First results of the "Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS)"

Role and benefits of infectious diseases specialists in the COVID-19 pandemic: Multilevel analysis of care provision in German hospitals using data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) cohort

PURPOSE

This study investigates the care provision and the role of infectious disease (ID) specialists during the coronavirus disease-2019 (COVID-19) pandemic.

METHODS

A survey was conducted at German study sites participating in the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS). Hospitals certified by the German Society of Infectious diseases (DGI) were identified as ID centers. We compared care provision and the involvement of ID specialists between ID and non-ID hospitals. Then we applied a multivariable regression model to analyse how clinical ID care influenced the mortality of COVID-19 patients in the LEOSS cohort.

RESULTS

Of the 40 participating hospitals in the study, 35% (14/40) were identified as ID centers. Among those, clinical ID care structures were more commonly established, and ID specialists were always involved in pandemic management and the care of COVID-19 patients. Overall, 68% (27/40) of the hospitals involved ID specialists in the crisis management team, 78% (31/40) in normal inpatient care, and 80% (28/35) in intensive care. Multivariable analysis revealed that COVID-19 patients in ID centers had a lower mortality risk compared to those in non-ID centers (odds ratio: 0.61 (95% CI 0.40-0.93), p = 0.021).

CONCLUSION

ID specialists played a crucial role in pandemic management and inpatient care.

Validation of the SACOV-19 score for identifying patients at risk of complicated or more severe COVID-19: a prospective study

PURPOSE

Identification of patients at risk of complicated or more severe COVID-19 is of pivotal importance, since these patients might require monitoring, antiviral treatment, and hospitalization. In this study, we prospectively evaluated the SACOV-19 score for its ability to predict complicated or more severe COVID-19.

METHODS

In this prospective multicenter study, we included 124 adult patients with acute COVID-19 in three German hospitals, who were diagnosed in an early, uncomplicated stage of COVID-19 within 72 h of inclusion. We determined the SACOV-19 score at baseline and performed a follow-up at 30 days.

RESULTS

The SACOV-19 score's AUC was 0.816. At a cutoff of > 3, it predicted deterioration to complicated or more severe COVID-19 with a sensitivity of 94% and a specificity of 55%. It performed significantly better in predicting complicated COVID-19 than the random tree-based SACOV-19 predictive model, the CURB-65, 4C mortality, or qCSI scores.

CONCLUSION

The SACOV-19 score is a feasible tool to aid decision making in acute COVID-19.

Utilization of anonymization techniques to create an external control arm for clinical trial data

BACKGROUND

Subject-level real-world data (RWD) collected during daily healthcare practices are increasingly used in medical research to assess questions that cannot be addressed in the context of a randomized controlled trial (RCT). A novel application of RWD arises from the need to create external control arms (ECAs) for single-arm RCTs. In the analysis of ECAs against RCT data, there is an evident need to manage and analyze RCT data and RWD in the same technical environment. In the Nordic countries, legal requirements may require that the original subject-level data be anonymized, i.e., modified so that the risk to identify any individual is minimal. The aim of this study was to conduct initial exploration on how well pseudonymized and anonymized RWD perform in the creation of an ECA for an RCT.

METHODS

This was a hybrid observational cohort study using clinical data from the control arm of the completed randomized phase II clinical trial (PACIFIC-AF) and RWD cohort from Finnish healthcare data sources. The initial pseudonymized RWD were anonymized within the (k, ε)-anonymity framework (a model for protecting individuals against identification). Propensity score matching and weighting methods were applied to the anonymized and pseudonymized RWD, to balance potential confounders against the RCT data. Descriptive statistics for the potential confounders and overall survival analyses were conducted prior to and after matching and weighting, using both the pseudonymized and anonymized RWD sets.

RESULTS

Anonymization affected the baseline characteristics of potential confounders only marginally. The greatest difference was in the prevalence of chronic obstructive pulmonary disease (4.6% vs. 5.4% in the pseudonymized compared to the anonymized data, respectively). Moreover, the overall survival changed in anonymization by only 8% (95% CI 4-22%). Both the pseudonymized and anonymized RWD were able to produce matched ECAs for the RCT data. Anonymization after matching impacted overall survival analysis by 22% (95% CI -21-87%).

CONCLUSIONS

Anonymization may be a viable technique for cases where flexible data transfer and sharing are required. As anonymization necessarily affects some aspects of the original data, further research and careful consideration of anonymization strategies are needed.

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.

Age and Comorbidity Burden of Patients Critically Ill with COVID-19 Affect Both Access to and Outcome of Ventilation Therapy in Intensive Care Units

During the COVID-19 pandemic, large numbers of elderly, multimorbid people required treatment in intensive care units. This study investigated how the inherent patient factors age and comorbidity burden affected the treatment strategy and the outcome achieved. Retrospective analysis of data from intensive care patients enrolled in the Lean European Open Survey on SARS-CoV2-Infected Patients (LEOSS) cohort found that a patient's age and comorbidity burden in fact influenced their mortality rate and the use of ventilation therapy. Evidence showed that advanced age and multimorbidity were associated with the restrictive use of invasive ventilation therapies, particularly ECMO. Geriatric patients with a high comorbidity burden were clustered in the sub-cohort of non-ventilated ICU patients characterized by a high mortality rate. The risk of death generally increased with older age and accumulating comorbidity burden. Here, the more aggressive an applied procedure, the younger the age in which a majority of patients died. Clearly, geriatric, multimorbid COVID-19 patients benefit less from invasive ventilation therapies. This implies the need for a holistic approach to therapy decisions, taking into account the patient's wishes.

Specialized palliative care for hospitalized patients with SARS-CoV-2 infection: an analysis of the LEOSS registry

PURPOSE

Symptom control for patients who were severely ill or dying from COVID-19 was paramount while resources were strained and infection control measures were in place. We aimed to describe the characteristics of SARS-CoV-2 infected patients who received specialized palliative care (SPC) and the type of SPC provided in a larger cohort.

METHODS

From the multi-centre cohort study Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS), data of patients hospitalized with SARS-CoV-2 infection documented between July 2020 and October 2021 were analysed.

RESULTS

273/7292 patients (3.7%) received SPC. Those receiving SPC were older and suffered more often from comorbidities, but 59% presented with an estimated life expectancy > 1 year. Main symptoms were dyspnoea, delirium, and excessive tiredness. 224/273 patients (82%) died during the hospital stay compared to 789/7019 (11%) without SPC. Symptom control was provided most common (223/273; 95%), followed by family and psychological support (50% resp. 43%). Personal contact with friends or relatives before or during the dying phase was more often documented in patients receiving SPC compared to patients without SPC (52% vs. 30%).

CONCLUSION

In 3.7% of SARS-CoV-2 infected hospitalized patients, the burden of the acute infection triggered palliative care involvement. Besides complex symptom management, SPC professionals also focused on psychosocial and family issues and aimed to enable personal contacts of dying patients with their family. The data underpin the need for further involvement of SPC in SARS-CoV-2 infected patients but also in other severe chronic infectious diseases.

Disease severity in hospitalized COVID-19 patients: comparing routine surveillance with cohort data from the LEOSS study in 2020 in Germany

INTRODUCTION

Studies investigating risk factors for severe COVID-19 often lack information on the representativeness of the study population. Here, we investigate factors associated with severe COVID-19 and compare the representativeness of the dataset to the general population.

METHODS

We used data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) of hospitalized COVID-19 patients diagnosed in 2020 in Germany to identify associated factors for severe COVID-19, defined as progressing to a critical disease stage or death. To assess the representativeness, we compared the LEOSS cohort to cases of hospitalized patients in the German statutory notification data of the same time period. Descriptive methods and Poisson regression models were used.

