Desflurane is risk factor for postoperative delirium in older patients' independent from intraoperative burst suppression duration.

Background: Postoperative Delirium (POD) is the most frequent neurocognitive complication after general anesthesia in older patients. The development of POD is associated with prolonged periods of burst suppression activity in the intraoperative electroencephalogram (EEG). The risk to present burst suppression activity depends not only on the age of the patient but is also more frequent during propofol anesthesia as compared to inhalative anesthesia. The aim of our study is to determine, if the risk to develop POD differs depending on the anesthetic agent given and if this correlates with a longer duration of intraoperative burst suppression. Methods: In this secondary analysis of the SuDoCo trail [ISRCTN 36437985] 1277 patients, older than 60 years undergoing general anesthesia were included. We preprocessed and analyzed the raw EEG files from each patient and evaluated the intraoperative burst suppression duration. In a logistic regression analysis, we assessed the impact of burst suppression duration and anesthetic agent used for maintenance on the risk to develop POD. Results: 18.7% of patients developed POD. Burst suppression duration was prolonged in POD patients (POD 27.5 min ± 21.3 min vs. NoPOD 21.4 ± 16.2 min, p < 0.001), for each minute of prolonged intraoperative burst suppression activity the risk to develop POD increased by 1.1% (OR 1.011, CI 95% 1.000-1.022, p = 0.046). Burst suppression duration was prolonged under propofol anesthesia as compared to sevoflurane and desflurane anesthesia (propofol 32.5 ± 20.3 min, sevoflurane 17.1 ± 12.6 min and desflurane 20.1 ± 16.0 min, p < 0.001). However, patients receiving desflurane anesthesia had a 1.8fold higher risk to develop POD, as compared to propofol anesthesia (OR 1.766, CI 95% 1.049-2.974, p = 0.032). Conclusion: We found a significantly increased risk to develop POD after desflurane anesthesia in older patients, even though burst suppression duration was shorter under desflurane anesthesia as compared to propofol anesthesia. Our finding might help to explain some discrepancies in studies analyzing the impact of burst suppression duration and EEG-guided anesthesia on the risk to develop POD.

Automated Monitoring of Adherence to Evidenced-Based Clinical Guideline Recommendations: Design and Implementation Study.

BACKGROUND: Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients. OBJECTIVE: This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype's utility in treatment recommendations. METHODS: We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital. RESULTS: We designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources-based format for the representation of clinical practice guidelines to provide an interoperable, standards-based guideline recommendation exchange format. CONCLUSIONS: Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the system.

A proteomic survival predictor for COVID-19 patients in intensive care.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.

Standardized High-Quality Processes for End-of-Life-Decision Making in the Intensive Care Unit Remain Robust during an Unprecedented New Pandemic-A Single-Center Experience.

Due to the global COVID-19 pandemic, a concomitant increase in awareness for end-of-life decisions (EOLDs) and advance care planning has been noted. Whether the dynamic pandemic situation impacted EOLD-processes on the intensive care unit (ICU) and patient-sided advance care planning in Germany is unknown. This is a retrospective analysis of all deceased patients of surgical ICUs of a university medical center from March 2020 to July 2021. All included ICUs had established standardized protocols and documentation for EOLD-related aspects of ICU therapy. The frequency of EOLDs and advance directives and the process of EOLDs were analyzed (No. of ethical approval EA2/308/20). A total number of 319 (85.5%) of all deceased patients received an EOLD. Advance directives were possessed by 83 (22.3%) of the patients and a precautionary power of attorney by 92 (24.7%) of the patients. There was no difference in the frequency of EOLDs and patient-sided advance care planning between patients with COVID-19 and non-COVID-19 patients. In addition, no differences in frequencies of do-not-resuscitate orders, withholding or withdrawing of intensive care medicine therapeutic approaches, timing of EOLDs, and participation of senior ICU attendings in EOLDs were noted between patients with COVID-19 and non-COVID-19 patients. Documentation of family conferences occurred more often in deceased patients with COVID-19 compared to non-COVID-19 patients (COVID-19: 80.0% vs. non-COVID-19: 56.8, p = 0.001). Frequency of EOLDs and completion rates of advance directives remained unchanged during the pandemic compared to pre-pandemic years. The EOLD process did not differ between patients with COVID-19 and non-COVID-19 patients. Institutional standard procedures might contribute to support the robustness of EOLD-making processes during unprecedented medical emergencies, such as new pandemic diseases.

