Ghrelin for Neuroprotection in Post-Cardiac Arrest Coma: A Randomized Clinical Trial.

Importance: Out-of-hospital cardiac arrest survival rates have markedly risen in the last decades, but neurological outcome only improved marginally. Despite research on more than 20 neuroprotective strategies involving patients in comas after cardiac arrest, none have demonstrated unequivocal evidence of efficacy; however, treatment with acyl-ghrelin has shown improved functional and histological brain recovery in experimental models of cardiac arrest and was safe in a wide variety of human study populations. Objective: To determine safety and potential efficacy of intravenous acyl-ghrelin to improve neurological outcome in patients in a coma after cardiac arrest. Design, Setting, and Participants: A phase 2, double-blind, placebo-controlled, multicenter, randomized clinical trial, Ghrelin Treatment of Comatose Patients After Cardiac Arrest: A Clinical Trial to Promote Cerebral Recovery (GRECO), was conducted between January 18, 2019, and October 17, 2022. Adult patients 18 years or older who were in a comatose state after cardiac arrest were assessed for eligibility; patients were from 3 intensive care units in the Netherlands. Expected death within 48 hours or unfeasibility of treatment initiation within 12 hours were exclusion criteria. Interventions: Patients were randomized to receive intravenous acyl-ghrelin, 600 µg (intervention group), or placebo (control group) within 12 hours after cardiac arrest, continued for 7 days, twice daily, in addition to standard care. Main Outcomes and Measures: Primary outcome was the score on the Cerebral Performance Categories (CPC) scale at 6 months. Safety outcomes included any serious adverse events. Secondary outcomes were mortality and neuron-specific enolase (NSE) levels on days 1 and 3. Results: A total of 783 adult patients in a coma after cardiac arrest were assessed for eligibility, and 160 patients (median [IQR] age, 68 [57-75] years; 120 male [75%]) were enrolled. A total of 81 patients (51%) were assigned to the intervention group, and 79 (49%) were assigned to the control group. The common odds ratio (OR) for any CPC improvement in the intervention group was 1.78 (95% CI, 0.98-3.22; P = .06). This was consistent over all CPC categories. Mean (SD) NSE levels on day 1 after cardiac arrest were significantly lower in the intervention group (34 [6] µg/L vs 56 [13] µg/L; P = .04) and on day 3 (28 [6] µg/L vs 52 [14] µg/L; P = .08). Serious adverse events were comparable in incidence and type between the groups. Mortality was 37% (30 of 81) in the intervention group vs 51% (40 of 79) in the control group (absolute risk reduction, 14%; 95% CI, -2% to 29%; P = .08). Conclusions and Relevance: In patients in a coma after cardiac arrest, intravenous treatment with acyl-ghrelin was safe and potentially effective to improve neurological outcome. Phase 3 trials are needed for conclusive evidence. Trial Registration: Clinicaltrialsregister.eu: EUCTR2018-000005-23-NL.

Conservative versus Liberal Oxygenation Targets in Intensive Care Unit Patients (ICONIC): A Randomized Clinical Trial.

Rationale: Supplemental oxygen is widely administered to ICU patients, but appropriate oxygenation targets remain unclear. Objectives: This study aimed to determine whether a low-oxygenation strategy would lower 28-day mortality compared with a high-oxygenation strategy. Methods: This randomized multicenter trial included mechanically ventilated ICU patients with an expected ventilation duration of at least 24 hours. Patients were randomized 1:1 to a low-oxygenation (PaO2, 55-80 mm Hg; or oxygen saturation as measured by pulse oximetry, 91-94%) or high-oxygenation (PaO2, 110-150 mm Hg; or oxygen saturation as measured by pulse oximetry, 96-100%) target until ICU discharge or 28 days after randomization, whichever came first. The primary outcome was 28-day mortality. The study was stopped prematurely because of the COVID-19 pandemic when 664 of the planned 1,512 patients were included. Measurements and Main Results: Between November 2018 and November 2021, a total of 664 patients were included in the trial: 335 in the low-oxygenation group and 329 in the high-oxygenation group. The median achieved PaO2 was 75 mm Hg (interquartile range, 70-84) and 115 mm Hg (interquartile range, 100-129) in the low- and high-oxygenation groups, respectively. At Day 28, 129 (38.5%) and 114 (34.7%) patients had died in the low- and high-oxygenation groups, respectively (risk ratio, 1.11; 95% confidence interval, 0.9-1.4; P = 0.30). At least one serious adverse event was reported in 12 (3.6%) and 17 (5.2%) patients in the low- and high-oxygenation groups, respectively. Conclusions: Among mechanically ventilated ICU patients with an expected mechanical ventilation duration of at least 24 hours, using a low-oxygenation strategy did not result in a reduction of 28-day mortality compared with a high-oxygenation strategy. Clinical trial registered with the National Trial Register and the International Clinical Trials Registry Platform (NTR7376).