RESULTS

Overall, 6672 hospitalized patients from LEOSS and 132,943 hospitalized cases from the German statutory notification data were included. In LEOSS, patients above 76 years were less likely represented (34.3% vs. 44.1%). Moreover, mortality was lower (14.3% vs. 21.5%) especially among age groups above 66 years. Factors associated with a severe COVID-19 disease course in LEOSS included increasing age, male sex (adjusted risk ratio (aRR) 1.69, 95% confidence interval (CI) 1.53-1.86), prior stem cell transplantation (aRR 2.27, 95% CI 1.53-3.38), and an elevated C-reactive protein at day of diagnosis (aRR 2.30, 95% CI 2.03-2.62).

CONCLUSION

We identified a broad range of factors associated with severe COVID-19 progression. However, the results may be less applicable for persons above 66 years since they experienced lower mortality in the LEOSS dataset compared to the statutory notification data.

SARS-CoV-2 infection in chronic kidney disease patients with pre-existing dialysis: description across different pandemic intervals and effect on disease course (mortality)

PURPOSE

Patients suffering from chronic kidney disease (CKD) are in general at high risk for severe coronavirus disease (COVID-19) but dialysis-dependency (CKD5D) is poorly understood. We aimed to describe CKD5D patients in the different intervals of the pandemic and to evaluate pre-existing dialysis dependency as a potential risk factor for mortality.

METHODS

In this multicentre cohort study, data from German study sites of the Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) were used. We multiply imputed missing data, performed subsequent analyses in each of the imputed data sets and pooled the results. Cases (CKD5D) and controls (CKD not requiring dialysis) were matched 1:1 by propensity-scoring. Effects on fatal outcome were calculated by multivariable logistic regression.

RESULTS

The cohort consisted of 207 patients suffering from CKD5D and 964 potential controls. Multivariable regression of the whole cohort identified age (> 85 years adjusted odds ratio (aOR) 7.34, 95% CI 2.45-21.99), chronic heart failure (aOR 1.67, 95% CI 1.25-2.23), coronary artery disease (aOR 1.41, 95% CI 1.05-1.89) and active oncological disease (aOR 1.73, 95% CI 1.07-2.80) as risk factors for fatal outcome. Dialysis-dependency was not associated with a fatal outcome-neither in this analysis (aOR 1.08, 95% CI 0.75-1.54) nor in the conditional multivariable regression after matching (aOR 1.34, 95% CI 0.70-2.59).

CONCLUSIONS

In the present multicentre German cohort, dialysis dependency is not linked to fatal outcome in SARS-CoV-2-infected CKD patients. However, the mortality rate of 26% demonstrates that CKD patients are an extreme vulnerable population, irrespective of pre-existing dialysis-dependency.

[Development and validation of potential structure indicators for clinical infectious disease (ID) care in German hospitals during the COVID-19 pandemic]

INTRODUCTION

This study describes the development and validation of structure indicators for clinical infectious disease (ID) care in German hospitals, which is important to adequately face the future challenges in ID medicine.

METHODS

A team of experts developed the structure indicators in a three-stage, multicriteria decision-making process: (1) identification of potential structure indicators based on a literature review, (2) written assessment process, and (3) face-to-face discussion to reach consensus and final selection of appropriate structure indicators. A field study was conducted to assess the developed structure indicators. A score based on the structure indicators was determined for each hospital and validated via receiver operator characteristic (ROC) curves using externally validated ID expertise (German Society of ID (DGI) Centre).

RESULTS

Based on a list of 45 potential structure indicators, 18 suitable indicators were developed for clinical ID care structures in German hospitals. Out of these, ten key indicators were defined for the general and coronavirus disease 2019- (COVID-19-) specific clinical ID care structures. In the field survey of clinical ID care provision for COVID-19 patients in 40 German hospitals, the participating facilities achieved 0 to 9 points (median 4) in the determined score. The area under the ROC curve was 0.893 (95% CI: 0.797, 0.988; p < 0.001).

DISCUSSION/CONCLUSION

The structure indicators developed within the framework of a transparent and established development process can be used in the future to both capture the current state and future developments of ID care quality in Germany and enable comparisons.

Clinical outcomes of hospitalized COVID-19 patients treated with remdesivir: a retrospective analysis of a large tertiary care center in Germany

PURPOSE

The benefits of antiviral treatment with remdesivir in hospitalized patients with COVID-19 remain controversial. Clinical analyses are needed to demonstrate which patient populations are most likely to benefit.

METHODS

In a retrospective monocentric analysis, patients with COVID-19 treated between July 1, 2020 and June 30, 2021 at Hospital St. Georg, Leipzig, Germany were evaluated. The primary endpoint was time to clinical improvement, and the secondary endpoint was 28-day mortality. Propensity score matching was used for the endpoint analysis.

RESULTS

A total of 839 patients were fully evaluated, 68% of whom received specific COVID-19 drug therapy. Remdesivir was used in 31.3% of the patients, corticosteroids in 61.7%, and monoclonal antibodies in 2.3%. While dexamethasone administration was the most common therapeutic approach during the second pandemic wave, combination therapy with remdesivir and corticosteroids predominated during the third wave. Cox regression analysis revealed that combination therapy was not associated with faster clinical improvement (median: 13 days in both matched groups, HR 0.97 [95% CI 0.77-1.21], P = 0.762). By contrast, 28-day mortality was significantly lower in the corticosteroid-remdesivir group (14.8% versus 22.2% in the corticosteroid group, HR 0.60 [95% CI 0.39-0.95], P = 0.03) in the low-care setting. This effect was also demonstrated in a subgroup analysis of patients with remdesivir monotherapy (n = 44) versus standard of care (SOC).

CONCLUSION

In COVID-19 patients with only mild disease (low-flow oxygen therapy and treatment in a normal ward) who received corticosteroids and/or remdesivir in addition to SOC, early administration of remdesivir was associated with a measurable survival benefit.

Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients

Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks. However, the degree of protection must be balanced against the impact on statistical properties. We studied an extreme case of this trade-off: the statistical validity of an open medical dataset based on the German National Pandemic Cohort Network (NAPKON), which was prepared for publication using a strong anonymization procedure. Descriptive statistics and results of regression analyses were compared before and after anonymization of multiple variants of the original dataset. Despite significant differences in value distributions, the statistical bias was found to be small in all cases. In the regression analyses, the median absolute deviations of the estimated adjusted odds ratios for different sample sizes ranged from 0.01 [minimum = 0, maximum = 0.58] to 0.52 [minimum = 0.25, maximum = 0.91]. Disproportionate impact on the statistical properties of data is a common argument against the use of anonymization. Our analysis demonstrates that anonymization can actually preserve validity of statistical results in relatively low-dimensional data.

Common data model for COVID-19 datasets

MOTIVATION

A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets are necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling.

RESULTS

To ensure the interoperability and compatibility among COVID-19 datasets, we present here a common data model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dyspnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed CDM for COVID-19 data interoperability.

