Red Blood Cell Distribution Is a Significant Predictor of Severe Illness in Coronavirus Disease 2019

Lippi, Giuseppe; Henry, Brandon Michael; Sanchís-Gomar, Fabián

doi:10.1159/000510914

Cited by 37 publications

(46 citation statements)

References 32 publications

(47 reference statements)

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“…We also found a few hematological indices that may be potential risk factors. High red cell distribution width (RDW) was associated with mortality in our study and was also identified in a recent meta-analysis of three studies as a risk factor 47 . Low lymphocyte percentage was a top-10 risk factor in cohort B, which may be related to immune functioning and response to infections.…”

Section: Discussionsupporting

confidence: 81%

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Wong

Xiang

2020

Preprint

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Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with severe disease. Accurate prediction of those at risk of developing severe infections is also important clinically. Methods: Based on the UK Biobank (UKBB data), we built machine learning(ML) models to predict the risk of developing severe or fatal infections, and to evaluate the major risk factors involved. We first restricted the analysis to infected subjects, then performed analysis at a population level, considering those with no known infections as controls. Hospitalization was used as a proxy for severity. Totally 93 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (e.g. hematological/liver and renal function/metabolic parameters etc.), anthropometric measures and other risk factors (e.g. smoking/drinking habits) were included as predictors. XGboost (gradient boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationship between risk factors and outcomes. Results: A total of 1191 severe and 358 fatal cases were identified. For the analysis among infected individuals (N=1747), our prediction model achieved AUCs of 0.668 and 0.712 for severe and fatal infections respectively. Since only pre-diagnostic clinical data were available, the main objective of this analysis was to identify baseline risk factors. The top five contributing factors for severity were age, waist-hip ratio(WHR), HbA1c, number of drugs taken(cnt_tx) and gamma-glutamyl transferase levels. For prediction of mortality, the top features were age, systolic blood pressure, waist circumference (WC), urea and WHR. In subsequent analyses involving the whole UKBB population (N for controls=489987), the corresponding AUCs for severity and fatality were 0.669 and 0.749. The same top five risk factors were identified for both outcomes, namely age, cnt_tx, WC, WHR and cystatin C. We also uncovered other features of potential relevance, including testosterone, IGF-1 levels, red cell distribution width (RDW) and lymphocyte percentage. Conclusions: We identified a number of baseline clinical risk factors for severe/fatal infection by an ML approach. For example, age, central obesity, impaired renal function, multi-comorbidities and cardiometabolic abnormalities may predispose to poorer outcomes. The presented prediction models may be useful at a population level to help identify those susceptible to developing severe/fatal infections, hence facilitating targeted prevention strategies. Further replications in independent cohorts are required to verify our findings.

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Section: Discussionsupporting

confidence: 81%

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Wong

Xiang

2020

Preprint

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“…A systematic review and meta-analysis has previously investigated whether RDW values predict severe disease in patients with SARS-CoV-2 infection [ 48 ]. While the reported weighted mean difference between patients with and without severe disease are comparable with those observed in our study (0.69 vs. 0.56%) only three studies were assessed, including a study published in MedRxiv but not peer reviewed.…”

Section: Discussionmentioning

confidence: 99%

Red Blood Cell Distribution Width, Disease Severity, and Mortality in Hospitalized Patients with SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis

Zinellu

Mangoni

2021

JCM

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The identification of biomarkers predicting disease severity and outcomes is the focus of intense research in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 infection). Ideally, such biomarkers should be easily derivable from routine tests. We conducted a systematic review and meta-analysis of the predictive role of the red blood cell distribution width (RDW), a routine hematological test, in patients with SARS-CoV-2 infection. We searched the electronic databases PubMed, Web of Science and Scopus, from January 2020 to November 2020, for studies reporting data on the RDW and coronavirus disease 2019 (COVID-19) severity, defined as severe illness or admission to the intensive care unit (ICU), and mortality. Eleven studies in 4901 COVID-19 patients were selected for the meta-analysis. Pooled results showed that the RDW values were significantly higher in patients with severe disease and non-survivors (standard mean difference, SMD = 0.56, 95% CI 0.31 to 0.81, p < 0.001). Heterogeneity between studies was extreme (I2 = 80.6%; p < 0.001). In sensitivity analysis, the effect size was not modified when each study was in turn removed (effect size range, between 0.47 and 0.63). The Begg’s (p = 0.53) and Egger’s tests (p = 0.52) showed no evidence of publication bias. No significant correlations were observed between SMD and age, gender, whole blood count, end point, study geographic area, or design. Our meta-analysis showed that higher RDW values are significantly associated with COVID-19 severity and mortality. This routine parameter might assist with early risk stratification in patients with SARS-CoV-2 infection.

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“…Based on an electronic search reporting on three studies on the total number of 11,445 COVID-19 patients and 2654 severe patients, RDW-CV was higher in COVID-19 patients with severe illness than in those with mild disease. They revealed that the absolute RDW-CV value was 0.69% higher in severe patients compared with those with mild disease [ 28 ]. A retrospective study has been conducted to investigate the relationship between RDW and COVID-19 mortality risk in 1198 adult patients diagnosed with SARS-CoV-2 at 4 Partners Healthcare Network Hospitals between 4 March 2020, and 28 April 2020.…”

Section: Discussionmentioning

confidence: 99%

Validation of Red Cell Distribution Width As a COVID-19 Severity Screening Tool

et al. 2021

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Aim: The aim of this study is the predictive validation of red cell distribution width (RDW) in COVID-19 patients. Method: In total, 331 COVID-19 patients were classified as ‘severe’ and ‘nonsevere’ groups based on the WHO standard criteria. The levels of RDW standard deviation (SD) were evaluated as both continuous and categorical variables. Multivariate statistical analyses were used. Results: RDW-SD ≤43 and ≤47 fl thresholds showed high specificity (90.1–91.4%) for diagnosing nonsevere illness and no risk of death. RDW-SD >47 indicated severe illness and a high mortality risk while 43<RDW-SD≤47 indicated severe illness with low risk of death. Conclusion: RDW-SD levels may be a potent independent predictor of the infection severity and mortality probability in COVID-19 patients.

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Red Blood Cell Distribution Is a Significant Predictor of Severe Illness in Coronavirus Disease 2019

Cited by 37 publications

References 32 publications

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Red Blood Cell Distribution Width, Disease Severity, and Mortality in Hospitalized Patients with SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis

Validation of Red Cell Distribution Width As a COVID-19 Severity Screening Tool

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