Airborne virus shedding of the alpha, delta, omicron SARS-CoV-2 variants and influenza virus in hospitalized patients.

Airborne transmission is an important transmission route for the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological data indicate that certain SARS-CoV-2 variants, like the omicron variant, are associated with higher transmissibility. We compared virus detection in air samples between hospitalized patients infected with different SARS-CoV-2 variants or influenza virus. The study was performed during three separate time periods in which subsequently the alpha, delta, and omicron SARS-CoV-2 variants were predominant. In total, 79 patients with coronavirus disease 2019 (COVID-19) and 22 patients with influenza A virus infection were included. Collected air samples were positive in 55% of patients infected with the omicron variant in comparison to 15% of those infected with the delta variant (p < 0.01). In multivariable analysis, the SARS-CoV-2 omicron BA.1/BA.2 variant (as compared to the delta variant) and the viral load in nasopharynx were both independently associated with air sample positivity, but the alpha variant and COVID-19 vaccination were not. The proportion of positive air samples patients infected with the influenza A virus was 18%. In conclusion, the higher air sample positivity rate of the omicron variant compared to previous SARS-CoV-2 variants may partially explain the higher transmission rates seen in epidemiological trends.

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.

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.

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients.

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome. Funding: Bronner P. Gonçalves, Peter Horby, Gail Carson, Piero L. Olliaro, Valeria Balan, Barbara Wanjiru Citarella, and research costs were supported by the UK Foreign, Commonwealth and Development Office (FCDO) and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z]; and Janice Caoili and Madiha Hashmi were supported by the UK FCDO and Wellcome [222048/Z/20/Z]. Peter Horby, Gail Carson, Piero L. Olliaro, Kalynn Kennon and Joaquin Baruch were supported by the Bill & Melinda Gates Foundation [OPP1209135]; Laura Merson was supported by University of Oxford's COVID-19 Research Response Fund - with thanks to its donors for their philanthropic support. Matthew Hall was supported by a Li Ka Shing Foundation award to Christophe Fraser. Moritz U.G. Kraemer was supported by the Branco Weiss Fellowship, Google.org, the Oxford Martin School, the Rockefeller Foundation, and the European Union Horizon 2020 project MOOD (#874850). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Contributions from Srinivas Murthy, Asgar Rishu, Rob Fowler, James Joshua Douglas, François Martin Carrier were supported by CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and coordinated out of Sunnybrook Research Institute. Contributions from Evert-Jan Wils and David S.Y. Ong were supported by a grant from foundation Bevordering Onderzoek Franciscus; and Andrea Angheben by the Italian Ministry of Health "Fondi Ricerca corrente-L1P6" to IRCCS Ospedale Sacro Cuore-Don Calabria. The data contributions of J.Kenneth Baillie, Malcolm G. Semple, and Ewen M. Harrison were supported by grants from the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. All funders of the ISARIC Clinical Characterisation Group are listed in the appendix.

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.

Psychological distress and health-related quality of life in patients after hospitalization during the COVID-19 pandemic: A single-center, observational study

Introduction: Illnesses requiring hospitalization are known to negatively impact psychological well-being and health-related quality of life (HRQoL) after discharge. The impact of hospitalization during the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) pandemic on psychological well-being and health-related quality of life is expected to be higher due to the exceptional circumstances within and outside the hospital during the pandemic surge. The objective of this study was to quantify psychological distress up to three months after discharge in patients hospitalized during the first coronavirus disease 2019 (COVID-19) pandemic wave. We also aimed to determine HRQoL, to explore predictors for psychological distress and HRQoL, and to examine whether psychological distress was higher in COVID-19 confirmed patients, and in those treated in Intensive Care Units (ICUs). Methods: In this single-center, observational cohort study, adult patients hospitalized with symptoms suggestive of COVID-19 between March 16 and April 28, 2020, were enrolled. Patients were stratified in analyses based on SARS-CoV-2 PCR results and the necessity for ICU treatment. The primary outcome was psychological distress, expressed as symptoms of post-traumatic stress disorder (PTSD), anxiety, and depression, up to three months post-discharge. Health-related quality of life (HRQoL) was the secondary outcome. Exploratory outcomes comprised predictors for psychological distress and HRQoL. Results: 294 of 622 eligible patients participated in this study (median age 64 years, 36% female). 16% and 13% of these patients reported probable PTSD, 29% and 20% probable anxiety, and 32% and 24% probabledepression at one and three months after hospital discharge, respectively. ICU patients reported less frequently probable depression, but no differences were found in PTSD, anxiety, or overall HRQoL. COVID-19 patients had a worse physical quality of life one month after discharge, and ICU patients reported a better mental quality of life three months after discharge. PTSD severity was predicted by time after discharge and being Caucasian. Severity of anxiety was predicted by time after discharge and being Caucasian. Depression severity was predicted by time after discharge and educational level. Conclusion: COVID-19 suspected patients hospitalized during the pandemic frequently suffer from psychological distress and poor health-related quality of life after hospital discharge. Non-COVID-19 and non-ICU patients appear to be at least as affected as COVID-19 and ICU patients, underscoring that (post-)hospital pandemic care should not predominantly focus on COVID-19 infected patients. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

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.