AVAILABILITY AND IMPLEMENTATION

The CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-Model.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Invasiveness of Ventilation Therapy Is Associated to Prevalence of Secondary Bacterial and Fungal Infections in Critically Ill COVID-19 Patients

Superinfections are a fundamental critical care problem, and their significance in severe COVID-19 cases needs to be determined. This study analyzed data from the Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort focusing on intensive care patients. A retrospective analysis of patient data from 840 cases of COVID-19 with critical courses demonstrated that co-infections were frequently present and were primarily of nosocomial origin. Furthermore, our analysis showed that invasive therapy procedures accompanied an increased risk for healthcare-associated infections. Non-ventilated ICU patients were rarely affected by secondary infections. The risk of infection, however, increased even when non-invasive ventilation was used. A further, significant increase in infection rates was seen with the use of invasive ventilation and even more so with extracorporeal membrane oxygenation (ECMO) therapy. The marked differences among ICU techniques used for the treatment of COVID-19-induced respiratory failure in terms of secondary infection risk profile should be taken into account for the optimal management of critically ill COVID-19 patients, as well as for adequate antimicrobial therapy.

The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics

The German government initiated the Network University Medicine (NUM) in early 2020 to improve national research activities on the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. To this end, 36 German Academic Medical Centers started to collaborate on 13 projects, with the largest being the National Pandemic Cohort Network (NAPKON). The NAPKON's goal is creating the most comprehensive Coronavirus Disease 2019 (COVID-19) cohort in Germany. Within NAPKON, adult and pediatric patients are observed in three complementary cohort platforms (Cross-Sectoral, High-Resolution and Population-Based) from the initial infection until up to three years of follow-up. Study procedures comprise comprehensive clinical and imaging diagnostics, quality-of-life assessment, patient-reported outcomes and biosampling. The three cohort platforms build on four infrastructure core units (Interaction, Biosampling, Epidemiology, and Integration) and collaborations with NUM projects. Key components of the data capture, regulatory, and data privacy are based on the German Centre for Cardiovascular Research. By April 01, 2022, 34 university and 40 non-university hospitals have enrolled 5298 patients with local data quality reviews performed on 4727 (89%). 47% were female, the median age was 52 (IQR 36-62-) and 50 pediatric cases were included. 44% of patients were hospitalized, 15% admitted to an intensive care unit, and 12% of patients deceased while enrolled. 8845 visits with biosampling in 4349 patients were conducted by April 03, 2022. In this overview article, we summarize NAPKON's design, relevant milestones including first study population characteristics, and outline the potential of NAPKON for German and international research activities.Trial registration https://clinicaltrials.gov/ct2/show/NCT04768998 . https://clinicaltrials.gov/ct2/show/NCT04747366 . https://clinicaltrials.gov/ct2/show/NCT04679584.

Hospitalized patients dying with SARS-CoV-2 infection—An analysis of patient characteristics and management in ICU and general ward of the LEOSS registry

Background

COVID-19 is a severe disease with a high need for intensive care treatment and a high mortality rate in hospitalized patients. The objective of this study was to describe and compare the clinical characteristics and the management of patients dying with SARS-CoV-2 infection in the acute medical and intensive care setting.

Methods

Descriptive analysis of dying patients enrolled in the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS), a non-interventional cohort study, between March 18 and November 18, 2020. Symptoms, comorbidities and management of patients, including palliative care involvement, were compared between general ward and intensive care unit (ICU) by univariate analysis.

Results

580/4310 (13%) SARS-CoV-2 infected patients died. Among 580 patients 67% were treated on ICU and 33% on a general ward. The spectrum of comorbidities and symptoms was broad with more comorbidities (≥ four comorbidities: 52% versus 25%) and a higher age distribution (>65 years: 98% versus 70%) in patients on the general ward. 69% of patients were in an at least complicated phase at diagnosis of the SARS-CoV-2 infection with a higher proportion of patients in a critical phase or dying the day of diagnosis treated on ICU (36% versus 11%). While most patients admitted to ICU came from home (71%), patients treated on the general ward came likewise from home and nursing home (44% respectively) and were more frequently on palliative care before admission (29% versus 7%). A palliative care team was involved in dying patients in 15%. Personal contacts were limited but more often documented in patients treated on ICU (68% versus 47%).

Conclusion

Patients dying with SARS-CoV-2 infection suffer from high symptom burden and often deteriorate early with a demand for ICU treatment. Therefor a demand for palliative care expertise with early involvement seems to exist.

COVID-19 outcomes in hospitalized Parkinson's disease patients in two pandemic waves in 2020: a nationwide cross-sectional study from Germany

BACKGROUND

The individualized clinical and public health management of the COVID-19 pandemic have changed over time, including care of people with PD. The objective was to investigate whether in-hospital COVID-19 outcomes and hospital care utilization of people with PD differed between the first two pandemic waves (W) 2020 in Germany.

METHODS

We conducted a nationwide cross-sectional study of inpatients with confirmed COVID-19 and PD between March 1 and May 31 (W1), and October 1 and December 31 (W2), 2020 and 2019, using an administrative database. Outcomes were in-hospital mortality, ICU admission rate, change in hospital care utilization, demographical data, PD clinical characteristics, and selected comorbidities. Differences were assessed between waves, PD/non-PD groups, and years.

RESULTS

We identified 2600 PD COVID-19 inpatients in W2 who in total showed higher in-hospital mortality rates and lower ICU admission rates, compared to both W1 (n = 775) and W1/W2 non-PD COVID-19 inpatients (n = 144,355). Compared to W1, W2 inpatients were more long-term care-dependent, older, more of female sex, and had less advanced disease. During both waves, PD inpatients were older, more frequently male and long-term care-dependent, and showed more risk comorbidities than non-PD COVID-19 inpatients. Decreases in hospital care utilization were stronger than average for PD inpatients but relatively weaker during W2. Non-COVID-19 PD inpatients showed poorer in-hospital outcomes in 2020 than in 2019 with better outcomes during W2.

CONCLUSIONS

In-hospital COVID-19 outcomes and hospital care utilization of PD patients in Germany differed between the two pandemic waves in 2020 with increased in-hospital mortality for PD COVID-19. Overall hospital care utilization for PD was increased during W2.

TRIAL REGISTRATION

No trial registration or ethical approval was required because data were publicly available, anonymized, and complied with the German data protection regulations.

Harmonization and standardization of data for a pan-European cohort on SARS- CoV-2 pandemic

The European project ORCHESTRA intends to create a new pan-European cohort to rapidly advance the knowledge of the effects and treatment of COVID-19. Establishing processes that facilitate the merging of heterogeneous clusters of retrospective data was an essential challenge. In addition, data from new ORCHESTRA prospective studies have to be compatible with earlier collected information to be efficiently combined. In this article, we describe how we utilized and contributed to existing standard terminologies to create consistent semantic representation of over 2500 COVID-19-related variables taken from three ORCHESTRA studies. The goal is to enable the semantic interoperability of data within the existing project studies and to create a common basis of standardized elements available for the design of new COVID-19 studies. We also identified 743 variables that were commonly used in two of the three prospective ORCHESTRA studies and can therefore be directly combined for analysis purposes. Additionally, we actively contributed to global interoperability by submitting new concept requests to the terminology Standards Development Organizations.

