Performance of monocyte distribution width in predicting the need for respiratory support in COVID-19 patients

doi:10.21203/rs.3.rs-2250629/v1

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Performance of monocyte distribution width in predicting the need for respiratory support in COVID-19 patients

https://doi.org/10.21203/rs.3.rs-2250629/v1

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Background: The emergence of the novel coronavirus caused by SARS-CoV-2 infection resulted in a global pandemic whose clinical features ranged from asymptomatic infection to a severe fulminant respiratory distress and death. The monocyte distribution width (MDW) is a novel inflammatory biomarker that reflects morphological changes in response to inflammation and has been shown to useful in identifying COVID-19 identification and predicting death. There are limited data on the use of MDW for predicting the need for respiratory support. The aim of the study was to determine the performance of MDW in predicting the need for respiratory support in patients with SAR-CoV-2 infection.

Methods: Our retrospective cohort study was conducted at Thammasat University hospital, Thailand. Hospitalized COVID-19 adult patients were enrolled. Respiratory support was any one of oxygen therapy, high flow nasal cannula, non-invasive or invasive mechanical ventilation. The performance of MDW was measured using the area under the receiver operating characteristic (AuROC) curve and multivariable logistic regression analysis evaluated the independent effect of MDW on respiratory support.

Results: Of the 250 enrolled patients, 122 (48.8%) patients were received respiratory support. The mean MDW was significantly higher in respiratory support group: 27.2±4.6 vs 23.6±4.1 (p<0.001). An MDW≥25 alone had the best AuROC characteristics: 0.70 [95% confidence interval (CI); 0.65-0.76] for a sensitivity, specificity, and positive likelihood ratio of 71.9%, 68.2%, and 2.26, respectively. Combining age, respiratory rate and oxygen saturation increased the AUROC to 0.84 (95% CI; 0.79-0.88). Increasing age [adjusted odds ratio (aOR) 1.03], respiratory rate (aOR 1.11) and MDW≥25 (aOR 3.42) significantly increased the odds of needing respiratory support whilst a higher oxygen saturation significantly reduced it (aOR 0.81).

Conclusion: MDW is a promising biomarker for predicting the need of respiratory support in COVID-19 patients and can easily be implemented in routine clinical practice.

monocyte distribution width

COVID-19

respiratory support

high flow nasal cannula

mechanical ventilation

mortality

The coronavirus 2019 (COVID-19) pandemic, caused by the SARS-CoV-2 virus, has been the most concerning public health crisis in recent times, affecting millions of individuals globally. During the COVID-19 outbreak in Thailand, there were more than 4,500,000 confirmed cases and more than 30,000 deaths. The pathogenesis of severe COVID-19 is attributed to a cytokine storm that occurs when the viral load is falling. The lung is a key target organ and respiratory failure is the common cause of death [1].

There has been growing interest in many biomarkers and their value in predicting COVID-19 severity and death [2, 3]. High levels of blood biomarkers and inflammatory cytokines [eg. C-reactive protein (CRP), interleukin-6 (IL-6)] are associated with disease severity and mortality. In response to SARS-CoV-2 infection, neutrophils and monocytes are recruited first followed by cytokines that characterize the innate and adaptive immune responses. The monocyte distribution width (MDW) is a novel biomarker, which is related to the size/volume of monocytes and which increases when activated monocytes increase in size during the inflammatory processes. MDW is used as a biomarker for sepsis identification and prognostication [4, 5] and, more recently, has been used to predict poor outcomes in COVID-19 [6, 7].

A MDW value ≥ 20.1 was helpful to detect SARS-CoV-2 infection in confirmed COVID-19 patients [8] and a higher value (MDW 28.8) was associated with ICU admission [9]. The difference between first and last MDW less than 1 correlated with unfavorable outcomes [10].

Given the ease at which the MDW can be measured in a full blood count, a routine hospital investigation, and its low cost, it has the potential to be used as a biomarker in clinical practice. Data on the correlation between MDW and the need for respiratory support in COVID-19 patients are few. We, therefore, set out to investigate this relationship in patients with COVID-19 infection.

Study design

This single-center, retrospective cohort study was conducted at Thammasat University hospital, a tertiary care referral hospital for COVID-19 patients in the Northern region of Bangkok and central Thailand. Approval from Thammasat university ethics committee was obtained (ID MTU-EC-EM-0-262/64). We followed the Strengthening the Reporting of Observational Studies in Epidemiology statement recommendations.

