Role of red blood cell distribution width, as a prognostic indicator in COVID-19: A systematic review and meta-analysis.
Soumya SarkarSundara KannanPuneet KhannaAkhil Kant SinghPublished in: Reviews in medical virology (2021)
The red blood cell distribution width (RDW), an indicator of anisocytosis has emerged as a potential tool for risk stratification of critically ill patients with sepsis. Prognostic predictors are of paramount interest for prompt intervention and optimal utilization of the healthcare system in this ongoing context of the Coronavirus Disease 2019 (COVID-19) pandemic. The current systematic review and meta-analysis aims to explore the utility of RDW in the prognosis of COVID-19 patients. A comprehensive screening of electronic databases was performed up to 30th April 2021 after enrolling in PROSPERO (CRD42020206685). Observational studies or interventional studies, evaluating the impact of RDW in COVID-19 outcomes (mortality and severity) are included in this meta-analysis.Our search retrieved 25 studies, with a total of 18,392 and 3,446 COVID-19 patients for mortality and disease severity outcomes. Deceased and critically ill patients had higher RDW levels on admission in comparison to survivors and non-severe patients (SMD = 0.46; 95%CI 0.31-0.71; I2 = 88% and SMD = 0.46; 95%CI 0.26-0.67; I2 = 60%, respectively). In a sub-group analysis of 2,980 patients, RDW > 14.5 has been associated with increased risk of mortality (OR = 2.73; 95%CI 1.96-3.82; I2 = 56%). However, the evidences is of low quality. A higher level of RDW on admission in COVID-19 patients is associated with increased morbidity and mortality. However, further studies regarding the cut-off value of RDW are the need of the hour.
Keyphrases
- red blood cell
- coronavirus disease
- sars cov
- case control
- systematic review
- cardiovascular events
- respiratory syndrome coronavirus
- randomized controlled trial
- ejection fraction
- risk factors
- newly diagnosed
- cardiovascular disease
- intensive care unit
- coronary artery disease
- acute kidney injury
- prognostic factors
- type diabetes
- machine learning
- young adults
- metabolic syndrome
- quality improvement
- adipose tissue
- weight loss
- patient reported