Obesity and Impaired Metabolic Health Increase Risk of COVID-19-Related Mortality in Young and Middle-Aged Adults to the Level Observed in Older People: The LEOSS Registry

Advanced age, followed by male sex, by far poses the greatest risk for severe COVID-19. An unresolved question is the extent to which modifiable comorbidities increase the risk of COVID-19-related mortality among younger patients, in whom COVID-19-related hospitalization strongly increased in 2021. A total of 3,163 patients with SARS-COV-2 diagnosis in the Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort were studied. LEOSS is a European non-interventional multi-center cohort study established in March 2020 to investigate the epidemiology and clinical course of SARS-CoV-2 infection. Data from hospitalized patients and those who received ambulatory care, with a positive SARS-CoV-2 test, were included in the study. An additive effect of obesity, diabetes and hypertension on the risk of mortality was observed, which was particularly strong in young and middle-aged patients. Compared to young and middle-aged (18-55 years) patients without obesity, diabetes and hypertension (non-obese and metabolically healthy; n = 593), young and middle-aged adult patients with all three risk parameters (obese and metabolically unhealthy; n = 31) had a similar adjusted increased risk of mortality [OR 7.42 (95% CI 1.55-27.3)] as older (56-75 years) non-obese and metabolically healthy patients [n = 339; OR 8.21 (95% CI 4.10-18.3)]. Furthermore, increased CRP levels explained part of the elevated risk of COVID-19-related mortality with age, specifically in the absence of obesity and impaired metabolic health. In conclusion, the modifiable risk factors obesity, diabetes and hypertension increase the risk of COVID-19-related mortality in young and middle-aged patients to the level of risk observed in advanced age.

COVID-19 mortality in cirrhosis is determined by cirrhosis-associated comorbidities and extrahepatic organ failure: Results from the multinational LEOSS registry

Background and Objective

International registries have reported high mortality rates in patients with liver disease and COVID‐19. However, the extent to which comorbidities contribute to excess COVID‐19 mortality in cirrhosis is controversial.

Methods

We used the multinational Lean European Open Survey on SARS‐CoV‐2‐infected patients (LEOSS) to identify patients with cirrhosis documented between March 2020 and March 2021, when the wild‐type and alpha variant were predominant. We compared symptoms, disease progression and mortality after propensity score matching (PSM) for age, sex, obesity, smoking status, and concomitant diseases. Mortality was also compared with that of patients with spontaneous bacterial peritonitis (SBP) without SARS‐CoV‐2 infection, a common bacterial infection and well‐described precipitator of acute‐on‐chronic liver failure.

Results

Among 7096 patients with SARS‐CoV‐2 infection eligible for analysis, 70 (0.99%) had cirrhosis, and all were hospitalized. Risk factors for severe COVID‐19, such as diabetes, renal disease, and cardiovascular disease were more frequent in patients with cirrhosis. Case fatality rate in patients with cirrhosis was 31.4% with the highest odds of death in patients older than 65 years (43.6% mortality; odds ratio [OR] 4.02; p = 0.018), Child‐Pugh class C (57.1%; OR 4.00; p = 0.026), and failure of two or more organs (81.8%; OR 19.93; p = 0.001). After PSM for demographics and comorbidity, the COVID‐19 case fatality of patients with cirrhosis did not significantly differ from that of matched patients without cirrhosis (28.8% vs. 26.1%; p = 0.644) and was similar to the 28‐day mortality in a comparison group of patients with cirrhosis and SBP (33.3% vs. 31.5%; p = 1.000).

Conclusions

In immunologically naïve patients with cirrhosis, mortality from wild‐type SARS‐CoV‐2 and the alpha variant is high and is largely determined by cirrhosis‐associated comorbidities and extrahepatic organ failure.

All-cause mortality and disease progression in SARS-CoV-2-infected patients with or without antibiotic therapy: an analysis of the LEOSS cohort

PURPOSE

Reported antibiotic use in coronavirus disease 2019 (COVID-19) is far higher than the actual rate of reported bacterial co- and superinfection. A better understanding of antibiotic therapy in COVID-19 is necessary.

METHODS

6457 SARS-CoV-2-infected cases, documented from March 18, 2020, until February 16, 2021, in the LEOSS cohort were analyzed. As primary endpoint, the correlation between any antibiotic treatment and all-cause mortality/progression to the next more advanced phase of disease was calculated for adult patients in the complicated phase of disease and procalcitonin (PCT) ≤ 0.5 ng/ml. The analysis took the confounders gender, age, and comorbidities into account.

RESULTS

Three thousand, six hundred twenty-seven cases matched all inclusion criteria for analyses. For the primary endpoint, antibiotic treatment was not correlated with lower all-cause mortality or progression to the next more advanced (critical) phase (n = 996) (both p > 0.05). For the secondary endpoints, patients in the uncomplicated phase (n = 1195), regardless of PCT level, had no lower all-cause mortality and did not progress less to the next more advanced (complicated) phase when treated with antibiotics (p > 0.05). Patients in the complicated phase with PCT > 0.5 ng/ml and antibiotic treatment (n = 286) had a significantly increased all-cause mortality (p = 0.029) but no significantly different probability of progression to the critical phase (p > 0.05).

CONCLUSION

In this cohort, antibiotics in SARS-CoV-2-infected patients were not associated with positive effects on all-cause mortality or disease progression. Additional studies are needed. Advice of local antibiotic stewardship- (ABS-) teams and local educational campaigns should be sought to improve rational antibiotic use in COVID-19 patients.

Clinical presentation, disease course, and outcome of COVID-19 in hospitalized patients with and without pre-existing cardiac disease: a cohort study across 18 countries

AIMS

Patients with cardiac disease are considered high risk for poor outcomes following hospitalization with COVID-19. The primary aim of this study was to evaluate heterogeneity in associations between various heart disease subtypes and in-hospital mortality.

METHODS AND RESULTS

We used data from the CAPACITY-COVID registry and LEOSS study. Multivariable Poisson regression models were fitted to assess the association between different types of pre-existing heart disease and in-hospital mortality. A total of 16 511 patients with COVID-19 were included (21.1% aged 66-75 years; 40.2% female) and 31.5% had a history of heart disease. Patients with heart disease were older, predominantly male, and often had other comorbid conditions when compared with those without. Mortality was higher in patients with cardiac disease (29.7%; n = 1545 vs. 15.9%; n = 1797). However, following multivariable adjustment, this difference was not significant [adjusted risk ratio (aRR) 1.08, 95% confidence interval (CI) 1.02-1.15; P = 0.12 (corrected for multiple testing)]. Associations with in-hospital mortality by heart disease subtypes differed considerably, with the strongest association for heart failure (aRR 1.19, 95% CI 1.10-1.30; P < 0.018) particularly for severe (New York Heart Association class III/IV) heart failure (aRR 1.41, 95% CI 1.20-1.64; P < 0.018). None of the other heart disease subtypes, including ischaemic heart disease, remained significant after multivariable adjustment. Serious cardiac complications were diagnosed in <1% of patients.

CONCLUSION

Considerable heterogeneity exists in the strength of association between heart disease subtypes and in-hospital mortality. Of all patients with heart disease, those with heart failure are at greatest risk of death when hospitalized with COVID-19. Serious cardiac complications are rare during hospitalization.

The Role of Diabetes and Hyperglycemia on COVID-19 Infection Course-A Narrative Review

It was previously reported that subjects with diabetes mellitus (DM) are more vulnerable to several bacterial or viral infections. In the era of coronavirus disease 2019 (COVID-19) pandemic, it is reasonable to wonder whether DM is a risk factor for COVID-19 infection, too. It is not yet clear whether DM increases the risk for contracting COVID-19 infection or not. However, patients with DM when infected are more likely to develop severe or even fatal COVID-19 disease course than patients without DM. Certain characteristics of DM patients may also deteriorate prognosis. On the other hand, hyperglycemia per se is related to unfavorable outcomes, and the risk may be higher for COVID-19 subjects without pre-existing DM. In addition, individuals with DM may experience prolonged symptoms, need readmission, or develop complications such as mucormycosis long after recovery from COVID-19; close follow-up is hence necessary in some selected cases. We here present a narrative review of the literature in order to set light into the relationship between COVID-19 infection and DM/hyperglycemia.