Living with Chronic Pain During the COVID-19 Pandemic: A Qualitative Analysis.

Purpose: COVID-19 pandemic containment measures have led to changes in various areas of life, including restrictions on health care. Patients with chronic pain may have faced an increased burden during pandemic and the resources of this vulnerable population are unknown. Therefore, a qualitative study was conducted to understand how people with chronic pain have experienced the course of the pandemic. Patients and Methods: Twenty semi-structured telephone interviews were conducted six months after the initial lockdown in Germany. The participants were patients with chronic pain who exhibited varying changes in their pain during the first German lockdown, recruited from a German outpatient pain clinic at a Department of Anesthesiology and Intensive Care. The semi-structured interview guidelines were designed to explore how patients with chronic pain experienced their pain during the pandemic, how they coped, and how they experienced pain management during this time. The interview recordings were transcribed verbatim and coded using the qualitative content analysis method. Results: Four themes emerged from the results: differential impact on pain experience, difficulty coping with pain, supportive pain management, and endurance. Conclusion: During this uncertain time, it was particularly important to maintain pain treatment in order to establish a sense of safety and stability. This underscores the special role of maintaining therapeutic contact during a pandemic and the potentially special role of telemedicine.

CytoSorb Rescue for COVID-19 Patients With Vasoplegic Shock and Multiple Organ Failure: A Prospective, Open-Label, Randomized Controlled Pilot Study.

OBJECTIVES: To investigate the effect of extracorporeal cytokine reduction by CytoSorb (CytoSorbents, Monmouth Junction, NJ) on COVID-19-associated vasoplegic shock. DESIGN: Prospective, randomized controlled pilot study. SETTING: Eight ICUs at three sites of the tertiary-care university hospital Charité-Universitätsmedizin Berlin. PATIENTS: COVID-19 patients with vasoplegic shock requiring norepinephrine greater than 0.2 µg/kg/min, C-reactive protein greater than 100 mg/L, and indication for hemodialysis. INTERVENTIONS: Randomization of 1:1 to receive CytoSorb for 3-7 days or standard therapy. To account for inadvertent removal of antibiotics, patients in the treatment group received an additional dose at each adsorber change. MEASUREMENTS AND MAIN RESULTS: The primary endpoint was time until resolution of vasoplegic shock, estimated by Cox-regression. Secondary endpoints included mortality, interleukin-6 concentrations, and catecholamine requirements. The study was registered in the German Registry of Clinical Trials (DRKS00021447). From November 2020 to March 2021, 50 patients were enrolled. Twenty-three patients were randomized to receive CytoSorb and 26 patients to receive standard of care. One patient randomized to cytokine adsorption was excluded due to withdrawal of informed consent. Resolution of vasoplegic shock was observed in 13 of 23 patients (56.5%) in the CytoSorb and 12 of 26 patients (46.2%) in the control group after a median of 5 days (interquartile range [IQR], 4-5 d) and 4 days (IQR, 3-5 d). The hazard ratio (HR) for the primary endpoint, adjusted for the predefined variables age, gender, extracorporeal membrane oxygenation-therapy, or time from shock onset to study inclusion was HR, 1.23 (95% CI, 0.54-2.79); p = 0.63. The mortality rate was 78% in the CytoSorb and 73% in the control group (unadjusted HR, 1.17 [95% CI, 0.61-2.23]; p = 0.64). The effects on inflammatory markers, catecholamine requirements, and the type and rates of adverse events were similar between the groups. CONCLUSIONS: In severely ill COVID-19 patients, CytoSorb did not improve resolution of vasoplegic shock or predefined secondary endpoints.

A Decade of Post-Intensive Care Syndrome: A Bibliometric Network Analysis.

