Precision Medicine and Machine Learning to predict critical disease and death due to Coronavirus disease 2019 (COVID-19).
Authors
Affiliations (15)
Affiliations (15)
- Infectious Diseases Unit, Department of Clinical Medicine, Health Sciences Center, State University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Infectious Diseases Unit, Department of Clinical Medicine, Health Sciences Center, State University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Infectious Diseases Unit, Department of Clinical Medicine, Health Sciences Center, State University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil; Department of Pathology, Clinical Analysis and Toxicology, Health Sciences Center, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil; Department of Pathology, Clinical Analysis and Toxicology, Health Sciences Center, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil; Pontifical Catholic University of Paraná, School of Medicine, Campus Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China. Electronic address: [email protected].
- Laboratory of Research in Applied Immunology, University Hospital of Londrina, University of Londrina, Londrina, Paraná, Brazil; Department of Pathology, Clinical Analysis and Toxicology, Health Sciences Center, University of Londrina, Londrina, Paraná, Brazil. Electronic address: [email protected].
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus Disease 2019 (COVID-19) and induces activation of inflammatory pathways, including the inflammasome. The aim was to construct Machine Learning (ML) models to predict critical disease and death in patients with COVID-19. A total of 528 individuals with SARS-CoV-2 infection were included, comprising 308 with critical and 220 with non-critical COVID-19. The ML models included imaging, demographic, inflammatory biomarkers, NLRP3 (rs10754558 and rs10157379) and IL18 (rs360717 and rs187238) inflammasome variants. Individuals with critical COVID-19 were older, higher male/female ratio, body mass index (BMI), rate of type 2 diabetes mellitus (T2DM), hypertension, inflammatory biomarkers, need of orotracheal intubation, intensive care unit admission, incidence of death, and sickness symptom complex (SSC) scores and lower peripheral oxygen saturation (SpO<sub>2</sub>) compared to those with non-critical disease. We found that 49.5 % of the variance in the severity of critical COVID-19 was explained by SpO<sub>2</sub> and SSC (negatively associated), chest computed tomography alterations (CCTA), inflammatory biomarkers, severe acute respiratory syndrome (SARS), BMI, T2DM, and age (positively associated). In this model, the NLRP3/IL18 variants showed indirect effects on critical COVID-19 that were mediated by inflammatory biomarkers, SARS, and SSC. Neural network models yielded a prediction of critical disease and death due to COVID-19 with an area under the receiving operating characteristic curve of 0.930 and 0.927, respectively. These ML methods increase the accuracy of predicting severity, critical illness, and mortality caused by COVID-19 and show that the genetic variants contribute to the predictive power of the ML models.