Participants

Adult patients aged 18 years or older with confirmed COVID-19 infection who presented to emergency department and were admitted in the Thammasat University hospital between 1st May − 31st August 2021 were enrolled. Patients who received treatment from other hospitals prior to admission and were referred patients were excluded. Patients were admitted to three levels of care: the intensive care unit (ICU), intermediate-ICU, and the infectious disease ward, according to their severity.

Data sources and collection

All of data were retrieved from electronic medical record. Demographic data were collected and included age, comorbidities, initial sequential organ failure assessment (SOFA) score, and vital signs were also recorded. Laboratory parameters including complete blood count with MDW, creatinine, lactate, C-reactive protein (CRP), D-dimer, procalcitonin, cardiac troponin, and LDH.

Respiratory support interventions were defined as any one of the following: oxygen therapy, non-invasive ventilation (NIV), high flow nasal cannula (HFNC), and invasive mechanical ventilation (IMV). Disease complications and outcomes – death or survived were gathered. The Complete blood count profiles measured by Unicel DxH 900 (Beckman coulter, Inc., CA, USA) from EDTA venous samples.

Sample size estimation

We first performed a pilot study, gathering data on 44 patients admitted to our hospital in March 2021. Of those, 21/33 (63%) patients with a MDW > 20 and 2/11(18.1%) patients with MDW ≤ 20 received respiratory support. A two-sample comparison of these proportions (63 vs. 18%) was used to calculate the sample size with 90% power and 5% alpha error (two-sided test). The estimated sample size was 56 patients per MDW group.

Statistical analysis

Descriptive data were summarized as mean and standard deviation (SD), median and interquartile range (IQR), or percentage, as appropriated. All variables were compared between patients who received respiratory support and those who did not. The unpaired ttest and exact probability test were used for continuous and categorical variables, respectively. The area under the receiver operating characteristic curve (AUROC) was used to assess the performance of the MDW for predicting the need for respiratory support. The cut-off value of MDW was determined using the sensitivity, specificity, and positive likelihood ratio. Multivariable logistic regression analysis was used to evaluate the independent effect of MDW on respiratory support needs. A p value less than 0.05 was statistically significant. We used STATA program version 14 (StataCorp, College Station, TX) for data analysis.

Participants

From 1st May to 31st August 2021, 421 confirmed COVID-19 patients were identified, of these, 170 were excluded due to prior treatment from other hospitals (101), and referral (70) (Fig. 1). Of the 250 patients who were eligible for the study, 122 patients were received respiratory support whilst 128 patients did not. Among these 250 patients, 50 (20%), 95 (38%), and 105 (42%) were admitted to ICU, intermediate ICU and cohort infectious disease ward, respectively.

Descriptive data

The patients who required respiratory support were significant older (57.1 vs 46.6, p < 0.001), had chronic kidney disease (83.3 vs 16.7, p 0.008), higher respiratory rate (23.0 vs 20.6, p < 0.001), less oxygen saturation (92.8% vs 97.1%, p < 0.001) and higher SOFA score on admission (3.4 vs 0.4, p < 0.001) compare with those without respiratory support. Severe and critical COVID-19 infection were received more respiratory support. Clinical characteristics and demographic data are summarized in Table 1.

Table 1

Patient baseline characteristics. BMI; body mass index, SOFA; sequential organ failure assessment.
Characteristic	Respiratory support (N = 122) n (%)	Non-respiratory support (N = 128) n (%)	p-value
Male	76 (62.3)	70 (54.7)	0.171
Age, mean (± SD) (years)	57.1 (± 16.8)	46.6 (± 16.5)	< 0.001
BMI ≥ 25 (kg/m²)	87 (71.3)	88 (68.6)	0.721
Comorbidity
Hypertension	52 (46.7)	39 (30.5)	0.036
Diabetes mellitus	31 (25.4)	22 (17.2)	0.178
Obesity (BMI ≥ 30)	37 (30.3)	31 (24.2)	0.380
Lung disease	7 (5.7)	3 (2.3)	0.164
Chronic kidney diseases	10 (8.2)	2 (1.6)	0.008
Dyslipidemia	24 (19.7)	28 (21.9)	0.716
Vital signs:
Body temperature, mean (± SD) (ºC)	37.6 (0.9)	37.4 (0.9)	0.098
Respiratory rate, mean (± SD) (/min)	23.0 (4.5)	20.6 (2.9)	< 0.001
Mean arterial pressure, mean (± SD) (mmHg)	98.5 (13.1)	96.8 (13.5)	0.324
Pulse rate, mean (± SD) (beat per min)	95.1 (15.5)	92.0 (13.9)	0.099
Oxygen saturation, mean (± SD) (%)	92.8 (7.6)	97.1 (2.2)	< 0.001
SOFA score at admission, mean (± SD)	3.4 (2.6)	0.6 (1.2)	< 0.001
Classification of COVID-19			< 0.001
Mild	4 (3.2)	127 (99.2)
Severe	76 (62.3)	1 (0.8)
Critical	42 (34.4)	0 (0)