Background and Objectives: In 2012, the umbrella term post-intensive care syndrome (PICS) was introduced to capture functional long-term impairments of survivors of critical illness. We present a bibliometric network analysis of the PICS research field. Materials and Methods: The Web of Science core database was searched for articles published in 2012 or later using 'post-intensive care syndrome' and variant spellings. Using VOSviewer, we computed co-authorship networks of countries, institutions, and authors, as well as keyword co-occurrence networks. We determined each country's relative research effort and Category Normalized Citation Index over time and analyzed the 100 most-cited articles with respect to article type, country of origin, and publishing journal. Results: Our search yielded 379 articles, of which 373 were analyzed. Annual PICS research output increased from 11 (2012) to 95 articles (2020). Most PICS research originates from the US, followed by England, Australia, the Netherlands, and Germany. We found various collaborations between countries, institutions, and authors, with recent collaborative networks of English and Australian institutions. Article keywords cover aspects of cognitive, mental health, and physical impairments, and more recently, COVID-19. Only a few keywords and articles pertained to PICS prevention and treatment. Conclusions: Our analysis of Web of Science-indexed PICS articles highlights the stark increase in PICS research output in recent years, primarily originating from US- and Europe-based authors and institutions. Despite the research field's growth, knowledge gaps with respect to PICS prevention and treatment remain.

Observational study of changes in utilization and outcomes in mechanical ventilation in COVID-19.

BACKGROUND: The role of non-invasive ventilation (NIV) in severe COVID-19 remains a matter of debate. Therefore, the utilization and outcome of NIV in COVID-19 in an unbiased cohort was determined. AIM: The aim was to provide a detailed account of hospitalized COVID-19 patients requiring non-invasive ventilation during their hospital stay. Furthermore, differences of patients treated with NIV between the first and second wave are explored. METHODS: Confirmed COVID-19 cases of claims data of the Local Health Care Funds with non-invasive and/or invasive mechanical ventilation (MV) in the spring and autumn pandemic period in 2020 were comparable analysed. RESULTS: Nationwide cohort of 17.023 cases (median/IQR age 71/61-80 years, 64% male) 7235 (42.5%) patients primarily received IMV without NIV, 4469 (26.3%) patients received NIV without subsequent intubation, and 3472 (20.4%) patients had NIV failure (NIV-F), defined by subsequent endotracheal intubation. The proportion of patients who received invasive MV decreased from 75% to 37% during the second period. Accordingly, the proportion of patients with NIV exclusively increased from 9% to 30%, and those failing NIV increased from 9% to 23%. Median length of hospital stay decreased from 26 to 21 days, and duration of MV decreased from 11.9 to 7.3 days. The NIV failure rate decreased from 49% to 43%. Overall mortality increased from 51% versus 54%. Mortality was 44% with NIV-only, 54% with IMV and 66% with NIV-F with mortality rates steadily increasing from 62% in early NIV-F (day 1) to 72% in late NIV-F (>4 days). CONCLUSIONS: Utilization of NIV rapidly increased during the autumn period, which was associated with a reduced duration of MV, but not with overall mortality. High NIV-F rates are associated with increased mortality, particularly in late NIV-F.

Design of Clinical Trials Evaluating Sedation in Critically Ill Adults Undergoing Mechanical Ventilation: Recommendations From Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research (SCEPTER) Recommendation III.

OBJECTIVES: Clinical trials evaluating the safety and effectiveness of sedative medication use in critically ill adults undergoing mechanical ventilation differ considerably in their methodological approach. This heterogeneity impedes the ability to compare results across studies. The Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research Recommendations convened a meeting of multidisciplinary experts to develop recommendations for key methodologic elements of sedation trials in the ICU to help guide academic and industry clinical investigators. DESIGN: A 2-day in-person meeting was held in Washington, DC, on March 28-29, 2019, followed by a three-round, online modified Delphi consensus process. PARTICIPANTS: Thirty-six participants from academia, industry, and the Food and Drug Administration with expertise in relevant content areas, including two former ICU patients attended the in-person meeting, and the majority completed an online follow-up survey and participated in the modified Delphi process. MEASUREMENTS AND MAIN RESULTS: The final recommendations were iteratively refined based on the survey results, participants' reactions to those results, summaries written by panel moderators, and a review of the meeting transcripts made from audio recordings. Fifteen recommendations were developed for study design and conduct, subject enrollment, outcomes, and measurement instruments. Consensus recommendations included obtaining input from ICU survivors and/or their families, ensuring adequate training for personnel using validated instruments for assessments of sedation, pain, and delirium in the ICU environment, and the need for methodological standardization. CONCLUSIONS: These recommendations are intended to assist researchers in the design, conduct, selection of endpoints, and reporting of clinical trials involving sedative medications and/or sedation protocols for adult ICU patients who require mechanical ventilation. These recommendations should be viewed as a starting point to improve clinical trials and help reduce methodological heterogeneity in future clinical trials.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia.

