Predictors of COVID‐19 severity: A literature review

Abstract

Summary The coronavirus disease 2019 (COVID‐19) pandemic is a rapidly evolving global emergency that continues to strain healthcare systems. Emerging research describes a plethora of patient factors—including demographic, clinical, immunologic, hematological, biochemical, and radiographic findings—that may be of utility to clinicians to predict COVID‐19 severity and mortality. We present a synthesis of the current literature pertaining to factors predictive of COVID‐19 clinical course and outcomes. Findings associated with increased disease severity and/or mortality include age > 55 years, multiple pre‐existing comorbidities, hypoxia, specific computed tomography findings indicative of extensive lung involvement, diverse laboratory test abnormalities, and biomarkers of end‐organ dysfunction. Hypothesis‐driven research is critical to identify the key evidence‐based prognostic factors that will inform the design of intervention studies to improve the outcomes of patients with COVID‐19 and to appropriately allocate scarce resources.

Kawasaki-like disease in children with COVID-19

“…While more than 90% of children with COVID-19 were described to have asymptomatic, mild, or moderate disease, new concerns emerged with reports on hyperinflammatory states or Kawasaki-like disease [2,3]. The resulting phenotypes are a combination of typical/atypical Kawasaki disease, Kawasaki shock syndrome, toxic shock syndrome, and macrophage activation syndrome/hemophagocytic lymphohistiocytosis [4,5]. Children with hyperinflammatory syndrome and multiorgan involvement were classified as having pediatric inflammatory multisystem syndrome (PIMS) or multisystem inflammatory syndrome in children (MIS-C) [6,7].…”

Immune Response to SARS-CoV-2 Infection in Obesity and T2D: Literature Review

“…Outbreak of the Coronavirus disease in 2019 (COVID-19) has spread rapidly across the world evidencing the weakness of the public health system worldwide [ 1 , 2 ]. Free access to scientific information related to COVID-19, facilitates the understanding of epidemiological, clinical, and molecular aspects of the new virus, which can help to contain the disease and to give alternatives for treatment [ 3 ].…”

Nonspecific blood tests as proxies for COVID-19 hospitalization: are there plausible associations after excluding noisy predictors?

“…Early identification of severe cases allows the optimization of emergency care support [1] and the improvement of patient outcomes [7]. However, patients who do not yet meet supportive care criteria may fail to receive the necessary care, when there is rapid deterioration or inability to promptly go to a hospital.…”

“…Interest in developing predictive models of COVID-19 outcomes are widespread [7,8]. A review of 50 prognostic models concluded that they are at high risk of bias [8].…”

“…discriminative performance to estimate the probability of hospitalization (models 1, 2, 3 with AUC>,810 at positive stratum). The combination of these blood tests generates models(4,5,6,7) with better discriminative ability (AUC>,856 at SARS-CoV-2=1). None of these blood tests presented AUC superior to ,680 at SARS-CoV-2=0.…”