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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.
In the current dismal situation of the COVID-19 pandemic, effective management of patients with pneumonia and acute respiratory distress syndrome is of vital importance. Due to the current lack of effective pharmacological concepts, this situation has caused interest in (re)considering historical reports on the treatment of patients with low-dose radiation therapy for pneumonia. Although these historical reports are of low-level evidence per se, hampering recommendations for decision-making in the clinical setting, they indicate effectiveness in the dose range between 0.3 and 1 Gy, similar to more recent dose concepts in the treatment of acute and chronic inflammatory/degenerative benign diseases with, e.g., a single dose per fraction of 0.5 Gy. This concise review aims to critically review the evidence for low-dose radiation treatment of COVID-19 pneumopathy and discuss whether it is worth investigating in the present clinical situation.
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.
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.
Evaluation of stability and inactivation methods of SARS-CoV-2 in context of laboratory settings
(2021)
The novel coronavirus SARS-CoV-2 is the causative agent of the acute respiratory disease COVID-19, which has become a global concern due to its rapid spread. Laboratory work with SARS-CoV-2 in a laboratory setting was rated to biosafety level 3 (BSL-3) biocontainment level. However, certain research applications in particular in molecular biology require incomplete denaturation of the proteins, which might cause safety issues handling contaminated samples. In this study, we evaluated lysis buffers that are commonly used in molecular biological laboratories for their ability to inactivate SARS-CoV-2. In addition, viral stability in cell culture media at 4 °C and on display glass and plastic surfaces used in laboratory environment was analyzed. Furthermore, we evaluated chemical and non-chemical inactivation methods including heat inactivation, UV-C light, addition of ethanol, acetone-methanol, and PFA, which might be used as a subsequent inactivation step in the case of insufficient inactivation. We infected susceptible Caco-2 and Vero cells with pre-treated SARS-CoV-2 and determined the tissue culture infection dose 50 (TCID50) using crystal violet staining and microscopy. In addition, lysates of infected cells and virus containing supernatant were subjected to RT-qPCR analysis. We have found that guanidine thiocyanate and most of the tested detergent containing lysis buffers were effective in inactivation of SARS-CoV-2, however, the M-PER lysis buffer containing a proprietary detergent failed to inactivate the virus. In conclusion, careful evaluation of the used inactivation methods is required especially for non-denaturing buffers. Additional inactivation steps might be necessary before removal of lysed viral samples from BSL-3.
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.
(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.
Background: SARS-CoV-2 is one of the most threatening pandemics in human history. As of the date of this analysis, it had claimed about 2 million lives worldwide, and the number is rising sharply. Governments, societies, and scientists are equally challenged under this burden. Objective: This study aimed to map global coronavirus research in 2020 according to various influencing factors to highlight incentives or necessities for further research. Methods: The application of established and advanced bibliometric methods combined with the visualization technique of density-equalizing mapping provided a global picture of incentives and efforts on coronavirus research in 2020. Countries’ funding patterns and their epidemiological and socioeconomic characteristics as well as their publication performance data were included. Results: Research output exploded in 2020 with momentum, including citation and networking parameters. China and the United States were the countries with the highest publication performance. Globally, however, publication output correlated significantly with COVID-19 cases. Research funding has also increased immensely. Conclusions: Nonetheless, the abrupt decline in publication efforts following previous coronavirus epidemics should demonstrate to global researchers that they should not lose interest even after containment, as the next epidemiological challenge is certain to come. Validated reporting worldwide and the inclusion of low-income countries are additionally important for a successful future research strategy.
Aim: It can be challenging to distinguish COVID-19 in children from other common infections. We set out to determine the rate at which children consulting a primary care paediatrician with an acute infection are infected with SARS-CoV-2 and to compare distinct findings. Method: In seven out-patient clinics, children aged 0–13 years with any new respiratory or gastrointestinal symptoms and presumed infection were invited to be tested for SARS-CoV-2. Factors that were correlated with testing positive were determined. Samples were collected from 25 January 2021 to 01 April 2021. Results: Seven hundred and eighty-three children participated in the study (median age 3 years and 0 months, range 1 month to 12 years and 11 months). Three hundred and fifty-eight were female (45.7%). SARS-CoV-2 RNA was detected in 19 (2.4%). The most common symptoms in children with as well as without detectable SARS-CoV-2 RNA were rhinitis, fever and cough. Known recent exposure to a case of COVID-19 was significantly correlated with testing positive, but symptoms or clinical findings were not. Conclusion: COVID-19 among the children with symptoms of an acute infection was uncommon, and the clinical presentation did not differ significantly between children with and without evidence of an infection with SARS-CoV-2.
TRIANNI mice carry an entire set of human immunoglobulin V region gene segments and are a powerful tool to rapidly isolate human monoclonal antibodies. After immunizing these mice with DNA encoding the spike protein of SARS-CoV-2 and boosting with spike protein, we identified 29 hybridoma antibodies that reacted with the SARS-CoV-2 spike protein. Nine antibodies neutralize SARS-CoV-2 infection at IC50 values in the subnanomolar range. ELISA-binding studies and DNA sequence analyses revealed one cluster of three clonally related neutralizing antibodies that target the receptor-binding domain and compete with the cellular receptor hACE2. A second cluster of six clonally related neutralizing antibodies bind to the N-terminal domain of the spike protein without competing with the binding of hACE2 or cluster 1 antibodies. SARS-CoV-2 mutants selected for resistance to an antibody from one cluster are still neutralized by an antibody from the other cluster. Antibodies from both clusters markedly reduced viral spread in mice transgenic for human ACE2 and protected the animals from SARS-CoV-2-induced weight loss. The two clusters of potent noncompeting SARS-CoV-2 neutralizing antibodies provide potential candidates for therapy and prophylaxis of COVID-19. The study further supports transgenic animals with a human immunoglobulin gene repertoire as a powerful platform in pandemic preparedness initiatives.