Optimal Dosing and Timing of High-Dose Corticosteroid Therapy in Hospitalized Patients With COVID-19: Study Protocol for a Retrospective Observational Multicenter Study (SELECT).

BACKGROUND: In hospitalized patients with COVID-19, the dosing and timing of corticosteroids vary widely. Low-dose dexamethasone therapy reduces mortality in patients requiring respiratory support, but it remains unclear how to treat patients when this therapy fails. In critically ill patients, high-dose corticosteroids are often administered as salvage late in the disease course, whereas earlier administration may be more beneficial in preventing disease progression. Previous research has revealed that increased levels of various biomarkers are associated with mortality, and whole blood transcriptome sequencing has the ability to identify host factors predisposing to critical illness in patients with COVID-19. OBJECTIVE: Our goal is to determine the most optimal dosing and timing of corticosteroid therapy and to provide a basis for personalized corticosteroid treatment regimens to reduce morbidity and mortality in hospitalized patients with COVID-19. METHODS: This is a retrospective, observational, multicenter study that includes adult patients who were hospitalized due to COVID-19 in the Netherlands. We will use the differences in therapeutic strategies between hospitals (per protocol high-dose corticosteroids or not) over time to determine whether high-dose corticosteroids have an effect on the following outcome measures: mechanical ventilation or high-flow nasal cannula therapy, in-hospital mortality, and 28-day survival. We will also explore biomarker profiles in serum and bronchoalveolar lavage fluid and use whole blood transcriptome analysis to determine factors that influence the relationship between high-dose corticosteroids and outcome. Existing databases that contain routinely collected electronic data during ward and intensive care admissions, as well as existing biobanks, will be used. We will apply longitudinal modeling appropriate for each data structure to answer the research questions at hand. RESULTS: As of April 2023, data have been collected for a total of 1500 patients, with data collection anticipated to be completed by December 2023. We expect the first results to be available in early 2024. CONCLUSIONS: This study protocol presents a strategy to investigate the effect of high-dose corticosteroids throughout the entire clinical course of hospitalized patients with COVID-19, from hospital admission to the ward or intensive care unit until hospital discharge. Moreover, our exploration of biomarker and gene expression profiles for targeted corticosteroid therapy represents a first step towards personalized COVID-19 corticosteroid treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT05403359; https://clinicaltrials.gov/ct2/show/NCT05403359. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48183.

Environmental contamination with highly resistant microorganisms after relocating to a new hospital building with 100% single-occupancy rooms: A prospective observational before-and-after study with a three-year follow-up.

INTRODUCTION: Inanimate surfaces within hospitals can be a source of transmission for highly resistant microorganisms (HRMO). While many hospitals are transitioning to single-occupancy rooms, the effect of single-occupancy rooms on environmental contamination is still unknown. We aimed to determine differences in environmental contamination with HRMO between an old hospital building with mainly multiple-occupancy rooms and a new hospital building with 100% single-occupancy rooms, and the environmental contamination in the new hospital building during three years after relocating. METHODS: Environmental samples were taken twice in the old hospital, and fifteen times over a three-year period in the new hospital. Replicate Organism Direct Agar Contact-plates (RODACs) were used to determine colony forming units (CFU). Cotton swabs premoistened with PBS were used to determine presence of methicillin-resistant Staphylococcus aureus, carbapenemase-producing Pseudomonas aeruginosa, highly resistant Enterobacterales, carbapenem-resistant Acinetobacter baumannii, and vancomycin-resistant Enterococcus faecium. All identified isolates were subjected to whole genome sequencing (WGS) using Illumina technology. RESULTS: In total, 4993 hospital sites were sampled, 724 in the old and 4269 in the new hospital. CFU counts fluctuated during the follow-up period in the new hospital building, with lower CFU counts observed two- and three years after relocating, which was during the COVID-19 pandemic. The CFU counts in the new building were equal to or surpassed the CFU counts in the old hospital building. In the old hospital building, 24 (3.3%) sample sites were positive for 49 HRMO isolates, compared to five (0.1%) sample sites for seven HRMO isolates in the new building (P < 0.001). In the old hospital, 89.8% of HRMO were identified from the sink plug. In the new hospital, 71.4% of HRMO were identified from the shower drain, and no HRMO were found in sinks. DISCUSSION: Our results indicate that relocating to a new hospital building with 100% single-occupancy rooms significantly decreases HRMO in the environment. Given that environmental contamination is an important source for healthcare associated infections, this finding should be taken into account when considering hospital designs for renovations or the construction of hospitals.

