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Outcomes of SARS-CoV-2 infections in patients with neurodegenerative diseases in the LEOSS cohort
(2021)
The impact of preexisting neurodegenerative diseases on superimposed SARS-CoV-2 infections remains controversial. Here we examined the course and outcome of SARS-CoV-2 infections in patients affected by Parkinson's disease (PD) or dementia compared to matched controls without neurodegenerative diseases in the LEOSS (Lean European Open Survey on SARS-CoV-2-infected patients) cohort, a large-scale prospective multicenter cohort study...
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.
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.
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.