{"id":1398,"date":"2025-03-13T13:18:59","date_gmt":"2025-03-13T04:18:59","guid":{"rendered":"https:\/\/lmi.jp\/articles\/?p=1398"},"modified":"2026-01-20T11:31:31","modified_gmt":"2026-01-20T02:31:31","slug":"retrospective-study-of-the-prognosis-viral-load-estimated-by-rt-pcr-cycle-threshold-values-and-monocyte-distribution-width-%ef%bc%88mdw%ef%bc%89-in-covid-19","status":"publish","type":"post","link":"https:\/\/lmi.jp\/articles\/2025\/03\/13\/retrospective-study-of-the-prognosis-viral-load-estimated-by-rt-pcr-cycle-threshold-values-and-monocyte-distribution-width-%ef%bc%88mdw%ef%bc%89-in-covid-19\/","title":{"rendered":"Retrospective study of the prognosis, viral load estimated by RT-PCR cycle threshold values, and monocyte distribution width \uff08MDW\uff09 in COVID-19"},"content":{"rendered":"\n

Kentaro Wakamatsu<\/strong><\/a>, MD, PhD1<\/sup><\/strong>, Zenzo Nagasawa<\/strong><\/a>, MT, PhD2<\/sup><\/strong>, Kouta Katsuki<\/strong><\/a>, MT 3<\/strong><\/sup>, Hiroyuki Kumazoe<\/a><\/strong>, MD1<\/strong><\/sup>, Masayo Yasuda<\/a><\/strong>, MT 3<\/sup><\/strong>, Sae Kawamoto<\/a><\/strong>, MT 3<\/sup><\/strong>, Ayano Kawamura<\/a><\/strong>, MT 3<\/sup><\/strong>, Junji Otsuka<\/a><\/strong>, M.D, PhD1<\/sup><\/strong>, Ruriko Kiyotani<\/a>1<\/sup><\/strong>, Izumi Fukui<\/a>1<\/sup><\/strong>, Sanae Maki<\/a><\/strong>, MD, PhD1<\/sup><\/strong>, Nobuhiko Nagata<\/a><\/strong>, MD, PhD4<\/sup><\/strong>, Masayuki Kawasaki<\/a><\/strong>, MD, PhD1<\/sup><\/strong>, Satomi Asai<\/a><\/strong>, MD, PhD5<\/sup><\/strong>, Hozumi Yamada<\/a><\/strong>, MD, PhD6<\/sup><\/strong><\/p>\n\n\n\n

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\u2003 Cite<\/span><\/span><\/i><\/i><\/span><\/summary>
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Wakamatsu K, Nagasawa Z, Katsuki K, Kumazoe H, Yasuda M, Kawamoto S, Kawamura A, Otsuka J, Kiyotani R, i Fukui I, Maki S, Nagata N, Kawasaki M, Asai S, Yamada H. Retrospective study of the prognosis, viral load estimated by RT-PCR cycle threshold values, and monocyte distribution width \uff08MDW\uff09 in COVID-19. Lab Med Int 2024; 3(3): 84-94. doi: 10.51041\/lmi.3.3_84<\/p>\n<\/div><\/details>\n<\/div>\n\n\n\n

Original
Lab Med Int 2024; 3(3): 84-94<\/p>\n\n\n\n

\u2020Correspondence: Department of Respiratory Medicine, National Hospital Organization Omuta National Hospital
1044-1 Oaza, Tachibana, Omuta City, Fukuoka 837-0911, Japan
E-mail: wakamatsu.kentaro.fe”@”mail.hosp.go.jp
Received October 3, 2023; accepted May 14, 2024
*1 Department of Respiratory Medicine, National Hospital Organization Omuta National Hospital
*2 Department of Medical Technology and Science, Faculty of Fukuoka Health Care, International University of Health and Welfare
*3 Department of Clinical Laboratory, National Hospital Organization Omuta National Hospital
*4 Department of Respiratory Medicine, Fukuoka Sanno Hospital
*5 Department of Laboratory Medicine, Tokai University School of Medicine
*6 Department of Respiratory Medicine, Keitendo Koga Hospital<\/span><\/strong><\/p>\n\n\n\n