The MDW and CRP values were significantly higher in the respiratory support group: 27.2 ± 4.6 vs 23.6 ± 4.1, p < 0.001) and (78.2 vs 25.5, p < 0.001), respectively. Among all CBC parameters, neutrophil count, lymphocyte count, platelets, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were significant different between the two groups as were the mean ferritin and procalcitonin levels. By contrast, serum lactate, D-dimer, and interleukin-6 were not different between the two groups (p = 0.062 and p = 0.676, respectively). Additional laboratory parameters are presented in Table 2.

Table 2

Laboratory parameters at admission. IQR; interquartile range, CRP; C-reactive protein, LDH; lactate dehydrogenase, NT-proBNP; N-terminal pro b-type natriuretic peptide.
Laboratory	Respiratory support (N = 122)		Non- RS support (N = 128)		p-value
Laboratory	Mean	±SD	Mean	±SD
Complete blood count and coagulogram
Monocyte distributive width day 1 (Unit)	27.2	4.6	23.6	4.1	< 0.001
Hemoglobin (g/dl)	13.8	1.8	13.4	1.8	0.110
White blood cell count (median; IQR) (10³/uL)	6.5	4.9, 8.5	6	4.7, 7.6	0.215
Neutrophil count (median; IQR) (cells/mm³)	4617	3120, 6873	3469	2562, 4900	< 0.001
Lymphocyte count (median; IQR) (cells/mm³)	999	666,1470	1540	1196,1890	< 0.001
Platelets (10³ /uL)	202.1	82.8	237.9	88.8	< 0.001
Neutrophil-to-lymphocyte ratio (median; IQR)	4.42	2.54, 7.94	2.22	1.42, 3.58	< 0.001
Platelets-to-lymphocyte ratio (median; IQR)	184.6	113.8, 291.9	139.7	103.2, 195.9	0.001
Blood chemistry
Creatinine (median; IQR) (mg/dL)	1.01	0.7, 1.3	0.85	0.69, 1.04	0.002
Lactate (median; IQR) (mmol/L) (n = 91)	2.9	1.8, 3.8	2.3	1.64, 2.6	0.062
CRP (median; IQR) (mg/dL) (n = 180)	78.21	42.3, 87.52	25.5	8.42, 68.35	< 0.001
Interleukein-6 (median; IQR) (pg/ml) (n = 24)	19.8	13.4, 45.82	27.48	4.77, 50.19	0.676
D-dimer (median; IQR) (ng/ml) (n = 96)	666	395, 1980	672	365, 1101	0.511
Ferritin (median; IQR) (ng/ml) (n = 9)	881	430, 1124	27.5	12, 43	0.040
Procalcitonin (median; IQR) (ug/L) (n = 125)	0.17	0.09, 0.37	0.11	0.07, 0.22	0.019
Cardiac troponin (median; IQR) (ng/ml) (n = 24)	0.04	0.009, 0.165	0.01	0.004, 0.095	0.260
NT-proBNP (median; IQR) (pg/mL) (n = 16)	1724.5	572, 3766	20	39, 201	0.080
LDH (median; IQR) (U/L) (n = 60)	420	293, 554	353	233, 437	0.022

Outcome data

122/250 (48.8%) patients received respiratory support that consisted mostly high-flow nasal cannula, followed by invasive mechanical ventilation, non-invasive ventilation, and oxygen mask with a bag: 113 (45.2%), 41 (16.4%), 20 (8%), and 17(6.8%), respectively, whilst 132 received conventional oxygen cannula. Vasopressor and renal replacement therapy were also significantly higher in the respiratory support group: 25/122 (20.5%) vs. 1/128 (0.7%, p < 0.001) and 9/122 (7.4%) vs 1/128 (0.7%), p = 0.008). COVID-19 patients who required respiratory support had a lower rate of survival (74.8% vs 100%, p < 0.001). A longer mean length of hospital stay was also found in the respiratory support group 17.5 vs 7.8 days (p < 0.001). In addition, the mortality rate at 7,14, and 28 days were significantly higher in the respiratory support group. The longer length of stay was also found in patients who require respiratory support (17.5 days vs 7.8 days, p < 0.001). The treatments and results are shown in Table 3.