[Well-meant oxygen administration with harmful effects]. / Goedbedoelde zuurstof met ongewenst effect.

For more than hundred years oxygen has been administered to patients for a variety of indications: first and foremost to treat, and later to prevent, hypoxemia. Some years after the first exhilarating reports, it became apparent that hyperoxemia may have harmful sequelae. The pathophysiological mechanism has been determined: vasoconstiction of coronary, cerebral and systemic arteries. And additionally the formation of reactive oxygen species, resulting in cellular damage and ultimately cell death. In a variety of medical emergencies the detrimental clinical effects of hyperoxemia have been demonstrated: increased mortality and more organ dysfunction. And recently it was found the latter also applies to patients undergoing (elective) surgery. It might therefore be concluded that hyperoxemia is justifiable for short periods of time to prevent hypoxemia (i.e. endotracheal intubation), but in all other situations normoxemia should be the target.

Early EEG monitoring predicts clinical outcome in patients with moderate to severe traumatic brain injury.

There is a need for reliable predictors in patients with moderate to severe traumatic brain injury to assist clinical decision making. We assess the ability of early continuous EEG monitoring at the intensive care unit (ICU) in patients with traumatic brain injury (TBI) to predict long term clinical outcome and evaluate its complementary value to current clinical standards. We performed continuous EEG measurements in patients with moderate to severe TBI during the first week of ICU admission. We assessed the Extended Glasgow Outcome Scale (GOSE) at 12 months, dichotomized into poor (GOSE 1-3) and good (GOSE 4-8) outcome. We extracted EEG spectral features, brain symmetry index, coherence, aperiodic exponent of the power spectrum, long range temporal correlations, and broken detailed balance. A random forest classifier using feature selection was trained to predict poor clinical outcome based on EEG features at 12, 24, 48, 72 and 96 h after trauma. We compared our predictor with the IMPACT score, the best available predictor, based on clinical, radiological and laboratory findings. In addition we created a combined model using EEG as well as the clinical, radiological and laboratory findings. We included hundred-seven patients. The best prediction model using EEG parameters was found at 72 h after trauma with an AUC of 0.82 (0.69-0.92), specificity of 0.83 (0.67-0.99) and sensitivity of 0.74 (0.63-0.93). The IMPACT score predicted poor outcome with an AUC of 0.81 (0.62-0.93), sensitivity of 0.86 (0.74-0.96) and specificity of 0.70 (0.43-0.83). A model using EEG and clinical, radiological and laboratory parameters resulted in a better prediction of poor outcome (p < 0.001) with an AUC of 0.89 (0.72-0.99), sensitivity of 0.83 (0.62-0.93) and specificity of 0.85 (0.75-1.00). EEG features have potential use for predicting clinical outcome and decision making in patients with moderate to severe TBI and provide complementary information to current clinical standards.

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.

Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records.

PURPOSE: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data. METHODS: Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors. RESULTS: A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors. CONCLUSION: In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves.

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.

Anti-C5a antibody (vilobelimab) therapy for critically ill, invasively mechanically ventilated patients with COVID-19 (PANAMO): a multicentre, double-blind, randomised, placebo-controlled, phase 3 trial.

