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Elevated serum SDMA and ADMA at hospital admission predict in-hospital mortality of COVID-19 patients.

ABSTRACT: COVID-19 is a disease with a variable clinical course ranging from mild symptoms to critical illness, organ failure, and death. Prospective biomarkers may help to predict the severity of an individual's clinical course and mortality risk. We analyzed asymmetric (ADMA) and symmetric dimethylarginine (SDMA) in blood samples from 31 patients hospitalized for COVID-19. We calculated associations of ADMA and SDMA with mortality and organ failure, and we developed a predictive algorithm based upon these biomarkers to predict mortality risk. Nine patients (29%) experienced in-hospital death. SDMA and ADMA serum concentrations were significantly higher at admission in COVID-19 patients who died than in survivors. Cut-offs of 0.90 µmol/L for SDMA (AUC, 0.904, p = 0.0005) and 0.66 µmol/L for ADMA (AUC, 0.874, p = 0.0013) were found in ROC analyses to best discriminate both subgroups of patients. Hazard ratio for in-hospital mortality was 12.2 (95% CI: 2.2-31.2) for SDMA and 6.3 (1.1-14.7) for ADMA above cut-off. Sequential analysis of both biomarkers allowed discriminating a high-risk group (87.5% mortality) from an intermediate-risk group (25% mortality) and a low-risk group (0% mortality). Elevated circulating concentrations of SDMA and ADMA may help to better identify COVID-19 patients with a high risk of in-hospital mortality.

SUBMITTER: Hannemann J

PROVIDER: S-EPMC8110746 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIdentification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients.MethodsA retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration.ResultsCompared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)).ConclusionsThe COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.

Project description:ObjectiveSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is protean in its manifestations, affecting nearly every organ system. However, nervous system involvement and its effect on disease outcome are poorly characterized. The objective of this study was to determine whether neurologic syndromes are associated with increased risk of inpatient mortality.MethodsA total of 581 hospitalized patients with confirmed SARS-CoV-2 infection, neurologic involvement, and brain imaging were compared to hospitalized non-neurologic patients with coronavirus disease 2019 (COVID-19). Four patterns of neurologic manifestations were identified: acute stroke, new or recrudescent seizures, altered mentation with normal imaging, and neuro-COVID-19 complex. Factors present on admission were analyzed as potential predictors of in-hospital mortality, including sociodemographic variables, preexisting comorbidities, vital signs, laboratory values, and pattern of neurologic manifestations. Significant predictors were incorporated into a disease severity score. Patients with neurologic manifestations were matched with patients of the same age and disease severity to assess the risk of death.ResultsA total of 4,711 patients with confirmed SARS-CoV-2 infection were admitted to one medical system in New York City during a 6-week period. Of these, 581 (12%) had neurologic issues of sufficient concern to warrant neuroimaging. These patients were compared to 1,743 non-neurologic patients with COVID-19 matched for age and disease severity admitted during the same period. Patients with altered mentation (n = 258, p = 0.04, odds ratio [OR] 1.39, confidence interval [CI] 1.04-1.86) or radiologically confirmed stroke (n = 55, p = 0.001, OR 3.1, CI 1.65-5.92) had a higher risk of mortality than age- and severity-matched controls.ConclusionsThe incidence of altered mentation or stroke on admission predicts a modest but significantly higher risk of in-hospital mortality independent of disease severity. While other biomarker factors also predict mortality, measures to identify and treat such patients may be important in reducing overall mortality of COVID-19.

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Elevated serum SDMA and ADMA at hospital admission predict in-hospital mortality of COVID-19 patients.

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