Download PDF<\/span><\/a><\/div>\n\n\n\n

ABSTRACT<\/h2>\n\n\n\n

Objectives: <\/strong>There are reports on prognostic factors including viral load, the presence\/absence of pneumonia and MDW at the time of hospital admission, however there are several challenges.
Methods:<\/strong>
Cohort 1: A total of 235 patients diagnosed with COVID-19 admitted to our hospital between July 2020 and July 2021 were investigated for prognostic factors including the presence\/absence of concurrent pneumonia and viral load.
Cohort 2: This investigation planned to investigate the prognostic significance of MDW included 80 patients diagnosed with COVID-19 who were admitted to our hospital between April 2021 when automatic blood cell counter measuring MDW was introduced in our hospital and September 2021.
Results:<\/strong>
Cohort 1: Based on the results of multivariate analysis, advanced age, high body mass index \uff08BMI\uff09, diabetes mellitus, and concurrent pneumonia were considered to be independent poor prognostic factors.
Cohort 2: The mean age and MDW on admission were significantly higher in the poor prognosis group \uff08n=38\uff09 than in the good prognosis group \uff08n=42\uff09 \uff08p<0.01\uff09, while high BMI, diabetes mellitus, and concurrent pneumonia showed no significant difference between the two groups.
Conclusions: MDW may be used as a biomarker of aggravation of COVID-19 on admission.<\/p>\n\n\n\n

\u3014Lab Med Int 2024; 3(3): 84-94\u3015<\/p>\n\n\n\n

Key Words<\/h3>\n\n\n\n

COVID-19, SARS-CoV-2, MDW, Ct, Prognosis<\/p>\n\n\n\n


List of abbreviations<\/strong><\/h3>\n\n\n\n


Alb=albumin; ALT=alanine aminotransferase; APTT=activated partial thromboplastin time; AUC=area under the curve; AST=aspartate aminotransferase; BMI=body mass index; BUN=blood urea nitrogen; CBC=complete blood count; CDC=Center for Disease Control and Prevention; CK=Creatine Kinase; COPD=chronic obstructive pulmonary disease; COVID-19 =coronavirus disease 2019; Cre=creatinine; CRP=C-reactive protein; Ct=cycle threshold; CT=Computed Tomography; EDTA=ethylenediaminetetraacetic acid; Glu=glucose; Hb=hemoglobin; HbA1c=Hemoglobin A1c; KL-6=krebs von den lungen-6; LD=lactate dehydrogenase; Ly=lymphocyte count; MDW=monocyte distribution width; Neu=Neutrophil count; NLR=neutrophil-to-lymphocyte ratio; NPV=negative predictive value; PCR=polymerase chain reaction; PCT=procalcitonin; Plt=platelet count; PPV=positive predictive value; PT=prothrombin time; RBC=red blood cell; RNA=ribo nucleic acid; ROC=receiver operating characteristic; RT-PCR=Reverse Transcription-PCR; SARS-CoV-2=severe acute respiratory syndrome coronavirus 2; SIRS=systemic inflammatory response syndrome; SP-A=surfactant protein A; SP-D=surfactant protein D; SpO2=oxygen saturation of peripheral artery; T-bil=total bilirubin; US=United States; WBC=white blood cell; WHO=World Health Organization<\/p>\n\n\n\n