Clinical characteristics, risk factors and outcomes in patients with severe COVID-19 registered in the ISARIC WHO clinical characterisation protocol: a prospective, multinational, multicentre, observational study

Due to the large number of patients with severe COVID-19, many were treated outside of the traditional walls of the ICU, and in many cases, by personnel who were not trained in critical care. The clinical characteristics and the relative impact of caring for severe COVID-19 patients outside of the ICU is unknown. This was a multinational, multicentre, prospective cohort study embedded in the ISARIC WHO COVID-19 platform. Severe COVID-19 patients were identified as those admitted to an ICU and/or those treated with one of the following treatments: invasive or non-invasive mechanical ventilation, high-flow nasal cannula, inotropes, and vasopressors. A logistic Generalised Additive Model was used to compare clinical outcomes among patients admitted and not to the ICU. A total of 40 440 patients from 43 countries and six continents were included in this analysis. Severe COVID-19 patients were frequently male (62.9%), older adults (median [IQR], 67 years [55, 78]), and with at least one comorbidity (63.2%). The overall median (IQR) length of hospital stay was 10 days (5–19) and was longer in patients admitted to an ICU than in those that were cared for outside of ICU (12 [6–23] versus 8 [4–15] days, p<0.0001). The 28-day fatality ratio was lower in ICU-admitted patients (30.7% [5797/18831] versus 39.0% [7532/19295], p<0.0001). Patients admitted to an ICU had a significantly lower probability of death than those who were not (adjusted OR:0.70, 95%CI: 0.65-0.75, p<0.0001). Patients with severe COVID-19 admitted to an ICU had significantly lower 28-day fatality ratio than those cared for outside of an ICU.

Intensive Care Unit-Specific Virtual Reality for Critically Ill Patients With COVID-19: Multicenter Randomized Controlled Trial.

BACKGROUND: Although psychological sequelae after intensive care unit (ICU) treatment are considered quite intrusive, robustly effective interventions to treat or prevent these long-term sequelae are lacking. Recently, it was demonstrated that ICU-specific virtual reality (ICU-VR) is a feasible and acceptable intervention with potential mental health benefits. However, its effect on mental health and ICU aftercare in COVID-19 ICU survivors is unknown. OBJECTIVE: This study aimed to explore the effects of ICU-VR on mental health and on patients' perceived quality of, satisfaction with, and rating of ICU aftercare among COVID-19 ICU survivors. METHODS: This was a multicenter randomized controlled trial. Patients were randomized to either the ICU-VR (intervention) or the control group. All patients were invited to an COVID-19 post-ICU follow-up clinic 3 months after hospital discharge, during which patients in the intervention group received ICU-VR. One month and 3 months later (4 and 6 months after hospital discharge), mental health, quality of life, perceived quality, satisfaction with, and rating of ICU aftercare were scored using questionnaires. RESULTS: Eighty-nine patients (median age 58 years; 63 males, 70%) were included. The prevalence and severity of psychological distress were limited throughout follow-up, and no differences in psychological distress or quality of life were observed between the groups. ICU-VR improved satisfaction with (mean score 8.7, SD 1.6 vs 7.6, SD 1.6 [ICU-VR vs control]; t64=-2.82, P=.006) and overall rating of ICU aftercare (mean overall rating of aftercare 8.9, SD 0.9 vs 7.8, SD 1.7 [ICU-VR vs control]; t64=-3.25; P=.002) compared to controls. ICU-VR added to the quality of ICU aftercare according to 81% of the patients, and all patients would recommend ICU-VR to other ICU survivors. CONCLUSIONS: ICU-VR is a feasible and acceptable innovative method to improve satisfaction with and rating of ICU aftercare and adds to its perceived quality. We observed a low prevalence of psychological distress after ICU treatment for COVID-19, and ICU-VR did not improve psychological recovery or quality of life. Future research is needed to confirm our results in other critical illness survivors to potentially facilitate ICU-VR's widespread availability and application during follow-up. TRIAL REGISTRATION: Netherlands Trial Register NL8835; https://www.trialregister.nl/trial/8835. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-021-05271-z.

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.

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.

Virtual reality for relatives of ICU patients to improve psychological sequelae: study protocol for a multicentre, randomised controlled trial.

