Diagnostic performance of monocyte distribution width and other leukocyte parameters for SARS-CoV-2 and influenza infection

In a recent study posted to the Research Square* preprint server, researchers used a novel diagnostic screen, monocyte distribution width (MDW) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza infection.

Study: Monocyte Distribution Width as a Pragmatic Diagnostic Screen for SARS-CoV-2 and Influenza Infection. Image Credit: Corona Borealis Studio/Shutterstock
Study: Monocyte Distribution Width as a Pragmatic Diagnostic Screen for SARS-CoV-2 and Influenza Infection. Image Credit: Corona Borealis Studio/Shutterstock

Background

MDW, a measure of monocyte cell volume changes, is elevated in viral infections, even the mild ones. Therefore, MDW is evolving as a novel diagnostic screen for viral infections, especially useful in the emergency department (ED) settings.

Notably, monocytes are a subset of circulating leukocytes that differentiate into dendritic cells (DCs) and macrophages under appropriate stimuli and contribute to producing antigen-presenting cells (APCs). Overall, monocytes facilitate inflammation and the initiation of host immune response.

The influenza A virus has caused several global pandemics since 1918 until recently in 2009 and still claims over 500,000 lives annually. Therefore, like SARS-CoV-2, early detection and isolation of asymptomatic or minimally symptomatic influenza patients are critical.

About the study

In the current study, researchers measured MDW sensitivity and specificity to diagnose SARS-CoV-2 and influenza infections. Additionally, they assessed other leukocyte parameters and the association between MDW levels and disease severity in patients with and without these viral infections.

They conducted the study between January 21, 2020, and July 14, 2021, in the ED of a hospital in Baltimore, Unites States (US). The study participants were adult patients over 18 years for whom clinicians ordered a complete blood count (CBC) within six hours of arriving in the ED.

The researchers collected all demographic, clinical, and hospital-use data from the electronic health record (EHR) system. The team mined co-morbidities by a combination of diagnostic codes for the health problems available in the EHR.

The researchers analyzed MDM using a UniCel DxH900 analyzer but a separate hematology analyzer to measure CBC parameters, including monocyte percentage, white blood cell (WBC) count, and neutrophil-to-lymphocyte ratio (NLR).

The team set abnormal WBC count, monocyte percentage, and NLR at greater than 12 x 109/L or less than 4 x 109/L, greater than 10%, and >10, respectively. The data for study outcomes were collected from the EHR and remained limited to the index hospital encounter. The study primarily identified the SARS-CoV-2 or influenza infection via reverse transcriptase-polymerase chain reaction (RT-PCR) assay.

The secondary outcome encompassed data for intensive care unit (ICU) admission, hospitalization, and mortality. The researchers modeled MDW and other leukocyte parameters in isolation as continuous variables expressed as median with interquartile range (IQR) and compared these using the Mann-Whitney U test.

Study findings

Of 1,923 ED visits, 104 (5.4%) tested positive for SARS-CoV-2. Conversely, of 2,090 ED visits, 48 (2.3%) tested positive for influenza. In the SARS-CoV-2-positive patient group, 42.3% required hospital admission, while 22.9% required hospitalization in the influenza-positive patient group. Patients positive for SARS-CoV-2 demonstrated higher severity of illness compared to influenza. Also, MDW levels were substantially higher among SARS-CoV-2-positive patients than those who tested negative (median 23U vs. 18.9U).

Similarly, the median MDW for influenza-positive patients was higher than the influenza-negative patients (24.1U vs. 19.1U). NLR values were similar for groups with and without viral infection, whereas median monocyte percentage and WBC slightly varied in positive versus negative patients for both viruses.

The overall diagnostic accuracy of MDW was similar, with an area under the receiver operating characteristic curve (AUC) of 0.83 for both SARS-CoV-2 and influenza. The authors noted negative likelihood ratios of 0.24 and 0.17 for SARS-CoV-2 and influenza infections. These ratios translated to an MDW value below 20U, indicating an approximate four-to-six-fold decrease in odds of having a viral infection.

Notably, median MDW values were below the established cutoff of 20U in uninfected patients discharged home after ED visit; however, these values were elevated in critically patients requiring ICU admission.

Further, the study findings indicated that SARS-CoV-2 or influenza might be the confounders when MDW served as a screening tool for sepsis. Nevertheless, the study reported the weighted mean difference in the MDW for patients with SARS-CoV-2 compared to patients without this virus was 4.2U. This data strongly suggest MDW is a reliable marker of SARS-CoV-2 infection.

The study accomplished better diagnostic performance for SARS-CoV-2 and influenza with an AUC of 0.83, suggesting MDW as an effective, ED population-wide screening test. More importantly, the sensitivity of MDW for SARS-CoV-2 infection exceeded that of most antigen-based SARS-CoV-2 testing platforms. MDW was also more sensitive than several presently used rapid influenza detection tests in outpatient and ED settings.

Conclusions

Together, the study findings indicated that MDW, a component of the CBC differential, ordered routinely regardless of clinical suspicion for viral infection, achieved a high level of diagnostic performance. In the future, research should focus on implementing MDW-based screening programs for SARS-CoV-2 and influenza infections and evaluate their utility in clinical settings.

*Important notice

Research Square publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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