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.

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.

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.

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.

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.

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.

[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.

Mid-regional proadrenomedullin, C-terminal proendothelin-1 values, and disease course are not different in critically ill SARS-CoV-2 pneumonia patients with obesity.

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/m2, n = 42) and patients without obesity (BMI < 30 kg/m2, 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/m2 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.

Lung Ultrasound Signs to Diagnose and Discriminate Interstitial Syndromes in ICU Patients: A Diagnostic Accuracy Study in Two Cohorts.

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.

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.

Anatomical Variation in Diaphragm Thickness Assessed with Ultrasound in Healthy Volunteers.

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 (ICCmidaxillary = .89, 95% confidence interval [CI]: 0.83-0.93, ICCposterior axillary = 0.74, 95% CI: 0.62-0.85, ICCmidclavicular = 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.

C-Terminal Proarginine Vasopressin is Associated with Disease Outcome and Mortality, but not with Delayed Cerebral Ischemia in Critically Ill Patients with an Aneurysmal Subarachnoid Hemorrhage: A Prospective Cohort Study.

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.

Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study.

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.

Early high protein provision and mortality in ICU patients including those receiving continuous renal replacement therapy.

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.

Lung- and Diaphragm-Protective Ventilation by Titrating Inspiratory Support to Diaphragm Effort: A Randomized Clinical Trial.

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.