Pre-medication with oral anticoagulants is associated with better outcomes in a large multinational COVID-19 cohort with cardiovascular comorbidities

AIMS

Coagulopathy and venous thromboembolism are common findings in coronavirus disease 2019 (COVID-19) and are associated with poor outcome. Timely initiation of anticoagulation after hospital admission was shown to be beneficial. In this study we aim to examine the association of pre-existing oral anticoagulation (OAC) with outcome among a cohort of SARS-CoV-2 infected patients.

METHODS AND RESULTS

We analysed the data from the large multi-national Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) from March to August 2020. Patients with SARS-CoV-2 infection were eligible for inclusion. We retrospectively analysed the association of pre-existing OAC with all-cause mortality. Secondary outcome measures included COVID-19-related mortality, recovery and composite endpoints combining death and/or thrombotic event and death and/or bleeding event. We restricted bleeding events to intracerebral bleeding in this analysis to ensure clinical relevance and to limit reporting errors. A total of 1 433 SARS-CoV-2 infected patients were analysed, while 334 patients (23.3%) had an existing premedication with OAC and 1 099 patients (79.7%) had no OAC. After risk adjustment for comorbidities, pre-existing OAC showed a protective influence on the endpoint death (OR 0.62, P = 0.013) as well as the secondary endpoints COVID-19-related death (OR 0.64, P = 0.023) and non-recovery (OR 0.66, P = 0.014). The combined endpoint death or thrombotic event tended to be less frequent in patients on OAC (OR 0.71, P = 0.056).

CONCLUSIONS

Pre-existing OAC is protective in COVID-19, irrespective of anticoagulation regime during hospital stay and independent of the stage and course of disease.

EpidemiologiCal POpulatioN STudy of SARS-CoV-2 in Lake CounTy, Illinois (CONTACT): Methodology and Baseline Characteristics of a Community-Based Surveillance Study

Introduction

EpidemiologiCal POpulatioN STudy of SARS-CoV-2 in Lake CounTy, Illinois (CONTACT) is an observational, epidemiological study with a 9-month longitudinal follow-up of nonhospitalized persons aged 18 years or older currently living or employed in Lake County, IL. We describe the study design and report baseline characteristics of the study participants, including the proportion of participants with acute or previous SARS-CoV-2 infection at enrollment.

Methods

At enrollment and subsequent timepoints, participants recruited through digital and paper-based advertising campaigns reported their occupational and school-based exposure, risk factors, and behaviors, and provided nasal and serum specimens. Stratified enrichment was used to enhance enrollment into medium- and higher-risk groups within four occupational risk groups for SARS-CoV-2 infection. RT-PCR and serologic (IgG) testing were conducted to detect acute or previous SARS-CoV-2 infection in participants, respectively.

Results

Between November 2020 and January 2021, 1008 participants (female 70.7%, mean age ± SD 51 ± 13.8 years) completed the questionnaire and diagnostic testing. Among participants, 41.8% (n = 421) were considered low risk, 24.6% (n = 248) were medium-to-low risk, 22.3% (n = 225) were medium-to-high risk, and 11.3% (n = 114) were high risk. Of 56 (5.6%) participants with evidence of acute or previous SARS-CoV-2 infection at baseline, 11 (19.6%) were RT-PCR-positive, 36 (64.3%) were IgG-seropositive, and 9 (16.1%) were positive by both assays. Participants who were adherent vs nonadherent to social distancing measures (odds ratio [95% CI] 0.8 [0.4–1.8]) were less likely, while those in higher vs lower occupational risk groups (2.0 [1.0–4.4]) were more likely to have evidence for acute or previous SARS-CoV-2 infection.

Conclusion

In fall/winter 2020/21, 5.6% of adults in a Lake County convenience sample had evidence for acute or previous SARS-CoV-2 infection at baseline. Nonadherence to social distancing measures and high-risk professions were associated with SARS-CoV-2 infection. The study is ongoing and future analyses will assess infection status over time.

Clinical Trial Registration

NCT04611230.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40121-022-00593-0.

[Wave riding - 12 months of COVID-19 in a German tertiary care center]

Einleitung  Mit weit mehr als 1400 stationär behandelten COVID-19-Patienten ist die Universitätsmedizin Essen der größte COVID-19-Versorger der Region Rhein-Ruhr. Wir präsentieren die Daten unserer Patienten aus den ersten 12 Monaten der Pandemie und die hieraus entstandenen praktischen Konzepte.

Methode  Retrospektive Analyse aller 1396 stationären COVID-19-Patienten, die zwischen dem 1. März 2020 und 28. Februar 2021 versorgt wurden, im Hinblick auf Komorbiditäten, Überleben und Komplikationen im Verlauf. Es erfolgte ein Gruppenvergleich zwischen Patienten auf Normalstation und Überwachungs-/ Intensivstation.

Ergebnisse  Bei einer Gesamtmortalität von 19,8 % (277/1396) starben 10,6 % (93/877) der Patienten auf Normalstation und 35,5 % (184/519) der Patienten auf Intensiv- und Überwachungsstationen im klinischen Verlauf. Hierbei waren ein Alter über 60 Jahre, Adipositas, maschinelle Beatmung, NO-Therapie, ECMO-Therapie sowie akutes Nierenversagen und Apoplex im Therapieverlauf unabhängige Prädiktoren für Mortalität.

Fazit  Die Mortalität unseres Kollektivs auf Normal- bzw. Intensivstationen liegt im Rahmen international publizierter Daten. Sowohl die hohe Rate von Komplikationen bei schwerem Verlauf als auch die große Bedeutung einfacher Komorbiditäten kann eindrücklich gezeigt werden. Das mittlere Alter der Patienten ist mit 60 Jahren auf Normalstation und 63 Jahren auf Intensivstationen überraschend niedrig. Maximaler Patienten- und Personalschutz, eine rasche und effektive Teststrategie im Rahmen der primären Triage, standardisierte Abläufe von der Notaufnahme bis zur Intensivstation sowie eine dynamische tagesaktuelle Anpassung der Ressourcen können eine hohe Versorgungsqualität, auch während der Pandemie, sichern.

The use of statins was associated with reduced COVID-19 mortality: a systematic review and meta-analysis

BACKGROUND

Statins are widely used to treat people with metabolic and cardiovascular disorders. The effect of statins on coronavirus disease 2019 (COVID-19) is unclear. To investigate the association between statins and COVID-19 outcomes and, if possible, identify the subgroup population that benefits most from statin use.

MATERIALS AND METHODS

A systematic review and meta-analysis of published studies that included statin users and described COVID-19 outcomes through 10 November 2020. This study used the generic inverse variance method to perform meta-analyses with random-effects modelling. The main outcomes were evaluation of the need for invasive mechanical ventilator (IMV) support, the need for intensive care unit (ICU) care and death. All outcomes were measured as dichotomous variables.

RESULTS

A total of 28 observational studies, covering data from 63,537 individuals with COVID-19, were included. The use of statins was significantly associated with decreased mortality (odds ratio [OR] = 0.71, 95% confidence interval [CI]: 0.55-0.92, I2=72%) and the need for IMV (OR = 0.81, 95% CI: 0.69-0.95, I2=0%) but was not linked to the need for ICU care (OR = 0.91, 95% CI: 0.55-1.51, I2=66%). Subgroup analysis further identified five types of studies in which statin users had even lower odds of death.