In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86-0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61-0.65]), SAPS2 (0.72 [95% CI 0.71-0.74]) and SOFA (0.76 [95% CI 0.75-0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73-0.78]) and Wang (laboratory: 0.62 [95% CI 0.59-0.65]; clinical: 0.56 [95% CI 0.55-0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70-0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.

Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions.

BACKGROUND: As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients. OBJECTIVE: This study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies. METHODS: This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation. RESULTS: We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed). CONCLUSIONS: Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.

CD169/SIGLEC1 is expressed on circulating monocytes in COVID-19 and expression levels are associated with disease severity.

Coronavirus disease 2019 (COVID-19) is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Type I interferons are important in the defense of viral infections. Recently, neutralizing IgG auto-antibodies against type I interferons were found in patients with severe COVID-19 infection. Here, we analyzed expression of CD169/SIGLEC1, a well described downstream molecule in interferon signaling, and found increased monocytic CD169/SIGLEC1 expression levels in patients with mild, acute COVID-19, compared to patients with severe disease. We recommend further clinical studies to evaluate the value of CD169/SIGLEC1 expression in patients with COVID-19 with or without auto-antibodies against type I interferons.

Critical Illness and Systemic Inflammation Are Key Risk Factors of Severe Acute Kidney Injury in Patients With COVID-19.

INTRODUCTION: Acute kidney injury (AKI) is an important complication in COVID-19, but its precise etiology has not fully been elucidated. Insights into AKI mechanisms may be provided by analyzing the temporal associations of clinical parameters reflecting disease processes and AKI development. METHODS: We performed an observational cohort study of 223 consecutive COVID-19 patients treated at 3 sites of a tertiary care referral center to describe the evolvement of severe AKI (Kidney Disease: Improving Global Outcomes stage 3) and identify conditions promoting its development. Descriptive statistics and explanatory multivariable Cox regression modeling with clinical parameters as time-varying covariates were used to identify risk factors of severe AKI. RESULTS: Severe AKI developed in 70 of 223 patients (31%) with COVID-19, of which 95.7% required kidney replacement therapy. Patients with severe AKI were older, predominantly male, had more comorbidities, and displayed excess mortality. Severe AKI occurred exclusively in intensive care unit patients, and 97.3% of the patients developing severe AKI had respiratory failure. Mechanical ventilation, vasopressor therapy, and inflammatory markers (serum procalcitonin levels and leucocyte count) were independent time-varying risk factors of severe AKI. Increasing inflammatory markers displayed a close temporal association with the development of severe AKI. Sensitivity analysis on risk factors of AKI stage 2 and 3 combined confirmed these findings. CONCLUSION: Severe AKI in COVID-19 was tightly coupled with critical illness and systemic inflammation and was not observed in milder disease courses. These findings suggest that traditional systemic AKI mechanisms rather than kidney-specific processes contribute to severe AKI in COVID-19.

CytoResc - "CytoSorb" Rescue for critically ill patients undergoing the COVID-19 Cytokine Storm: A structured summary of a study protocol for a randomized controlled trial.