Table 3

Treatment and outcomes
	N (%)
Received Respiratory support (N = 122)
Oxygen mask with bag	20 (16.4)
Non-invasive ventilation	17 (13.9)
High-flow nasal cannula	113 (92.6)
Mechanical ventilation	41 (33.6)
Treatment
	Respiratory support (N = 122) n (%)	Non-respiratory support (N = 128) n (%)	p-value
Vasopressor requirement	25 (20.5)	1 (0.7)	< 0.001
Renal replacement therapy	9 (7.4)	1 (0.7)	0.008
Result
7-day mortality	5 (4.1)	0 (0)	0.025
14-day mortality	20 (16.4)	0 (0)	< 0.001
28-day mortality	26 (21.3)	0 (0)	< 0.001
Length of stay mean (± SD) (day)	17.5 (10.1)	7.8 (4.9)	< 0.001
Survival to discharge	90 (74.8)	128 (100)	< 0.001

Compared to the non-support group, the respiratory support group had significantly higher complication rates (Table 4). Almost all patients in respiratory support group developed respiratory failure [118 (96%)], ARDS [40 (32.7%)] and required invasive mechanical ventilation [41 (34%)]. Hospital acquired pneumonia, sepsis, septic shock, acute kidney injury and cardiac arrest were more frequent in the respiratory support group (Table 4).

Table 4

Complications
Complication	Respiratory support (N = 122) n (%)	Non-respiratory support (N = 128) n (%)	p-value
Hypotension	32 (26.2)	1 (0.7)	< 0.001
Arrhythmia	22 (18)	3 (2.3)	< 0.001
Cardiac arrest	32 (26.2)	0 (0)	< 0.001
Respiratory failure	118 (96.7)	1 (0.7)	< 0.001
Acute respiratory distress syndrome (ARDS)	40 (32.8)	1 (0.7)	< 0.001
Hospital-acquired pneumonia	54 (44.3)	4 (3.1)	< 0.001
Sepsis	49 (40.2)	1 (0.7)	< 0.001
Septic shock	28 (23)	0 (0)	< 0.001
Acute kidney injury	41 (33.6)	7 (5.5)	< 0.001
Gastrointestinal bleeding	10 (8.2)	1 (0.7)	0.004
Received blood transfusion	11 (9.0)	0 (0)	< 0.001
Pulmonary embolism	4 (3.3)	0 (0)	0.053
Transaminitis	24 (19.7)	14 (10.9)	0.048

We identified a MDW value of ≥ 25 as best cutoff for predicting the need for respiratory support in COVID-19 patients. The AuROC was 0.70 [95% confidence interval (CI); 0.64–0.76, sensitivity 71.9, specificity 68.2, positive likelihood ratio 2.26] (Table 5). By logistic regression analyses, the independent factors associated with need respiratory support were a MDW ≥ 25, increasing age, increasing respiratory rate, and a lower oxygen saturation (Table 6).

Table 5

Cut point of MDW in COVID-19 patients with respiratory support requirement. 95% CI; 95% confidence interval, LR+; likelihood ratio of positive, LR-; likelihood ratio of negative.
Cut point	AuROC	Sensitivity, % (95% CI)	Specificity, % (95% CI)	LR+	LR-
(≥ 20)	0.572	96.7 (91.8–99.1)	17.8 (11.7–25.5)	1.18	0.19
(≥ 21)	0.618	95.9 (90.6–98.6)	27.9 (20.4–36.5)	1.33	0.15
(≥ 22)	0.645	94.2 (88.4–97.6)	34.9 (26.7–43.8)	1.45	0.17
(≥ 23)	0.674	86.8 (79.4–92.2)	48.1 (39.2–57.0)	1.67	0.28
(≥ 24)	0.671	76.9 (68.3–84.0)	57.4 (48.4–66.0)	1.80	0.40
(≥ 25)	0.700	71.9 (63.0-79.7)	68.2 (59.4–76.1)	2.26	0.41
(≥ 26)	0.657	56.2 (46.9–65.2)	75.2 (66.8–82.4)	2.27	0.58
(≥ 27)	0.630	46.3 (37.2–55.6)	79.8 (71.9–86.4)	2.30	0.67
(≥ 28)	0.582	28.9 (21.0-37.9)	87.6 (80.6–92.7)	2.33	0.81
(≥ 29)	0.581	24.0 (16.7–32.6)	92.2 (86.2–96.2)	3.09	0.82
(≥ 30)	0.564	19.0 (12.4–27.1)	93.8 (88.1–97.3)	3.07	0.86