CONCLUSIONS

The use of statins was significantly associated with a reduced need for IMV and decreased mortality among individuals with COVID-19. Statins may not need to be discontinued because of concern for COVID-19 on admission. Further randomized controlled trial (RCTs) are needed to clarify the causal effect between statin use and severe COVID-19 outcomes.Key messagesParticipants in five types of studies were shown to have even lower odds of death when taking statins.The use of statins was significantly associated with a reduced need for invasive mechanical ventilation and decreased all-cause mortality among individuals with COVID-19. However, statin use did not prevent participants from needing care in the intensive care unit.The results justify performing randomized controlled trials (RCTs) to validate the benefits of statins on COVID-19 outcomes.

[The COVID-19 Pandemic as an Opportunity and Challenge for Registries in Health Services Research: Lessons Learned from the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS)]

OBJECTIVE

The Coronavirus Disease-2019 (COVID-19) pandemic has brought opportunities and challenges, especially for health services research based on routine data. In this article we will demonstrate this by presenting lessons learned from establishing the currently largest registry in Germany providing a detailed clinical dataset on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected patients: the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS).

METHODS

LEOSS is based on a collaborative and integrative research approach with anonymous recruitment and collection of routine data and the early provision of data in an open science context. The only requirement for inclusion was a SARS-CoV-2 infection confirmed by virological diagnosis. Crucial strategies to successfully realize the project included the dynamic reallocation of available staff and technical resources, an early and direct involvement of data protection experts and the ethics committee as well as the decision for an iterative and dynamic process of improvement and further development.

RESULTS

Thanks to the commitment of numerous institutions, a transsectoral and transnational network of currently 133 actively recruiting sites with 7,227 documented cases could be established (status: 18.03.2021). Tools for data exploration on the project website, as well as the partially automated provision of datasets according to use cases with varying requirements, enabled us to utilize the data collected within a short period of time. Data use and access processes were carried out for 97 proposals assigned to 27 different research areas. So far, nine articles have been published in peer-reviewed international journals.

CONCLUSION

As a collaborative effort of the whole network, LEOSS developed into a large collection of clinical data on COVID-19 in Germany. Even though in other international projects, much larger data sets could be analysed to investigate specific research questions through direct access to source systems, the uniformly maintained and technically verified documentation standard with many discipline-specific details resulted in a large valuable data set with unique characteristics. The lessons learned while establishing LEOSS during the current pandemic have already created important implications for the design of future registries and for pandemic preparedness and response.

Long-Term SARS-CoV-2 Specific Immunity Is Affected by the Severity of Initial COVID-19 and Patient Age

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the greatest medical challenge. Although crucial to the future management of the pandemic, the factors affecting the persistence of long-term SARS-CoV-2 immunity are not well understood. Therefore, we determined the extent of important correlates of SARS-CoV-2 specific protection in 200 unvaccinated convalescents after COVID-19. To investigate the effective memory response against the virus, SARS-CoV-2 specific T cell and humoral immunity (including virus-neutralizing antibodies) was determined over a period of one to eleven months. SARS-CoV-2 specific immune responses were present in 90% of individual patients. Notably, immunosuppressed patients did not have long-term SARS-CoV-2 specific T cell immunity. In our cohort, the severity of the initial illness influenced SARS-CoV-2 specific T cell immune responses and patients’ humoral immune responses to Spike (S) protein over the long-term, whereas the patients’ age influenced Membrane (M) protein-specific T cell responses. Thus, our study not only demonstrated the long-term persistence of SARS-CoV-2 specific immunity, it also determined COVID-19 severity and patient age as significant factors affecting long-term immunity.

[German registry www.Covid19-Rheuma.de : Status report after 1 year of the pandemic]

The COVID-19 registry ( www.covid19-rheuma.de ) of the German Society of Rheumatology was the first registry for the acquisition and systemic evaluation of viral infections in patients with inflammatory rheumatic diseases (IRD). This has enabled rapid generation of scientific data that will help to improve the care of patients with IRD in the context of the pandemic. In addition to confirming general risk factors, such as patient age and comorbidities (e.g. cardiovascular, chronic lung and kidney diseases), the use of glucocorticoids and the disease activity of the rheumatic disease could be identified as disease-specific independent risk factors for the need of hospitalization due to COVID-19. Evaluations of the continuously growing cohort of patients with IRD and COVID-19 enable recommendations for patient care to be based on better evidence. Cooperation with international rheumatology registries (e.g. European COVID-19 registry for IRD) enables analyses of aggregated cohorts of patients with IRD and COVID-19 for international comparisons and statistically even more reliable statements.

Specific Risk Factors for Fatal Outcome in Critically Ill COVID-19 Patients: Results from a European Multicenter Study

(1) Background: The aim of our study was to identify specific risk factors for fatal outcome in critically ill COVID-19 patients. (2) Methods: Our data set consisted of 840 patients enclosed in the LEOSS registry. Using lasso regression for variable selection, a multifactorial logistic regression model was fitted to the response variable survival. Specific risk factors and their odds ratios were derived. A nomogram was developed as a graphical representation of the model. (3) Results: 14 variables were identified as independent factors contributing to the risk of death for critically ill COVID-19 patients: age (OR 1.08, CI 1.06–1.10), cardiovascular disease (OR 1.64, CI 1.06–2.55), pulmonary disease (OR 1.87, CI 1.16–3.03), baseline Statin treatment (0.54, CI 0.33–0.87), oxygen saturation (unit = 1%, OR 0.94, CI 0.92–0.96), leukocytes (unit 1000/μL, OR 1.04, CI 1.01–1.07), lymphocytes (unit 100/μL, OR 0.96, CI 0.94–0.99), platelets (unit 100,000/μL, OR 0.70, CI 0.62–0.80), procalcitonin (unit ng/mL, OR 1.11, CI 1.05–1.18), kidney failure (OR 1.68, CI 1.05–2.70), congestive heart failure (OR 2.62, CI 1.11–6.21), severe liver failure (OR 4.93, CI 1.94–12.52), and a quick SOFA score of 3 (OR 1.78, CI 1.14–2.78). The nomogram graphically displays the importance of these 14 factors for mortality. (4) Conclusions: There are risk factors that are specific to the subpopulation of critically ill COVID-19 patients.

Neurological symptoms and complications in predominantly hospitalized COVID-19 patients: Results of the European multinational Lean European Open Survey on SARS-Infected Patients (LEOSS)

Background and purpose

During acute coronavirus disease 2019 (COVID‐19) infection, neurological signs, symptoms and complications occur. We aimed to assess their clinical relevance by evaluating real‐world data from a multinational registry.

Methods

We analyzed COVID‐19 patients from 127 centers, diagnosed between January 2020 and February 2021, and registered in the European multinational LEOSS (Lean European Open Survey on SARS‐Infected Patients) registry. The effects of prior neurological diseases and the effect of neurological symptoms on outcome were studied using multivariate logistic regression.

Results

A total of 6537 COVID‐19 patients (97.7% PCR‐confirmed) were analyzed, of whom 92.1% were hospitalized and 14.7% died. Commonly, excessive tiredness (28.0%), headache (18.5%), nausea/emesis (16.6%), muscular weakness (17.0%), impaired sense of smell (9.0%) and taste (12.8%), and delirium (6.7%) were reported. In patients with a complicated or critical disease course (53%) the most frequent neurological complications were ischemic stroke (1.0%) and intracerebral bleeding (ICB; 2.2%). ICB peaked in the critical disease phase (5%) and was associated with the administration of anticoagulation and extracorporeal membrane oxygenation (ECMO). Excessive tiredness (odds ratio [OR] 1.42, 95% confidence interval [CI] 1.20–1.68) and prior neurodegenerative diseases (OR 1.32, 95% CI 1.07–1.63) were associated with an increased risk of an unfavorable outcome. Prior cerebrovascular and neuroimmunological diseases were not associated with an unfavorable short‐term outcome of COVID‐19.

