A cross-sectional investigation of communication in Do-Not-Resuscitate orders in Dutch hospitals

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 No

Electroweak Radiative Corrections to Neutral-Current Drell-Yan Processes at Hadron Colliders

We calculate the complete electroweak O(alpha) corrections to pp, pbar p -> l+l- X (l=e, mu) in the Standard Model of electroweak interactions. They comprise weak and photonic virtual one-loop corrections as well as real photon radiation to the parton-level processes q bar q -> gamma,Z -> l+l-. We study in detail the effect of the radiative corrections on the l+l- invariant mass distribution, the cross section in the Z boson resonance region, and on the forward-backward asymmetry, A_FB, at the Fermilab Tevatron and the CERN Large Hadron Collider. The weak corrections are found to increase the Z boson cross section by about 1%, but have little effect on the forward-backward asymmetry in the Z peak region. Threshold effects of the W box diagrams lead to pronounced effects in A_FB at m(l+l-) approx 160 GeV which, however, will be difficult to observe experimentally. At high di-lepton invariant masses, the non-factorizable weak corrections are found to become large.Comment: Revtex3 file, 39 pages, 2 tables, 12 figure

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.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

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 PaO(2)/FiO(2) 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

Attitudes of Dutch intensive care unit clinicians towards oxygen therapy

Background: Over the last decade, there has been an increasing awareness for the potential harm of the administration of too much oxygen. We aimed to describe self-reported attitudes towards oxygen therapy by clinicians from a large representative sample of intensive care units (ICUs) in the Netherlands.Methods: In April 2019, 36 ICUs in the Netherlands were approached and asked to send out a questionnaire (59 questions) to their nursing and medical staff (ICU clinicians) eliciting self-reported behaviour and attitudes towards oxygen therapy in general and in specific ICU case scenarios.Results: In total, 1361 ICU clinicians (71% nurses, 24% physicians) from 28 ICUs returned the questionnaire. Of responding ICU clinicians, 64% considered oxygen-induced lung injury to be a major concern. The majority of respondents considered a partial pressure of oxygen (PaO2) of 6-10 kPa (45-75 mmHg) and an arterial saturation (SaO(2)) of 85-90% as acceptable for 15 minutes, and a PaO2 7-To kPa (53-75 mmHg) and SaO(2) 90-95% as acceptable for 24-48 hours in an acute respiratory distress syndrome (ARDS) patient. In most case scenarios, respondents reported not to change the fraction of inspired oxygen (FiO(2)) if SaO(2) was 90-95% or PaO2 was 12 kPa (90 mmHg).Conclusion: A representative sample of ICU clinicians from the Netherlands were concerned about oxygen-induced lung injury, and reported that they preferred PaO2 and SaO(2) targets in the lower physiological range and would adjust ventilation settings accordingly.Perioperative Medicine: Efficacy, Safety and Outcom

Attitudes of Dutch intensive care unit clinicians towards oxygen therapy

Background: Over the last decade, there has been an increasing awareness for the potential harm of the administration of too much oxygen. We aimed to describe self-reported attitudes towards oxygen therapy by clinicians from a large representative sample of intensive care units (ICUs) in the Netherlands.Methods: In April 2019, 36 ICUs in the Netherlands were approached and asked to send out a questionnaire (59 questions) to their nursing and medical staff (ICU clinicians) eliciting self-reported behaviour and attitudes towards oxygen therapy in general and in specific ICU case scenarios.Results: In total, 1361 ICU clinicians (71% nurses, 24% physicians) from 28 ICUs returned the questionnaire. Of responding ICU clinicians, 64% considered oxygen-induced lung injury to be a major concern. The majority of respondents considered a partial pressure of oxygen (PaO2) of 6-10 kPa (45-75 mmHg) and an arterial saturation (SaO(2)) of 85-90% as acceptable for 15 minutes, and a PaO2 7-To kPa (53-75 mmHg) and SaO(2) 90-95% as acceptable for 24-48 hours in an acute respiratory distress syndrome (ARDS) patient. In most case scenarios, respondents reported not to change the fraction of inspired oxygen (FiO(2)) if SaO(2) was 90-95% or PaO2 was 12 kPa (90 mmHg).Conclusion: A representative sample of ICU clinicians from the Netherlands were concerned about oxygen-induced lung injury, and reported that they preferred PaO2 and SaO(2) targets in the lower physiological range and would adjust ventilation settings accordingly.Perioperative Medicine: Efficacy, Safety and Outcom

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

BACKGROUND: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. METHODS: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. RESULTS: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. DISCUSSION: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research