Table 6

Logistic regression of monocyte distribution width (MDW) ≥ 25 for respiratory support.
Respiratory support	Odd ratio	95% CI	p-value
MDW ≥ 25	3.42	1.85–6.33	< 0.001
Age	1.03	1.01–1.05	< 0.001
Respiratory rate	1.11	1.00–1.21	0.041
Oxygen saturation (SpO₂)	0.81	0.72–0.91	< 0.001
*adjusted for age, respiratory rate, and oxygen saturation.

The AuROC of clinical features (age, RR, and oxygen saturation), CRP, MDW ≥ 25, and MDW ≥ 25 plus clinical features for predicting respiratory support requirement are shown in Table 7; clinical features plus a MDW ≥ 25 gave the best result and this was significantly better (p < 0.001) than clinical features alone.

Table 7

The area under receiver operative curve (AUROC) of clinical features, CRP, MDW ≥ 25, and MDW ≥ 25 with clinical features as predictors of respiratory support and 28-day mortality in COVID-19 patients. RR; respiratory rate, CRP; c-reactive protein, MDW; monocyte distribution width.
Predictor	AuROC	95%CI	p-value
Respiratory support
Clinical features only (age + RR + SpO₂)	0.82	0.76–0.87	Ref
CRP	0.72	0.65–0.80	< 0.001
MDW ≥ 25	0.70	0.65–0.76	< 0.001
MDW ≥ 25 + clinical features*	0.84	0.79–0.88	< 0.001
28-day mortality
Clinical features only (age + RR + SpO₂)	0.87	0.80–0.93	Ref
CRP	0.73	0.63–0.83	0.144
MDW ≥ 25	0.64	0.55–0.73	< 0.001
MDW ≥ 25 + clinical features*	0.87	0.79–0.94	1.000
*Reference with clinical features (age, respiratory rate, and oxygen saturation),

In this retrospective study, we have shown that an increase in the MDW was associated with more severe disease, the need for respiratory support and death. SARS-CoV-2 infection ranges in severity from asymptomatic to mild flu like symptoms to severe respiratory failure and multi-organ failure. Severe disease is related to the cytokine storm that develops in the second phase of the illness as the viral load is declining. Several studies have addressed the usefulness of biomarkers for predicting disease severity and outcomes of COVID-19 infection like CRP, thrombocytopenia, procalcitonin, d-dimer and LDH [2, 3].

When host cells are infected by viruses, the innate immune system is activated to eliminate them, and interferon plays an important role. Macrophages are one source of type I interferon and NK cells are primed to be activated by type 1 interferon, which the produce interferon-gamma (type 2 interferon) to prime macrophages. In a hyperactive state, macrophages grow larger and their ability to phagocytose and present antigens increases. During the inflammatory process, monocytes are attracted away from the circulation [11, 12] and when activated their size increases [13]. This change in size and MDW can be measured in new generation hematologic analyzers. MDW has been used as a marker for the early detection of sepsis and a predictor of prognosis [4, 5]. It is also raised in SARS-CoV-2 infection and is associated with poor outcomes [10].

Many biomarkers were discovered in COVID-19 patients to predict outcomes and guiding treatment. CRP is a predictor for disease severity and poor outcomes [3, 14]. High CRP associated with increase in-hospital mortality [15]. Riva et al. reported a significant positive correlation between MDW and inflammatory markers such as CRP, fibrinogen, and ferritin in COVID-19 patients [6]. Alsuwadi revealed that high MDW value (> 24.6) had a strong correlation with poor prognosis COVID-19 biomarkers [7]. Similar to previous studies, CRP was elevated in patients with SARS-CoV-2 infection and statistically significant higher in the respiratory support group. Among all CBC parameters, neutrophil count, lymphocyte count, platelets, NLR and PLR are significant higher in respiratory support group. NLR and PLR are combined hematologic biomarker elevated in COVID-19 patients [16] and associated with severe COVID-19 [17, 18].