OBJECTIVES: Approximately 8 - 10 % of COVID-19 patients present with a serious clinical course and need for hospitalization, 8% of hospitalized patients need ICU-treatment. Currently, no causal therapy is available and treatment is purely supportive. The main reason for death in critically ill patients is acute respiratory failure. However, in a number of patients a severe hyperinflammatory response with excessively elevated proinflammatory cytokines causes vasoplegic shock resistant to vasopressor therapy. A new polystyrene-based hemoadsorber (CytoSorb®, Cytosorbents Inc., New Jersey, USA) has been shown to adsorb effectively cytokines and other middle molecular weight toxins this way reducing their blood concentrations. This has been routinely used in clinical practice in the EU for other conditions where a cytokine storm occurs and an observational study has just been completed on COVID-19 patients. We hypothesized that the extracorporeal elimination of cytokines in critically ill COVID-19 patients with suspected hyperinflammation and shock may stabilize hemodynamics and improve outcome. The primary endpoint is time until resolution of vasoplegic shock, which is a well implemented, clinically relevant endpoint in critical care studies. TRIAL DESIGN: Phase IIb, multicenter, prospective, open-label, randomized, 1:1 parallel group pilot study comparing the additional use of "CytoSorb" to standard of care without "CytoSorb". PARTICIPANTS: Patients are recruited from the Intensive Care Units (ICUs) of 7 participating centers in Germany (approximately 10 ICUs). All patients aged 18- 80 with positive polymerase chain reaction (PCR) test for SARS-CoV-2, a C-reactive protein (CRP) ≥ 100 mg/l, a Procalcitonin (PCT) < 2 ng/l, and suspected cytokine storm defined via a vasoplegic shock (Norepinephrine > 0.2 µg/min/kg to achieve a Mean Arterial Pressure ≥ 65mmHg). Patients are included irrespective of indication for renal replacement therapy. Suspected or proven bacterial cause for vasoplegic shock is a contraindication. INTERVENTION AND COMPARATOR: Within 24 hours after meeting the inclusion criteria patients will be randomized to receive either standard of care or standard of care and additional "CytoSorb" therapy via a shaldon catheter for 3-7 days. Filter exchange is done every 24 hours. If patients receive antibiotics, an additional dose of antibiotics is administered after each change of "CytoSorb" filter in order to prevent underdosing due to "CytoSorb" treatment. MAIN OUTCOMES: Primary outcome is time to resolution of vasoplegic shock (defined as no need for vasopressors for at least 8 hours in order to sustain a MAP ≥ 65mmHg) in days. Secondary outcomes are 7 day mortality after fulfilling the inclusion criteria, mortality until hospital discharge, Interleukin-6 (IL-6) measurement on day 1 and 3, need for mechanical ventilation, duration of mechanical ventilation, duration of ICU-stay, catecholamine dose on day 1/2/3 after start of "CytoSorb" and acute kidney injury. RANDOMIZATION: An electronic randomization will be performed using the study software secuTrial® administered by the Clinical Study Center (CSC) of the Charité - Universitätsmedizin Berlin, Germany. Randomization is done in blocks by 4 stratified by including center. BLINDING (MASKING): The trial will be non-blinded for the clinicians and patients. The statistician will receive a blinded data set, so that all analyses will be conducted blinded. NUMBERS TO BE RANDOMIZED (SAMPLE SIZE): As this is a pilot study with the goal to examine the feasibility of the study design as well as the intervention effect, no formal sample size calculation was conducted. A total number of approximately 80-100 patients is planned (40-50 patients per group). Safety assessment is done after the inclusion of each 10 patients per randomization group. TRIAL STATUS: Please see the study protocol version from April 24 2020. Recruitment of patients is still pending. TRIAL REGISTRATION: The study was registered on April 27 2020 in the German Registry of Clinical Trials (DRKS) under the number DRKS00021447. FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

Studying the pathophysiology of coronavirus disease 2019: a protocol for the Berlin prospective COVID-19 patient cohort (Pa-COVID-19).

PURPOSE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide causing a global health emergency. Pa-COVID-19 aims to provide comprehensive data on clinical course, pathophysiology, immunology and outcome of COVID-19, to identify prognostic biomarkers, clinical scores, and therapeutic targets for improved clinical management and preventive interventions. METHODS: Pa-COVID-19 is a prospective observational cohort study of patients with confirmed SARS-CoV-2 infection treated at Charité - Universitätsmedizin Berlin. We collect data on epidemiology, demography, medical history, symptoms, clinical course, and pathogen testing and treatment. Systematic, serial blood sampling will allow deep molecular and immunological phenotyping, transcriptomic profiling, and comprehensive biobanking. Longitudinal data and sample collection during hospitalization will be supplemented by long-term follow-up. RESULTS: Outcome measures include the WHO clinical ordinal scale on day 15 and clinical, functional, and health-related quality-of-life assessments at discharge and during follow-up. We developed a scalable dataset to (i) suit national standards of care, (ii) facilitate comprehensive data collection in medical care facilities with varying resources, and (iii) allow for rapid implementation of interventional trials based on the standardized study design and data collection. We propose this scalable protocol as blueprint for harmonized data collection and deep phenotyping in COVID-19 in Germany. CONCLUSION: We established a basic platform for harmonized, scalable data collection, pathophysiological analysis, and deep phenotyping of COVID-19, which enables rapid generation of evidence for improved medical care and identification of candidate therapeutic and preventive strategies. The electronic database accredited for interventional trials allows fast trial implementation for candidate therapeutic agents. TRIAL REGISTRATION: Registered at the German registry for clinical studies (DRKS00021688).