Conclusion

Our data on mostly hospitalized COVID‐19 patients show that excessive tiredness or prior neurodegenerative disease at first presentation increase the risk of an unfavorable short‐term outcome. ICB in critical COVID‐19 was associated with therapeutic interventions, such as anticoagulation and ECMO, and thus may be an indirect complication of a life‐threatening systemic viral infection.

SARS-CoV-2-directed antibodies persist for more than six months in a cohort with mild to moderate COVID-19

OBJECTIVE

To follow serological immune responses of front-line healthcare workers after PCR-confirmed COVID-19 for a mean of 30 weeks, describe the time-course of SARS-CoV-2 spike protein-specific IgG, IgA and IgM levels and to identify associations of the immune response with symptoms, demographic parameters and severity of disease.

METHODS

Anti-SARS-CoV-2 S protein-specific IgG, IgA and IgM antibodies were measured at three time points during the 30-week follow-up. COVID-19-specific symptoms were assessed with standardized questionnaires.

RESULTS

95% of the participants mounted an IgG response with only modest decline after week 12. IgG-type antibodies were still detectable in almost 90% of the subjects at 30 weeks. IgA and IgM responses were less robust and antibody titers decreased more rapidly. At 30 weeks, only 25% still had detectable IgA-type and none had IgM-type antibodies. Higher age and higher disease severity were independently associated with higher IgG antibody levels, albeit with wide variations.

CONCLUSION

Serological immune responses after COVID-19 show considerable inter-individual variability, but show an association with increasing age and higher severity of disease. IgG-type anti-SARS-CoV-2 antibodies remain positive in 90% of the individuals 30 weeks after onset of symptoms.

Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients

Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77-0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77-0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the "first wave" of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78-0.87]; for full follow-up: 0.82 [95% CI: 0.78-0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.

Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

BACKGROUND

COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19.

METHODS

This systematic review was registered at PROSPERO under CRD42020177154. We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies.

RESULTS

Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10 mg/L, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49 U/L, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88 pg/mL, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 to 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73).

DISCUSSION

This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors that predict severe COVID-19 outcomes and will inform clinical scores to support early decision-making.

Pulmonary function impairment of asymptomatic and persistently symptomatic patients 4 months after COVID-19 according to disease severity

Objective

Evaluation of pulmonary function impairment after COVID-19 in persistently symptomatic and asymptomatic patients of all disease severities and characterisation of risk factors.

Methods

Patients with confirmed SARS-CoV-2 infection underwent prospective follow-up with pulmonary function testing and blood gas analysis during steady-state cycle exercise 4 months after acute illness. Pulmonary function impairment (PFI) was defined as reduction below 80% predicted of DLCOcSB, TLC, FVC, or FEV1. Clinical data were analyzed to identify risk factors for impaired pulmonary function.

Results

76 patients were included, hereof 35 outpatients with mild disease and 41 patients hospitalized due to COVID-19. Sixteen patients had critical disease requiring mechanical ventilation, 25 patients had moderate–severe disease. After 4 months, 44 patients reported persisting respiratory symptoms. Significant PFI was prevalent in 40 patients (52.6%) occurring among all disease severities. The most common cause for PFI was reduced DLCOcSB (n = 39, 51.3%), followed by reduced TLC and FVC. The severity of PFI was significantly associated with mechanical ventilation (p < 0.001). Further risk factors for DLCO impairment were COPD (p < 0.001), SARS-CoV-2 antibody-Titer (p = 0.014) and in hospitalized patients CT score. A decrease of paO2 > 3 mmHg during cycle exercise occurred in 1/5 of patients after mild disease course.

Conclusion

We characterized pulmonary function impairment in asymptomatic and persistently symptomatic patients of different severity groups of COVID-19 and identified further risk factors associated with persistently decreased pulmonary function. Remarkably, gas exchange abnormalities were revealed upon cycle exercise in some patients with mild disease courses and no preexisting pulmonary condition.

Supplementary Information

The online version contains supplementary material available at 10.1007/s15010-021-01669-8.

Neurological manifestations of COVID-19 - an approach to categories of pathology

BACKGROUND

Various neurological manifestations of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported, associated with a broad spectrum of diverse neurological symptoms and syndromes. Estimating rate and relevance of these manifestations remains difficult as there is a lack of standardised case definitions.

METHODS

We defined comprehensive categories including most reported neurological manifestations associated with SARS-CoV-2 to allow for a more standardised data collection. After a literature search of MEDLINE with ten keywords, 12 selected studies and larger case series were included. We compared the rate and relevance of neurological manifestations in hospitalized patients. We propose four main categories including 1) cerebrovascular disease, 2) inflammatory syndromes of the central nervous system (CNS), peripheral nervous system (PNS) and muscle, 3) metabolic/toxic dysfunction of CNS, PNS and muscle and 4) miscellaneous disorders.

CONCLUSION

Ageusia (702) and anosmia (805) have been reported as the most common and the first occurring neurological symptoms. Cerebrovascular disease (451) and encephalopathy (663) were associated with a more severe course and worse clinical outcome. Any neurological manifestation was associated with a longer hospital stay and a higher morbidity and mortality compared to patients without neurological manifestations. We suggest reporting future neurological manifestations of coronavirus disease-19 (COVID-19) following a pathophysiology-based approach using standardized pre-defined case definitions to yield more specific and comparable data.

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

PURPOSE

While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.

METHODS

We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).

RESULTS

The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.

CONCLUSION

We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

Elevated markers of thrombo-inflammatory activation predict outcome in patients with cardiovascular comorbidities and COVID-19 disease: insights from the LEOSS registry

AIMS

SARS-CoV-2 infection is associated with adverse outcomes in patients with cardiovascular disease. Here, we analyzed whether specific biomarkers predict the clinical course of COVID-19 in patients with cardiovascular comorbidities.

METHODS AND RESULTS

We enrolled 2147 patients with SARS-CoV-2 infection which were included in the Lean European Open Survey on SARS-CoV‑2 (LEOSS)-registry from March to June 2020. Clinical data and laboratory values were collected and compared between patients with and without cardiovascular comorbidities in different clinical stages of the disease. Predictors for mortality were calculated using multivariate regression analysis. We show that patients with cardiovascular comorbidities display significantly higher markers of myocardial injury and thrombo-inflammatory activation already in the uncomplicated phase of COVID-19. In multivariate analysis, elevated levels of troponin [OR 1.54; (95% CI 1.22-1.96), p < 0.001)], IL-6 [OR 1.69 (95% CI 1.26-2.27), p < 0.013)], and CRP [OR 1.32; (95% CI 1.1-1.58), p < 0.003)] were predictors of mortality in patients with COVID-19.

CONCLUSION

Patients with cardiovascular comorbidities show elevated markers of thrombo-inflammatory activation and myocardial injury, which predict mortality, already in the uncomplicated phase of COVID-19. Starting targeted anti-inflammatory therapy and aggressive anticoagulation already in the uncomplicated phase of the disease might improve outcomes after SARS-CoV-2 infection in patients with cardiovascular comorbidities. Elevated markers of thrombo-inflammatory activation predict outcome in patients with cardiovascular comorbidities and COVID-19 disease: insights from the LEOSS registry.

