UltraNurses' for clinical recognizability. UltraNurses demonstrated rapid learning trajectories, but the impact on clinical-decision making remained unknown. The aim of this study was to investigate the effects of UltraNurse ultrasound on clinical management. Methods: This was a prospective observational single center study within a mixed medical and surgical intensive care unit. All adult patients with an indication for UltraNurse thoracic ultrasound were included. The study consisted of three steps: pre- and post- data collection, with the ultrasound examination conducted in-between these two steps. The examination consisted of a standardized ultrasound protocol aimed at the lungs and cardiac output. Primary outcome was what percentage of ultrasounds led to a change of management. Secondary outcomes included: percentage of changes executed within first 8 hours, frequency of pathology found, percentage of diagnosis change, and frequency of UltraNurse ultrasounds per shift. Results: A total of 102 ultrasound examinations were performed in 65 patients (89% mechanically-ventilated). Ultrasound examinations suggested changes of management in 26% of cases, of which 96% were executed within 8 hours. Most changes were within the nursing scope (56%), specifically: 44% of examinations changed fluid management. UltraNurse ultrasound detected pathology in 97% of cases. In 7% of cases, the diagnosis was changed, sometimes leading to life-saving interventions. UltraNurses performed one thoracic ultrasound examination per four shifts. Conclusion: In adult intensive care unit patients, UltraNurse thoracic ultrasound led to a change of management in more than a quarter of the cases, of which almost all were executed within the first 8 hours. Study registration: Netherlands Trial Registration NL9047, VUmc 2020.011 (prospectively registered on: 13-11-2020).

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.

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 PaO2/FiO2 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 PaO2/FiO2 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 PaO2/FiO2 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.

Leveraging electronic health records for data science: common pitfalls and how to avoid them.

Analysis of electronic health records (EHRs) is an increasingly common approach for studying real-world patient data. Use of routinely collected data offers several advantages compared with other study designs, including reduced administrative costs, the ability to update analysis as practice patterns evolve, and larger sample sizes. Methodologically, EHR analysis is subject to distinct challenges because data are not collected for research purposes. In this Viewpoint, we elaborate on the importance of in-depth knowledge of clinical workflows and describe six potential pitfalls to be avoided when working with EHR data, drawing on examples from the literature and our experience. We propose solutions for prevention or mitigation of factors associated with each of these six pitfalls-sample selection bias, imprecise variable definitions, limitations to deployment, variable measurement frequency, subjective treatment allocation, and model overfitting. Ultimately, we hope that this Viewpoint will guide researchers to further improve the methodological robustness of EHR analysis.

INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY.

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.

Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock-a two-centre randomised clinical trial.

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.

[Is prone positioning of not-mechanically ventilated patients with moderate hypoxaemia due to covid-19 advantageous?] / Is buikligging bij niet-ernstig zieke patiënten met covid-19 zinvol?

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.

Evolution of Respiratory Muscles Thickness in Mechanically Ventilated Patients With COVID-19.

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.