BACKGROUND: Vilobelimab, an anti-C5a monoclonal antibody, was shown to be safe in a phase 2 trial of invasively mechanically ventilated patients with COVID-19. Here, we aimed to determine whether vilobelimab in addition to standard of care improves survival outcomes in this patient population. METHODS: This randomised, double-blind, placebo-controlled, multicentre phase 3 trial was performed at 46 hospitals in the Netherlands, Germany, France, Belgium, Russia, Brazil, Peru, Mexico, and South Africa. Participants aged 18 years or older who were receiving invasive mechanical ventilation, but not more than 48 h after intubation at time of first infusion, had a PaO2/FiO2 ratio of 60-200 mm Hg, and a confirmed SARS-CoV-2 infection with any variant in the past 14 days were eligible for this study. Eligible patients were randomly assigned (1:1) to receive standard of care and vilobelimab at a dose of 800 mg intravenously for a maximum of six doses (days 1, 2, 4, 8, 15, and 22) or standard of care and a matching placebo using permuted block randomisation. Treatment was not continued after hospital discharge. Participants, caregivers, and assessors were masked to group assignment. The primary outcome was defined as all-cause mortality at 28 days in the full analysis set (defined as all randomly assigned participants regardless of whether a patient started treatment, excluding patients randomly assigned in error) and measured using Kaplan-Meier analysis. Safety analyses included all patients who had received at least one infusion of either vilobelimab or placebo. This study is registered with ClinicalTrials.gov, NCT04333420. FINDINGS: From Oct 1, 2020, to Oct 4, 2021, we included 368 patients in the ITT analysis (full analysis set; 177 in the vilobelimab group and 191 in the placebo group). One patient in the vilobelimab group was excluded from the primary analysis due to random assignment in error without treatment. At least one dose of study treatment was given to 364 (99%) patients (safety analysis set). 54 patients (31%) of 177 in the vilobelimab group and 77 patients (40%) of 191 in the placebo group died in the first 28 days. The all-cause mortality rate at 28 days was 32% (95% CI 25-39) in the vilobelimab group and 42% (35-49) in the placebo group (hazard ratio 0·73, 95% CI 0·50-1·06; p=0·094). In the predefined analysis without site-stratification, vilobelimab significantly reduced all-cause mortality at 28 days (HR 0·67, 95% CI 0·48-0·96; p=0·027). The most common TEAEs were acute kidney injury (35 [20%] of 175 in the vilobelimab group vs 40 [21%] of 189 in the placebo), pneumonia (38 [22%] vs 26 [14%]), and septic shock (24 [14%] vs 31 [16%]). Serious treatment-emergent adverse events were reported in 103 (59%) of 175 patients in the vilobelimab group versus 120 (63%) of 189 in the placebo group. INTERPRETATION: In addition to standard of care, vilobelimab improves survival of invasive mechanically ventilated patients with COVID-19 and leads to a significant decrease in mortality. Vilobelimab could be considered as an additional therapy for patients in this setting and further research is needed on the role of vilobelimab and C5a in other acute respiratory distress syndrome-causing viral infections. FUNDING: InflaRx and the German Federal Government.

Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.

BACKGROUND: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. METHODS: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. RESULTS: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). CONCLUSIONS: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far.

Suboptimal plasma concentrations with posaconazole suspension as prophylaxis in critically ill COVID-19 patients at risk of Covid-associated pulmonary aspergillosis.

WHAT IS KNOWN AND OBJECTIVE: The safety and efficacy of different antifungal agents in the prophylaxis of invasive fungal infection in patients with haematological disorders are known. We comment on the poor bioavailability of posaconazole suspension to suggest that it is not useful in critically ill COVID patients. COMMENT: The increased mortality and high incidence of COVID-associated pulmonary aspergillosis (CAPA) might justify administration of off-label posaconazole for preventing CAPA, being the only drug officially registered for prophylaxis of fungal infections. We decided to initiate off-label posaconazole prophylaxis in COVID-19 patients, who were mechanically ventilated and exposed to high-dose steroids for progressive pulmonary disease or ARDS. We found that posaconazole suspension was inadequate. Very low trough levels were observed after administration, and the dose adjustments necessary for the therapeutic drug monitoring (TDM) of the drug in our critically ill ICU patients were not useful. WHAT IS NEW AND CONCLUSION: Posaconazole suspension should not be used to prevent CAPA in COVID-19 patients on high-dose steroid therapy.

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

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.

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

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, Fio2, 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 Pao2/Fio2 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.

[Communication about in hospital resuscitation orders]. / Communicatie over reanimatiebeleid in het ziekenhuis.