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.

PEEP-FiO2 table versus EIT to titrate PEEP in mechanically ventilated patients with COVID-19-related ARDS.

RATIONALE: It is unknown how to titrate positive end-expiratory pressure (PEEP) in patients with COVID-19-related acute respiratory distress syndrome (ARDS). Guidelines recommend the one-size-fits-all PEEP-FiO2 table. In this retrospective cohort study, an electrical impedance tomography (EIT)-guided PEEP trial was used to titrate PEEP. OBJECTIVES: To compare baseline PEEP according to the high PEEP-FiO2 table and personalized PEEP following an EIT-guided PEEP trial. METHODS: We performed an EIT-guided decremental PEEP trial in patients with moderate-to-severe COVID-19-related ARDS upon intensive care unit admission. PEEP was set at the lowest PEEP above the intersection of curves representing relative alveolar overdistention and collapse. Baseline PEEP was compared with PEEP set according to EIT. We identified patients in whom the EIT-guided PEEP trial resulted in a decrease or increase in PEEP of ≥ 2 cmH2O. MEASUREMENTS AND MAIN RESULTS: We performed a PEEP trial in 75 patients. In 23 (31%) patients, PEEP was decreased ≥ 2 cmH2O, and in 24 (32%) patients, PEEP was increased ≥ 2 cmH2O. Patients in whom PEEP was decreased had improved respiratory mechanics and more overdistention in the non-dependent lung region at higher PEEP levels. These patients also had a lower BMI, longer time between onset of symptoms and intubation, and higher incidence of pulmonary embolism. Oxygenation improved in patients in whom PEEP was increased. CONCLUSIONS: An EIT-guided PEEP trial resulted in a relevant change in PEEP in 63% of patients. These results support the hypothesis that PEEP should be personalized in patients with ARDS.

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.

Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study.

BACKGROUND: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients. METHODS: We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 'wave' in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values. RESULTS: We included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80-0.84]) compared to the National early warning score (0.72 [0.69-0.74]) and the Modified early warning score (0.67 [0.65-0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [- 0.09 to 0.14], slope = 0.79 [0.73-0.86]). CONCLUSIONS: This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed.

Joint Modeling of Repeated Measurements of Different Biomarkers Predicts Mortality in COVID-19 Patients in the Intensive Care Unit.

Introduction: Predicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6, and suPAR on mortality in patients admitted to the ICU with COVID-19. Methods: This was a retrospective single center cohort study. Patients were included if they tested positive for SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker's value in a day compared to no change in the same period. Results: A total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6, and suPAR were significantly predictive of in-hospital mortality with HRs of 1.523 (1.012-6.540), 75.25 (1.116-6247), and 24.45 (1.696-1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6, and suPAR were 1.117 (1.03-1.639), 3.116 (1.029-9.963), and 2.319 (1.149-6.243) respectively. Conclusion: Joint models for the analysis of repeated measurements of PCT, suPAR, and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality.

Gravid uterine torsion after prone positioning in SARS-CoV2 (COVID-19)-related acute respiratory distress syndrome.

A multiparous pregnant patient was admitted to the intensive care unit in her third trimester of pregnancy for prone positioning mechanical ventilation after developing SARS-CoV2 (COVID-19)-related acute respiratory distress syndrome. Repositioning in left lateral tilt was followed by uterine contractions and cardiotocography alterations. Preterm caesarean section was performed based on persistent foetal tachycardia and suspected foetal distress, followed by a per-operative diagnosis of uterine levotorsion. This case report is the first to explore a potential causal link between prolonged prone positioning in late pregnancy and postural gravid uterine torsion and highlights the need for appropriate foetal monitoring during prone positioning mechanical ventilation support.

Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the first and the consecutive upsurges of the second wave of the COVID-19 pandemic in the Netherlands

BackgroundTo assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands.MethodsData from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and indicators of quality of care during the first two upsurges (N = 4215: October 5, 2020–January 31, 2021) and the final upsurge of the second wave, called the ‘third wave’ (N = 4602: February 1, 2021–June 30, 2021) were compared with those during the first wave (N = 2733, February–May 24, 2020).ResultsDuring the second and third wave, there were less patients treated with mechanical ventilation (58.1 and 58.2%) and vasoactive drugs (48.0 and 44.7%) compared to the first wave (79.1% and 67.2%, respectively). The occupancy rates as fraction of occupancy in 2019 (1.68 and 1.55 vs. 1.83), the numbers of ICU relocations (23.8 and 27.6 vs. 32.3%) and the mean length of stay at the ICU (HRs of ICU discharge = 1.26 and 1.42) were lower during the second and third wave. No difference in adjusted hospital mortality between the second wave and the first wave was found, whereas the mortality during the third wave was considerably lower (OR = 0.80, 95% CI [0.71–0.90]).ConclusionsThese data show favorable shifts in the treatment of COVID-19 patients at the ICU over time. The adjusted mortality decreased in the third wave. The high ICU occupancy rate early in the pandemic does probably not explain the high mortality associated with COVID-19.

Hyperinflammation Reduces Midazolam Metabolism in Critically Ill Adults with COVID-19.

BACKGROUND AND OBJECTIVE: Many patients treated for COVID-19 related acute respiratory distress syndrome in the intensive care unit are sedated with the benzodiazepine midazolam. Midazolam undergoes extensive metabolism by CYP3A enzymes, which may be inhibited by hyperinflammation. Therefore, an exaggerated proinflammatory response, as often observed in COVID-19, may decrease midazolam clearance. To develop a population pharmacokinetic model for midazolam in adult intensive care unit patients infected with COVID-19 and to assess the effect of inflammation, reflected by IL-6, on the pharmacokinetics of midazolam. METHODS: Midazolam blood samples were collected once a week between March 31 and April 30 2020. Patients were excluded if they concomitantly received CYP3A4 inhibitors, CYP3A4 inducers and/or continuous renal replacement therapy. Midazolam and metabolites were analyzed with an ultra-performance liquid chromatography-tandem mass spectrometry method. A population pharmacokinetic model was developed, using nonlinear mixed effects modelling. IL-6 and CRP, markers of inflammation, were analyzed as covariates. RESULTS: The data were described by a one-compartment model for midazolam and the metabolites 1-OH-midazolam and 1-OH-midazolam-glucuronide. The population mean estimate for midazolam clearance was 6.7 L/h (4.8-8.5 L/h). Midazolam clearance was reduced by increased IL-6 and IL-6 explained more of the variability within our patients than CRP. The midazolam clearance was reduced by 24% (6.7-5.1 L/h) when IL-6 increases from population median 116 to 300 pg/mL. CONCLUSIONS: Inflammation, reflected by high IL-6, reduces midazolam clearance in critically ill patients with COVID-19. This knowledge may help avoid oversedation, but further research is warranted.

High procalcitonin levels associated with increased intensive care unit admission and mortality in patients with a COVID-19 infection in the emergency department.