INTRODUCTION: Intensive care unit (ICU) admission of a relative might lead to psychological distress and complicated grief (post-intensive care syndrome-family; PICS-F). Evidence suggests that increased distress during ICU stay increases risk of PICS-F, resulting in difficulty returning to their normal lives after the ICU experience. Effective interventions to improve PICS-F are currently lacking. In the present trial, we hypothesised that information provision using ICU-specific Virtual Reality for Family members/relatives (ICU-VR-F) may improve understanding of the ICU and subsequently improve psychological well-being and quality of life in relatives of patients admitted to the ICU. METHODS AND ANALYSIS: This multicentre, clustered randomised controlled trial will be conducted from January to December 2021 in the mixed medical-surgical ICUs of four hospitals in Rotterdam, the Netherlands. We aim to include adult relatives of 160 ICU patients with an expected ICU length of stay over 72 hours. Participants will be randomised clustered per patient in a 1:1 ratio to either the intervention or control group. Participants allocated to the intervention group will receive ICU-VR-F, an information video that can be watched in VR, while the control group will receive usual care. Initiation of ICU-VR-F will be during their hospital visit unless participants cannot visit the hospital due to COVID-19 regulations, then VR can be watched digitally at home. The primary objective is to study the effect of ICU-VR-F on psychological well-being and quality of life up to 6 months after the patients' ICU discharge. The secondary outcome is the degree of understanding of ICU treatment and ICU modalities. ETHICS AND DISSEMINATION: The Medical Ethics Committee of the Erasmus Medical Centre, Rotterdam, the Netherlands, approved the study and local approval was obtained from each participating centre (NL73670.078.20). Our findings will be disseminated by presentation of the results at (inter)national conferences and publication in scientific, peer-reviewed journals. TRIAL REGISTRATION NUMBER: Netherlands Trial Register (TrialRegister.nl, NL9220).

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.

Early high-dose vitamin C in post-cardiac arrest syndrome (VITaCCA): study protocol for a randomized, double-blind, multi-center, placebo-controlled trial.

BACKGROUND: High-dose intravenous vitamin C directly scavenges and decreases the production of harmful reactive oxygen species (ROS) generated during ischemia/reperfusion after a cardiac arrest. The aim of this study is to investigate whether short-term treatment with a supplementary or very high-dose intravenous vitamin C reduces organ failure in post-cardiac arrest patients. METHODS: This is a double-blind, multi-center, randomized placebo-controlled trial conducted in 7 intensive care units (ICUs) in The Netherlands. A total of 270 patients with cardiac arrest and return of spontaneous circulation will be randomly assigned to three groups of 90 patients (1:1:1 ratio, stratified by site and age). Patients will intravenously receive a placebo, a supplementation dose of 3 g of vitamin C or a pharmacological dose of 10 g of vitamin C per day for 96 h. The primary endpoint is organ failure at 96 h as measured by the Resuscitation-Sequential Organ Failure Assessment (R-SOFA) score at 96 h minus the baseline score (delta R-SOFA). Secondary endpoints are a neurological outcome, mortality, length of ICU and hospital stay, myocardial injury, vasopressor support, lung injury score, ventilator-free days, renal function, ICU-acquired weakness, delirium, oxidative stress parameters, and plasma vitamin C concentrations. DISCUSSION: Vitamin C supplementation is safe and preclinical studies have shown beneficial effects of high-dose IV vitamin C in cardiac arrest models. This is the first RCT to assess the clinical effect of intravenous vitamin C on organ dysfunction in critically ill patients after cardiac arrest. TRIAL REGISTRATION: ClinicalTrials.gov NCT03509662. Registered on April 26, 2018. https://clinicaltrials.gov/ct2/show/NCT03509662 European Clinical Trials Database (EudraCT): 2017-004318-25. Registered on June 8, 2018. https://www.clinicaltrialsregister.eu/ctr-search/trial/2017-004318-25/NL.

Psychologic Distress and Quality of Life After ICU Treatment for Coronavirus Disease 2019: A Multicenter, Observational Cohort Study.

