RESUMO
The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient's outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.
Assuntos
Algoritmos , COVID-19 , Aprendizado de Máquina , SARS-CoV-2 , Humanos , COVID-19/diagnóstico , Masculino , Feminino , Pessoa de Meia-Idade , Índice de Gravidade de Doença , Adulto , Biomarcadores/sangue , Idoso , PrognósticoRESUMO
In the present study, the levels of serum and airway soluble chemokines, pro-inflammatory/regulatory cytokines, and growth factors were quantified in critically ill COVID-19 patients (total n=286) at distinct time points (D0, D2-6, D7, D8-13 and D>14-36) upon Intensive Care Unit (ICU) admission. Augmented levels of soluble mediators were observed in serum from COVID-19 patients who progress to death. An opposite profile was observed in tracheal aspirate samples, indicating that systemic and airway microenvironment diverge in their inflammatory milieu. While a bimodal distribution was observed in the serum samples, a unimodal peak around D7 was found for most soluble mediators in tracheal aspirate samples. Systems biology tools further demonstrated that COVID-19 display distinct eccentric soluble mediator networks as compared to controls, with opposite profiles in serum and tracheal aspirates. Regardless the systemic-compartmentalized microenvironment, networks from patients progressing to death were linked to a pro-inflammatory/growth factor-rich, highly integrated center. Conversely, patients evolving to discharge exhibited networks of weak central architecture, with lower number of neighborhood connections and clusters of pro-inflammatory and regulatory cytokines. All in all, this investigation with robust sample size landed a comprehensive snapshot of the systemic and local divergencies composed of distinct immune responses driven by SARS-CoV-2 early on severe COVID-19.
Assuntos
COVID-19 , Estado Terminal , Citocinas/metabolismo , Humanos , Cinética , SARS-CoV-2RESUMO
An alarming disease caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) named COVID-19 has emerged as an unprecedented public health problem and ignited a world health crisis. As opposed to what was believed at the beginning of the pandemic, the virus has not only spread but persevere causing secondary waves and challenging the concept of herd immunity against viral infections. While the majority of SARS-CoV-2-infected individuals may remain asymptomatic, a fraction of individuals may develop low to high-grade severity signs and symptoms of COVID-19. The disease is multifactorial and can progress quickly, leading to severe complications and even death in a few days. Therefore, understanding the pre-existing factors for disease development has never been so pressing. In this scenario, the insights on the mechanisms underlying disease allied to the immune response developed during the viral invasion could shed light on novel predictive factors and prognostic tools for COVID-19 management and interventions. A recent genome-wide association study (GWAS) revealed several molecules that significantly impacted critically ill COVID-19 patients, leading to the core mechanisms of COVID-19 pathogenesis. Considering these findings and the fact that ACE-2 polymorphisms alone cannot explain disease progress and severity, this review aims at summarizing the most important and recent findings of the research and expert consensus of possible cytokine-related polymorphisms existing in the differential expression of paramount immune molecules that could be crucial for providing guidelines for decision-making and appropriate clinical management of COVID-19.