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2.
Artif Intell Life Sci ; 1: 100020, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1588542

ABSTRACT

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

3.
J Med Virol ; 93(12): 6703-6713, 2021 12.
Article in English | MEDLINE | ID: covidwho-1544323

ABSTRACT

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.


Subject(s)
COVID-19/diagnosis , Adult , Age Factors , Aged , Aged, 80 and over , C-Reactive Protein/analysis , COVID-19/mortality , COVID-19/pathology , Female , Fibrin Fibrinogen Degradation Products/analysis , Humans , Male , Middle Aged , Reproducibility of Results , Risk Assessment , Risk Factors , Severity of Illness Index , Urea/blood , Young Adult
4.
Eur J Neurol ; 28(12): 3925-3937, 2021 12.
Article in English | MEDLINE | ID: covidwho-1515204

ABSTRACT

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.


Subject(s)
COVID-19 , Neurodegenerative Diseases , Stroke , Headache , Humans , SARS-CoV-2
5.
Gesundheitswesen ; 83(S 01): S45-S53, 2021 Nov.
Article in German | MEDLINE | ID: covidwho-1500783

ABSTRACT

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.


Subject(s)
COVID-19 , Pandemics , Germany/epidemiology , Health Services Research , Humans , Pandemics/prevention & control , Registries , SARS-CoV-2
6.
J Clin Med ; 10(17)2021 Aug 27.
Article in English | MEDLINE | ID: covidwho-1374437

ABSTRACT

(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.

7.
Infection ; 50(2): 359-370, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1316346

ABSTRACT

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.


Subject(s)
COVID-19 , Early Warning Score , Area Under Curve , COVID-19/diagnosis , Humans , Machine Learning , Retrospective Studies , SARS-CoV-2
8.
Infection ; 49(4): 725-737, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1182343

ABSTRACT

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.


Subject(s)
COVID-19/complications , COVID-19/mortality , Renal Insufficiency, Chronic/complications , SARS-CoV-2 , Adolescent , Adult , Aged, 80 and over , Cohort Studies , Comorbidity , Humans , Logistic Models , Middle Aged , Renal Insufficiency, Chronic/immunology , Risk Factors , Young Adult
9.
Sci Data ; 7(1): 435, 2020 12 10.
Article in English | MEDLINE | ID: covidwho-972239

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , Data Anonymization , Pandemics , Registries , Adult , Aged , Aged, 80 and over , Biomedical Research , Confidentiality , Datasets as Topic , Female , Humans , Male , Middle Aged
10.
Infection ; 49(1): 63-73, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-812468

ABSTRACT

PURPOSE: Knowledge regarding patients' clinical condition at severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is sparse. Data in the international, multicenter Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort study may enhance the understanding of COVID-19. METHODS: Sociodemographic and clinical characteristics of SARS-CoV-2-infected patients, enrolled in the LEOSS cohort study between March 16, 2020, and May 14, 2020, were analyzed. Associations between baseline characteristics and clinical stages at diagnosis (uncomplicated vs. complicated) were assessed using logistic regression models. RESULTS: We included 2155 patients, 59.7% (1,287/2,155) were male; the most common age category was 66-85 years (39.6%; 500/2,155). The primary COVID-19 diagnosis was made in 35.0% (755/2,155) during complicated clinical stages. A significant univariate association between age; sex; body mass index; smoking; diabetes; cardiovascular, pulmonary, neurological, and kidney diseases; ACE inhibitor therapy; statin intake and an increased risk for complicated clinical stages of COVID-19 at diagnosis was found. Multivariable analysis revealed that advanced age [46-65 years: adjusted odds ratio (aOR): 1.73, 95% CI 1.25-2.42, p = 0.001; 66-85 years: aOR 1.93, 95% CI 1.36-2.74, p < 0.001; > 85 years: aOR 2.38, 95% CI 1.49-3.81, p < 0.001 vs. individuals aged 26-45 years], male sex (aOR 1.23, 95% CI 1.01-1.50, p = 0.040), cardiovascular disease (aOR 1.37, 95% CI 1.09-1.72, p = 0.007), and diabetes (aOR 1.33, 95% CI 1.04-1.69, p = 0.023) were associated with complicated stages of COVID-19 at diagnosis. CONCLUSION: The LEOSS cohort identified age, cardiovascular disease, diabetes and male sex as risk factors for complicated disease stages at SARS-CoV-2 diagnosis, thus confirming previous data. Further data regarding outcomes of the natural course of COVID-19 and the influence of treatment are required.


Subject(s)
COVID-19/epidemiology , Cardiovascular Diseases/epidemiology , Diabetes Mellitus/epidemiology , Kidney Diseases/epidemiology , Lung Diseases/epidemiology , Pandemics , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Angiotensin-Converting Enzyme Inhibitors/adverse effects , Body Mass Index , COVID-19/diagnosis , COVID-19/physiopathology , COVID-19/virology , Cardiovascular Diseases/diagnosis , Cardiovascular Diseases/physiopathology , Cardiovascular Diseases/virology , Cohort Studies , Comorbidity , Diabetes Mellitus/diagnosis , Diabetes Mellitus/physiopathology , Diabetes Mellitus/virology , Europe/epidemiology , Female , Humans , Hydroxymethylglutaryl-CoA Reductase Inhibitors/adverse effects , Kidney Diseases/diagnosis , Kidney Diseases/physiopathology , Kidney Diseases/virology , Logistic Models , Lung Diseases/diagnosis , Lung Diseases/physiopathology , Lung Diseases/virology , Male , Middle Aged , SARS-CoV-2/pathogenicity , Severity of Illness Index , Sex Factors
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