We identified a MDW value of 25 as the best cut-off to predict the need for respiratory support for a sensitivity of 72% and specificity of 68%. This finding is consistent with that of Kim et al. who showed that a MDW of 25.8 at admission predicted the need for high flow nasal cannula or invasive mechanical ventilation in COVID-19 patients [19]. However, this MDW cut off demonstrated a fair predictive discrimination for 28-day mortality (AuROC 0.64), which differed from the study of Riva et al. who found that a MDW cutoff 26.4 was associated with fatal outcome with an AuROC of 0.76, (95% CI; 0.66–0.87) and a sensitivity of 0.75 and specificity of 0.70) [6].

Crouser et al. determined that a MDW > 20 distinguished sepsis from other conditions in high-risk emergency patients [20], while a MDW ≥ 20 favored a diagnosis of COVID-19 over other respiratory tract infections and influenza with AuROCs of 0.70 and 0.63 for COVID-19 and influenza, respectively [21]. In another study, the same cut off showed an outstanding diagnostic performance for predicting SARS-CoV-2 infection with an AuROC of 0.91, sensitivity 98%, specificity 65%, positive predictive value 51.9%, and negative predictive value 98.6% [9]. Previous studies have shown that a MDW ≥ 21 was the best threshold for predicting hospital stay longer than 14 days (OR 5.67, 95% CI; 1.19–27.10) [22] and a MDW > 28 was significantly associated with ICU admission [9].

We also found that a combination of age, respiratory rate and oxygen saturation had high independent value and when combined with a MDW ≥ 25, increased the predictive value of MDW for predicting the need for respiratory support and 28-day mortality (AuROC 0.82, 95% CI; 0.76–0.87 and AUROC 0.87, 95% CI; 0.80–0.93, respectively). Grasselli et al. revealed that a 10-year increase in age was associated with an increased risk of mortality of 75% (HR 1.75, 95% CI; 1.60–1.92) [23]. Respiratory rate has also been identified as an independent predictor for a 5-fold increase in mortality (OR 4.90, 95% CI; 1.08–22.24) [24] and tachypnea or hypoxemia (SpO₂ ≤ 93%) were strongly associated with severe COVID-19 with ORs of 7.51 (95% CI; 1.66–33.91) and 4.69 (95% CI; 1.56–14.09), respectively [25]. Reyes et al. found that age and respiratory rate associated with 28-day mortality (OR 2.42, 95% CI; 2.36–2.48 and OR 1.5, 95% CI; 1.13–1.17, respectively [26]. Our study had several limitations. Firstly, this was a retrospective study from one a single center in a middle-income country, so its generalizability is limited. We did not exclude other causes of increased MDW, e.g. hematologic malignancy. Nevertheless, this study provided valuable data of MDW and clinical parameters for predicting respiratory support in COVID-19 patients. The results can be practically applied in real world practice.

In conclusion, we have shown that a MDW ≥ 25 is a promising biomarker for predicting the need of respiratory support in COVID-19 patients. Measuring MDW is inexpensive, reported as part of a routine CBC, and easily implemented. More research is required to examine the performance of MDW with other biomarkers, clinical parameters, or severity scores.

MDW

monocyte distribution width

CRP

C-reactive protein

IL6

interleukin-6

ICU

Intensive care unit

SOFA

sequential organ failure assessment score

NIV

non-invasive ventilation

HFNC

high flow nasal cannula

IMV

invasive mechanical ventilation

Ethical approval

The trial was approved by the Human Research Ethics Committee of the Faculty of Medicine of Thammasat University (ID MTU-EC-EM-0-262/64). Because this study is observational study from retrospectively collected data, the process of obtaining written informed consent was waived.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that there have no competing interests.

Funding

This study is funded by the Research group in Emergency Medicine and Emergency Critical Care of the Faculty of Medicine of Thammasat University.

Authors’ contributions

KD, WS, and SW were responsible for conceptualization and methodology. KD and CH were responsible for data curation and investigation. KD, SW and WS were responsible for formal analysis. KD and WS were responsible for supervision, validation, and review and editing. KD, SW, WS, CH, and PP were responsible for original draft preparation. All authors contributed to data interpretation and discussion and approved the published version of the manuscript.

Acknowledgement

We greatly appreciate all the members of the Thammasat University Hospital for their efforts and devotion during the crisis of COVID-19. This study is part of the project from the Research Group in Emergency Medicine and Emergency Critical Care of the Faculty of Medicine of Thammasat University.

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No competing interests reported.

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Performance of monocyte distribution width in predicting the need for respiratory support in COVID-19 patients

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