Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study
| العنوان: | Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study |
|---|---|
| المؤلفون: | Olga Krysko, Elena Kondakova, Olga Vershinina, Elena Galova, Anna Blagonravova, Ekaterina Gorshkova, Claus Bachert, Mikhail Ivanchenko, Dmitri V. Krysko, Maria Vedunova |
| المصدر: | Frontiers in Immunology, Vol 12 (2021) FRONTIERS IN IMMUNOLOGY Frontiers in Immunology |
| بيانات النشر: | Frontiers Media S.A., 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Male, Support Vector Machine, medicine.medical_treatment, Immunology, Disease, Logistic regression, Fibrinogen, Severity of Illness Index, Monocytes, chemistry.chemical_compound, Severity of illness, Medicine and Health Sciences, Humans, Immunology and Allergy, Medicine, Aspartate Aminotransferases, Diagnosis, Computer-Assisted, Prospective Studies, HMGB1 Protein, Interleukin 6, Prospective cohort study, Original Research, Creatinine, IL-6, L-Lactate Dehydrogenase, biology, SARS-CoV-2, business.industry, Macrophages, COVID-19, prediction models, Alanine Transaminase, Middle Aged, RC581-607, Prognosis, artificial intelligence, MODEL, Cytokine, chemistry, CELLS, biology.protein, Cytokines, Female, Artificial intelligence, Immunologic diseases. Allergy, business, Biomarkers, macrophage derived cytokine, medicine.drug |
| الوصف: | BackgroundPrediction of the severity of COVID-19 at its onset is important for providing adequate and timely management to reduce mortality.ObjectiveTo study the prognostic value of damage parameters and cytokines as predictors of severity of COVID-19 using an extensive immunologic profiling and unbiased artificial intelligence methods.MethodsSixty hospitalized COVID-19 patients (30 moderate and 30 severe) and 17 healthy controls were included in the study. The damage indicators high mobility group box 1 (HMGB1), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), extensive biochemical analyses, a panel of 47 cytokines and chemokines were analyzed at weeks 1, 2 and 7 along with clinical complaints and CT scans of the lungs. Unbiased artificial intelligence (AI) methods (logistic regression and Support Vector Machine and Random Forest algorithms) were applied to investigate the contribution of each parameter to prediction of the severity of the disease.ResultsOn admission, the severely ill patients had significantly higher levels of LDH, IL-6, monokine induced by gamma interferon (MIG), D-dimer, fibrinogen, glucose than the patients with moderate disease. The levels of macrophage derived cytokine (MDC) were lower in severely ill patients. Based on artificial intelligence analysis, eight parameters (creatinine, glucose, monocyte number, fibrinogen, MDC, MIG, C-reactive protein (CRP) and IL-6 have been identified that could predict with an accuracy of 83−87% whether the patient will develop severe disease.ConclusionThis study identifies the prognostic factors and provides a methodology for making prediction for COVID-19 patients based on widely accepted biomarkers that can be measured in most conventional clinical laboratories worldwide. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1664-3224 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47e36de518e28423473e5225f34c81e1Test https://www.frontiersin.org/articles/10.3389/fimmu.2021.715072/fullTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....47e36de518e28423473e5225f34c81e1 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 16643224 |
|---|