Factors associated with disease severity and mortality among patients with COVID-19: A systematic review and meta-analysis

Abstract

Background Understanding the factors associated with disease severity and mortality in Coronavirus disease (COVID-19) is imperative to effectively triage patients. We performed a systematic review to determine the demographic, clinical, laboratory and radiological factors associated with severity and mortality in COVID-19. Methods We searched PubMed, Embase and WHO database for English language articles from inception until May 8, 2020. We included Observational studies with direct comparison of clinical characteristics between a) patients who died and those who survived or b) patients with severe disease and those without severe disease. Data extraction and quality assessment were performed by two authors independently. Results Among 15680 articles from the literature search, 109 articles were included in the analysis. The risk of mortality was higher in patients with increasing age, male gender (RR 1.45, 95%CI 1.23–1.71), dyspnea (RR 2.55, 95%CI 1.88–2.46), diabetes (RR 1.59, 95%CI 1.41–1.78), hypertension (RR 1.90, 95%CI 1.69–2.15). Congestive heart failure (OR 4.76, 95%CI 1.34–16.97), hilar lymphadenopathy (OR 8.34, 95%CI 2.57–27.08), bilateral lung involvement (OR 4.86, 95%CI 3.19–7.39) and reticular pattern (OR 5.54, 95%CI 1.24–24.67) were associated with severe disease. Clinically relevant cut-offs for leukocytosis(>10.0 x109/L), lymphopenia(< 1.1 x109/L), elevated C-reactive protein(>100mg/L), LDH(>250U/L) and D-dimer(>1mg/L) had higher odds of severe disease and greater risk of mortality. Conclusion Knowledge of the factors associated of disease severity and mortality identified in our study may assist in clinical decision-making and critical-care resource allocation for patients with COVID-19.

A scaling approach to estimate the age-dependent COVID-19 infection fatality ratio from incomplete data

“…Finally, our analysis relies exclusively on public statics’ data and can easily be updated as more accurate information is available (for instance regarding the attack rates in different countries or better estimations of the total number of infections). For instance, severity rates are now known to be strongly dependent on the patients sex [ 10 ] or comorbidities [ 13 ] too, features that could be directly included in this analysis with no effort and that would greatly help to understand the interplay between them and age. In addition, if consolidated, the probabilities and the approach explained here, can be easily used to estimate the degree of penetration of the SARS-CoV-2 in different cities, regions, or countries, and to track the evolution of the pandemics.…”

“…Efforts have been made to determine the clinical severity of the virus [ 4 – 8 ] and its dependence with factors such as age [ 9 ], sex [ 10 ] or comorbidities [ 11 – 13 ], but determining precisely how deadly this virus is remains hard [ 14 , 15 ]. Many different solutions using the available data have been proposed to extract the correct CFR [ 2 , 16 – 21 ], estimate the number of infections [ 22 , 23 ] or the infection fatality ratio [ 24 – 27 ].…”

The systemic inflammatory response and clinicopathological characteristics in patients admitted to hospital with COVID-19 infection: Comparison of 2 consecutive cohorts

“…These whole-body changes are characteristic of a systemic inflammatory response to tissue injury. Indeed, measures of this systemic inflammatory response have been shown to have prognostic value (4)(5)(6). In particular, the 4C mortality score was developed in more than 55,000 patients with COVID-19 and measured the systemic inflammatory response using C-reactive protein (6).…”

Pathophysiology of acute respiratory syndrome coronavirus 2 infection: a systematic literature review to inform EULAR points to consider

“…Although many studies assessed the impact of comorbidities on clinical outcomes of COVID-19, 91 only one study explored the effect of comorbidities, namely, type 2 diabetes (T2D) on immune response in patients with COVID-19 ( online supplemental table S5 ). By means of unsupervised analyses of cytometry data and principal component analysis) including lymphocyte and monocyte subpopulations, the authors identified three distinct clusters of patients corresponding to COVID-19 without T2D, COVID-19+T2D and T2D without COVID-19‐19.…”