BACKGROUND: Patients with a severe COVID-19 infection often require admission at an intensive care unit (ICU) when they develop acute respiratory distress syndrome (ARDS). Hyperinflammation plays an important role in the development of ARDS in COVID-19. Procalcitonin (PCT) is a biomarker which may be a predictor of hyperinflammation. When patients with COVID-19 are in the emergency department (ED), elevated PCT levels could be associated with severe COVID-19 infections. The goal of this study is to investigate the association between PCT levels and severe COVID-19 infections in the ED. METHODS: This was a retrospective cohort study including patients with a confirmed COVID-19 infection who visited the ED of Erasmus Medical Center in Rotterdam, the Netherlands, between March and December 2020. The primary outcome was a severe COVID-19 infection, which was defined as patients who required ICU admission, all cause in-hospital mortality and mortality within 30 days after hospital discharge. PCT levels were measured during the ED visit. We used logistic regression to calculate the odds ratio (OR) with 95% confidence interval (95% CI) and corresponding area under the curve (AUC) of PCT on a severe COVID-19 infection, adjusting for bacterial coinfections, age, sex, comorbidities, C-reactive protein (CRP) and D-dimer. RESULTS: A total of 332 patients were included in the final analysis of this study, of which 105 patients reached the composite outcome of a severe COVID-19 infection. PCT showed an unadjusted OR of 4.19 (95%CI: 2.52-7.69) on a severe COVID-19 infection with an AUC of 0.82 (95% CI: 0.76-0.87). Corrected for bacterial coinfection, the OR of PCT was 4.05 (95% CI: 2.45-7.41). Adjusted for sex, bacterial coinfection, age any comorbidity, CRP and D-dimer, elevated PCT levels were still significantly associated with a severe COVID-19 infection with an adjusted OR of 2.11 (95% CI: 1.36-3.61). The AUC of this multivariable model was 0.85 (95%CI: 0.81-0.90). CONCLUSION: High PCT levels are associated with high rates of severe COVID-19 infections in patients with a COVID-19 infection in the ED. The routine measurement of PCT in patients with a COVID-19 infection in the ED may assist physicians in the clinical decision making process regarding ICU disposition.

Advanced respiratory monitoring in mechanically ventilated patients with coronavirus disease 2019-associated acute respiratory distress syndrome.

PURPOSE OF REVIEW: To summarize the current knowledge about the application of advanced monitoring techniques in coronavirus disease 2019 (COVID-19). RECENT FINDINGS: Due to the heterogeneity between patients, management of COVID-19 requires daily monitoring of and/or aeration and inspiratory effort. Electrical impedance tomography can be used to optimize positive end-expiratory pressure, monitor the response to changes in treatment or body position and assess pulmonary perfusion and ventilation/perfusion matching. Lung ultrasound is more readily available and can be used to measure and monitor recruitment, provide an indication of diaphragm function and pulmonary perfusion disturbances. Esophageal pressure measurements enable the calculation of the transpulmonary pressure and inspiratory effort in order to prevent excessive stress on the lung. While esophageal pressure measurements are the golden standard in determining inspiratory effort, alternatives like P0.1, negative pressure swing during a single airway occlusion and change in central venous pressure are more readily available and capable of diagnosing extreme inspiratory efforts. SUMMARY: Although there is little data on the effectiveness of advanced monitoring techniques in COVID-19, regular monitoring should be a central part of the management of COVID-19-related acute respiratory distress syndrome (C-ARDS).

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.

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.

Seasonal coronavirus–specific B cells with limited SARS-CoV-2 cross-reactivity dominate the IgG response in severe COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease 2019 (COVID-19). Little is known about the interplay between preexisting immunity to endemic seasonal coronaviruses and the development of a SARS-CoV-2-specific IgG response. We investigated the kinetics, breadth, magnitude, and level of cross-reactivity of IgG antibodies against SARS-CoV-2 and heterologous seasonal and epidemic coronaviruses at the clonal level in patients with mild or severe COVID-19 as well as in disease control patients. We assessed antibody reactivity to nucleocapsid and spike antigens and correlated this IgG response to SARS-CoV-2 neutralization. Patients with COVID-19 mounted a mostly type-specific SARS-CoV-2 response. Additionally, IgG clones directed against a seasonal coronavirus were boosted in patients with severe COVID-19. These boosted clones showed limited cross-reactivity and did not neutralize SARS-CoV-2. These findings indicate a boost of poorly protective CoV-specific antibodies in patients with COVID-19 that correlated with disease severity, revealing "original antigenic sin."

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.