BACKGROUND: The decision to attempt or refrain from resuscitation is preferably based on prognostic factors for outcome and subsequently communicated with patients. Both patients and physicians consider good communication important, however little is known about patient involvement in and understanding of cardiopulmonary resuscitation (CPR) directives. AIM: To determine the prevalence of Do Not Resuscitate (DNR)-orders, to describe recollection of CPR-directive conversations and factors associated with patient recollection and understanding. METHODS: This was a two-week nationwide multicentre cross-sectional observational study using a study-specific survey. The study population consisted of patients admitted to non-monitored wards in 13 hospitals. Data were collected from the electronic medical record (EMR) concerning CPR-directive, comorbidity and at-home medication. Patients reported their perception and expectations about CPR-counselling through a questionnaire. RESULTS: A total of 1136 patients completed the questionnaire. Patients' CPR-directives were documented in the EMR as follows: 63.7% full code, 27.5% DNR and in 8.8% no directive was documented. DNR was most often documented for patients >80 years (66.4%) and in patients using >10 medications (45.3%). Overall, 55.8% of patients recalled having had a conversation about their CPR-directive and 48.1% patients reported the same CPR-directive as the EMR. Most patients had a good experience with the CPR-directive conversation in general (66.1%), as well as its timing (84%) and location (94%) specifically. CONCLUSIONS: The average DNR-prevalence is 27.5%. Correct understanding of their CPR-directive is lowest in patients aged ≥80 years and multimorbid patients. CPR-directive counselling should focus more on patient involvement and their correct understanding.

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.

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.

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.

BACKGROUND: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. METHODS: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. RESULTS: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. CONCLUSION: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

Recombinant human activated protein C in the treatment of acute respiratory distress syndrome: a randomized clinical trial.

RATIONALE: Pulmonary coagulopathy may play a pathogenetic role in acute respiratory distress syndrome (ARDS), by contributing to alveolocapillary inflammation and increased permeability. Recombinant human activated protein C (rh-APC) may inhibit this process and thereby improve patient outcome. METHODS: A prospective randomized, saline-controlled, single-blinded clinical trial was performed in the intensive care units of two university hospitals, and patients with ARDS were included within 24 h after meeting inclusion criteria. INTERVENTION: A 4-day course of intravenous rh-APC (24 mcg/kg/h) (n = 33) versus saline (n = 38). OUTCOMES: The primary outcome parameter was the pulmonary leak index (PLI) of 67Gallium-transferrin as a measure of alveolocapillary permeability and secondary outcomes were disease severity scores and ventilator-free days, among others. RESULTS: Baseline characteristics were similar; in 87% of patients the PLI was above normal and in 90% mechanical or non-invasive ventilation was instituted at a median lung injury score of 2.5. There was no evidence that Rh-APC treatment affected the PLI or attenuated lung injury and sequential organ failure assessment scores. Mean ventilator-free days amounted to 14 (rh-APC) and 12 days (saline, P = 0.35). 28-day mortality was 6% in rh-APC- and 18% in saline-treated patients (P = 0.12). There was no difference in bleeding events. The study was prematurely discontinued because rh-APC was withdrawn from the market. CONCLUSION: There is no evidence that treatment with intravenous rh-APC during 4 days for infectious or inflammatory ARDS ameliorates increased alveolocapillary permeability or the clinical course of ARDS patients. We cannot exclude underpowering. TRIAL REGISTRATION: Nederlands Trial Register ISRCTN 52566874.

Case Report: Bilateral reexpansion pulmonary edema following treatment of a unilateral hemothorax.

Bilateral re-expansion pulmonary edema (RPE) is an extremely rare entity. We report the unique case of bilateral RPE following a traumatic, unilateral hemopneumothorax in a young healthy male. Bilateral RPE occurred only one hour after drainage of a unilateral hemopneumothorax. The patient was treated with diuretics and supplemental oxygen. Diagnosis was confirmed by excluding other causes, using laboratory findings, chest radiography, pulmonary and cardiac ultrasound and high resolution computed tomography. His recovery was uneventful. The pathophysiology of bilateral RPE is not well known. Treatment is mainly supportive and consists of diuretics, mechanical ventilation, inotropes and steroids. In case of a pulmonary deterioration after the drainage of a traumatic pneumothorax, bilateral RPE should be considered after exclusion of more common causes of dyspnea.