Why We Are Losing the War Against COVID-19 on the Data Front and How to Reverse the Situation

With over 117 million COVID-19–positive cases declared and the death count approaching 3 million, we would expect that the highly digitalized health systems of high-income countries would have collected, processed, and analyzed large quantities of clinical data from patients with COVID-19. Those data should have served to answer important clinical questions such as: what are the risk factors for becoming infected? What are good clinical variables to predict prognosis? What kinds of patients are more likely to survive mechanical ventilation? Are there clinical subphenotypes of the disease? All these, and many more, are crucial questions to improve our clinical strategies against the epidemic and save as many lives as possible. One might assume that in the era of big data and machine learning, there would be an army of scientists crunching petabytes of clinical data to answer these questions. However, nothing could be further from the truth. Our health systems have proven to be completely unprepared to generate, in a timely manner, a flow of clinical data that could feed these analyses. Despite gigabytes of data being generated every day, the vast quantity is locked in secure hospital data servers and is not being made available for analysis. Routinely collected clinical data are, by and large, regarded as a tool to inform decisions about individual patients, and not as a key resource to answer clinical questions through statistical analysis. The initiatives to extract COVID-19 clinical data are often promoted by private groups of individuals and not by health systems, and are uncoordinated and inefficient. The consequence is that we have more clinical data on COVID-19 than on any other epidemic in history, but we have failed to analyze this information quickly enough to make a difference. In this viewpoint, we expose this situation and suggest concrete ideas that health systems could implement to dynamically analyze their routine clinical data, becoming learning health systems and reversing the current situation.

Clinical course and predictive risk factors for fatal outcome of SARS-CoV-2 infection in patients with chronic kidney disease

Purpose

The ongoing pandemic caused by the novel severe acute respiratory coronavirus 2 (SARS-CoV-2) has stressed health systems worldwide. Patients with chronic kidney disease (CKD) seem to be more prone to a severe course of coronavirus disease (COVID-19) due to comorbidities and an altered immune system. The study’s aim was to identify factors predicting mortality among SARS-CoV-2-infected patients with CKD.

Methods

We analyzed 2817 SARS-CoV-2-infected patients enrolled in the Lean European Open Survey on SARS-CoV-2-infected patients and identified 426 patients with pre-existing CKD. Group comparisons were performed via Chi-squared test. Using univariate and multivariable logistic regression, predictive factors for mortality were identified.

Results

Comparative analyses to patients without CKD revealed a higher mortality (140/426, 32.9% versus 354/2391, 14.8%). Higher age could be confirmed as a demographic predictor for mortality in CKD patients (> 85 years compared to 15–65 years, adjusted odds ratio (aOR) 6.49, 95% CI 1.27–33.20, p = 0.025). We further identified markedly elevated lactate dehydrogenase (> 2 × upper limit of normal, aOR 23.21, 95% CI 3.66–147.11, p < 0.001), thrombocytopenia (< 120,000/µl, aOR 11.66, 95% CI 2.49–54.70, p = 0.002), anemia (Hb < 10 g/dl, aOR 3.21, 95% CI 1.17–8.82, p = 0.024), and C-reactive protein (≥ 30 mg/l, aOR 3.44, 95% CI 1.13–10.45, p = 0.029) as predictors, while renal replacement therapy was not related to mortality (aOR 1.15, 95% CI 0.68–1.93, p = 0.611).

Conclusion

The identified predictors include routinely measured and universally available parameters. Their assessment might facilitate risk stratification in this highly vulnerable cohort as early as at initial medical evaluation for SARS-CoV-2.

Supplementary Information

The online version contains supplementary material available at 10.1007/s15010-021-01597-7.

A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint Lesion^TM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.

[S2k Guideline - Recommendations for Inpatient Therapy of Patients with COVID-19]

Since December 2019, the novel coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome - Corona Virus-2) has been spreading rapidly in the sense of a global pandemic. This poses significant challenges for clinicians and hospitals and is placing unprecedented strain on the healthcare systems of many countries. The majority of patients with Coronavirus Disease 2019 (COVID-19) present with only mild symptoms such as cough and fever. However, about 6 % require hospitalization. Early clarification of whether inpatient and, if necessary, intensive care treatment is medically appropriate and desired by the patient is of particular importance in the pandemic. Acute hypoxemic respiratory insufficiency with dyspnea and high respiratory rate (> 30/min) usually leads to admission to the intensive care unit. Often, bilateral pulmonary infiltrates/consolidations or even pulmonary emboli are already found on imaging. As the disease progresses, some of these patients develop acute respiratory distress syndrome (ARDS). Mortality reduction of available drug therapy in severe COVID-19 disease has only been demonstrated for dexamethasone in randomized controlled trials. The main goal of supportive therapy is to ensure adequate oxygenation. In this regard, invasive ventilation and repeated prone positioning are important elements in the treatment of severely hypoxemic COVID-19 patients. Strict adherence to basic hygiene, including hand hygiene, and the correct wearing of adequate personal protective equipment are essential when handling patients. Medically necessary actions on patients that could result in aerosol formation should be performed with extreme care and preparation.

Spinner C, Pfeifer M, Marx G, Karagiannidis C. Clinical Practice Guideline: Recommendations on Inpatient Treatment of Patients with COVID-19

BACKGROUND

Since identification of the first cases in December 2019, COVID-19, caused by the novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2) has spread across the world, giving rise to a global pandemic.

METHODS

A literature search was carried out in PubMed, using search terms defined by the authors. Questions important for the management of patients with COVID-19 were identified and discussed, and recommendations or statements on these topics were formulated in a structured consensus process.

RESULTS

Determination of the indication for the admission of COVID-19 patients to the hospital should involve consideration of age, comorbidities, respiratory rate, and oxygen saturation. Every patient admitted without a recent PCR test should be tested immediately. It is recommended that any COVID-19 patient with hypoxemia (SpO2 <90%) despite being given oxygen, dyspnea, or a high respiratory rate be admitted to intensive care. In the case of hypoxemic respiratory insufficiency, an attempt at treatment with high-flow oxygen or non-invasive ventilation is suggested, while patients with severe hypoxemia/high respiratory rate should undergo intubation and invasive ventilation. In the presence of additional risk factors (such as obesity, known thrombophilia, intensive care treatment, or elevated D-dimers), intensified prophylaxis against thromboembolism may be indicated. Treatment with dexamethasone decreases the mortality among patients with severe or critical COVID-19. The important personal protection measures are attention to hygiene and the correct wearing of personal protective equipment.

CONCLUSION

The principal treatment measures are maintenance of adequate oxygenation, pharmacological prevention of thrombosis, and, in severe cases, administration of dexamethasone.

The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond

BACKGROUND

The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.

METHODS

Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.

RESULTS

A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.

CONCLUSION

GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19

The Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) is a European registry for studying the epidemiology and clinical course of COVID-19. To support evidence-generation at the rapid pace required in a pandemic, LEOSS follows an Open Science approach, making data available to the public in real-time. To protect patient privacy, quantitative anonymization procedures are used to protect the continuously published data stream consisting of 16 variables on the course and therapy of COVID-19 from singling out, inference and linkage attacks. We investigated the bias introduced by this process and found that it has very little impact on the quality of output data. Current laws do not specify requirements for the application of formal anonymization methods, there is a lack of guidelines with clear recommendations and few real-world applications of quantitative anonymization procedures have been described in the literature. We therefore believe that our work can help others with developing urgently needed anonymization pipelines for their projects.

Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

Background

COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19.

Methods

We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies.

Results

Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 - 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73).

Discussion

This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors in predicting severe COVID-19 outcomes and will inform decision analytical tools to support clinical decision-making.