To quantify short- and long-term psychologic distress, that is, symptoms of posttraumatic stress disorder, anxiety, and depression, and the health-related quality of life in coronavirus disease 2019 ICU survivors. DESIGN: A prospective, observational cohort study. SETTING: Postcoronavirus disease 2019 clinics of three hospitals in Rotterdam, the Netherlands. PATIENTS: Adult patients admitted for coronavirus disease 2019 to the ICU, who visited the postcoronavirus disease 2019 follow-up clinic. MEASURES AND MAIN RESULTS: The primary outcomes were psychologic distress and overall and mental health-related quality of life, assessed using the Impact of Event Scale-Revised, Hospital Anxiety and Depression Scale, Short-Form 36, and European Quality of Life 5D, 6 weeks, 3 months, and 6 months post hospital discharge. Second, we compared 3-month psychologic and mental health-related quality of life outcomes with a historical critical illness survivor cohort and overall and mental health-related quality of life with the Dutch population. We included 118 patients with a median age of 61 years (95% range, 36-77 yr) of whom 79 (68%) were male. At 6 weeks, 13 patients (23%) reported psychologic distress, copresence of probable psychiatric disorders was common, and no decline in psychologic distress was observed throughout follow-up. Coronavirus disease 2019 patients tend to suffer less from posttraumatic stress disorder and reported less severe symptoms of anxiety (Hospital Anxiety and Depression Scale Anxiety Score: 3 [0-17] vs 5 [0-16]; estimated mean difference 2.3 [95% CI, 0.0-4.7]; p = 0.05) and depression (Hospital Anxiety and Depression Scale Depression Score: 3 [0-15] vs 5 [0-16]; estimated mean difference 2.4 [95% CI, 0.1-2.4]; p = 0.04) than the historical critical illness cohort. Overall and mental health-related quality of life increased over time. Coronavirus disease 2019 ICU survivors reported better mental health-related quality of life than our historical cohort, but overall and mental health-related quality of life was still poorer than the Dutch population. CONCLUSIONS: Psychologic distress was common in coronavirus disease 2019 ICU survivors and remained similar until 6 months after hospital discharge. Health-related quality of life increased over time and was higher than in a historical cohort, but was lower than in the Dutch population. Our findings highlight that coronavirus disease 2019 ICU survivors should be monitored after ICU treatment to detect possible psychologic distress.

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.

Effect of intensive care unit-specific virtual reality (ICU-VR) to improve psychological well-being and quality of life in COVID-19 ICU survivors: a study protocol for a multicentre, randomized controlled trial.

BACKGROUND: The SARS-CoV-2 outbreak has resulted in a tremendous increase in hospital and intensive care unit (ICU) admissions all over the world. Patients with severe coronavirus disease 2019 (COVID-19) warranting ICU treatment usually have prolonged mechanical ventilation and are expected to be prone to develop psychological impairments, such as post-traumatic stress disorder (PTSD), anxiety and depression, which negatively impact quality of life. To date, no effective treatment strategy is available. In the current trial, we aim to assess the effect of an ICU-specific virtual reality (ICU-VR) intervention on psychological well-being and quality of life after COVID-19 ICU treatment. METHODS: In this multicentre, randomized controlled trial, we aim to examine whether COVID-19-specific ICU-VR, offered 3 months after hospital discharge, improves psychological well-being and quality of life. Secondary objectives are, firstly, to examine the intra-group changes in psychological well-being and quality of life and the inter-group differences in psychological well-being and quality of life during follow-up, up to 12 months after hospital discharge, and secondly, to examine patients' satisfaction with and rating of ICU care and aftercare and patients' perspectives on ICU-VR. Eighty adult patients treated for COVID-19 in the mixed-surgical ICUs of four hospitals in Rotterdam, the Netherlands, will be included and randomized (1:1) to either early or late ICU-VR between June 29 and December 31, 2020. Patients randomized to early ICU-VR will receive the ICU-VR intervention during an outpatient clinic visit 3 months after hospital discharge, whereas patients randomized to late ICU-VR will receive ICU-VR 6 months after hospital discharge. Primary outcomes of this study are psychological well-being, assessed using the Impact of Event Scale-Revised (IES-R) and the Hospital Anxiety and Depression Scale (HADS), and quality of life, assessed using the European Quality of Life 5 Dimensions (EQ-5D) and RAND-36 questionnaires, up to 6 months after hospital discharge. DISCUSSION: Currently, an effective treatment for psychological sequelae after ICU treatment for specific illnesses is unavailable. Results from this study will provide insight whether virtual reality is a modality that can be used in ICU aftercare to improve psychological well-being and quality of life, or satisfaction, after ICU treatment for specific illnesses such as COVID-19. TRIAL REGISTRATION: This trial has been retrospectively registered on the Netherlands Trial Register on August 14, 2020 ( NL8835 ).

Intensive Care Unit-Specific Virtual Reality for Psychological Recovery After ICU Treatment for COVID-19; A Brief Case Report.

A substantial number of ICU survivors are expected due to the SARS-CoV-2 outbreak, who are at risk for psychological impairments, such as post-traumatic stress disorder (PTSD), anxiety, and depression. We designed a COVID-19 intensive care unit-specific virtual reality (ICU-VR) intervention and tested it on one of our COVID-19 patients. The impact of event scale-revised and the hospital anxiety and depression scale showed that this patient suffered from PTSD, anxiety, and depression on the day of the intervention. One week after receiving ICU-VR, levels of PTSD, anxiety and depression had normalized, and stayed normalized until 6 months after discharge. In conclusion, innovative technologies, such as VR, have the potential to improve psychological rehabilitation, and should therefore be considered by clinicians for the treatment of ICU-related psychological sequelae after COVID-19.