I\uff0eIntroduction<\/h2>\n\n\n\n

Coronavirus disease 2019 \uff08COVID-19\uff09 is an acute respiratory infection caused by severe acute respiratory syndrome coronavirus 2 \uff08SARS-CoV-2\uff09 which was originally reported in December 2019 as \u201cpneumonia of unknown cause\u201d in Wuhan, Hubei, China. It was officially pronounced a new type of coronavirus by the Chinese Center for Disease Control and Prevention \uff08CDC\uff09 on January 7, 2020. 1\uff09<\/strong><\/sup>
We often experience patients with COVID-19 not having any serious respiratory symptoms on admission, however rapid aggravation of respiratory status during follow-up. Therefore, for the treatment of COVID-19, it is important to diagnose the disease early, predict its aggravation, and provide therapeutic intervention early. The 6.2th edition of the Clinical Practice Guidelines for Novel Coronavirus Infections \uff08COVID-19\uff09 lists the following risk factors for aggravation: \uff081\uff09 elderly persons aged 65 years or older, \uff082\uff09 malignant tumor, \uff083\uff09 chronic obstructive pulmonary disease \uff08COPD\uff09, \uff084\uff09 chronic kidney disease, \uff085\uff09 type 2 diabetes mellitus, \uff086\uff09 hypertension, \uff087\uff09 dyslipidemia, \uff088\uff09 obesity \uff08BMI of 30 kg\/m\u00b2 or higher\uff09, \uff089\uff09 smoking, \uff0810\uff09 immunodeficiency after solid organ transplant, \uff0811\uff09 late pregnancy. Multiple factors have also been listed as markers of aggravation, including \uff081\uff09 elevated D-dimer, \uff082\uff09 elevated CRP, \uff083\uff09 elevated LD, \uff084\uff09 elevated ferritin, \uff085\uff09 lymphopenia, \uff086\uff09 elevated creatinine, \uff087\uff09 elevated troponin, and \uff088\uff09 elevated KL-6. 2
Computed Tomography \uff08CT\uff09 scans can be performed relatively easily, since our hospital has a dedicated CT examination device for COVID-19 patients. It is also possible to estimate the viral RNA amount using the number of positive Ct value of the Smart Gene\u00ae<\/sup>, a PCR reagent for SARS-CoV-2 detection \uff08MIZUHO MEDY Co., Ltd.\uff093\uff09<\/sup><\/strong>. There are reports on prognostic factors including viral load and the presence\/absence of pneumonia at the time of hospital admission, however there are several challenges.
As previously reported, in recent years, devices that can analyze detailed white blood cell information with automated hematology analyzers have appeared, and it has been reported that Monocyte Distribution Width \uff08MDW\uff09 is useful for diagnosing sepsis and assessing the severity of COVID-19 4\uff09-23\uff09<\/sup><\/strong>.
In this study we examined prognostic factors in COVID-19, including the presence\/absence of concurrent pneumonia and viral load, and also examined MDW.<\/p>\n\n\n\n

II\uff0eMethods<\/h2>\n\n\n\n

1. Study subjects<\/strong><\/p>\n\n\n\n

Cohort 1:<\/strong> The study included 235 patients \uff08severity: mild: 74 patients, moderate I: 116 patients, and moderate II: 45 patients\uff09 admitted to the National Hospital Organization Omuta Hospital and confirmed to have COVID-19 by SARS-CoV-2 PCR test between July 2020 and July 2021. In the evaluation, a comparative evaluation was performed retrospectively using blood samples collected at the initial visit. Severity was classified according to the 6.2th edition of the Clinical Practice Guidelines2\uff09<\/sup><\/strong>. The details of patient background are shown in Table 1<\/strong>.
In the diagnosis of COVID-19, nasopharyngeal swabs were collected from patients and the samples are analyzed by a fully automated genetic analyzer, Smart Gene\u00ae<\/sup> \uff08Mizuho Medy, Co., Ltd.\uff09.
Pneumonia was diagnosed based on chest CT findings by 2 radiologists and 1 pulmonologist. The presence\/absence of pneumonia was evaluated based on the chest CT image at the initial visit to the hospital.
Cohort 2: The 88 patients with COVID-19 confirmed by SARS-CoV-2 PCR test who visited the National Hospital Organization Omuta National Hospital between April and September 2021 when the automatic blood cell counter UniCel DxH 900 series Coulter Cellular Analysis System \uff08Beckman Coulter, hereafter referred to as DxH 900\uff09 was introduced, included 80 patients \uff08severity: mild in 18 patients, moderate I in 46 patients, and moderate II in 16 patients\uff09 whose MDW was measured on admission.
MDW in EDTA 2K-added whole blood venous sample was measured by DxH 900 \uff08Beckman Coulter, K.K.\uff09.
This study was conducted after being approved by the ethics committee of the National Hospital Organization Omuta Hospital \uff08Approval No.: 3-19\uff09.<\/p>\n\n\n\n

2. Detection method of SARS-CoV-2 with Smart Gene\u00ae<\/sup>\uff08nasopharyngeal swab samples\uff09<\/strong>
RT-PCR was performed using the previously reported method 3\uff09, 20\uff09<\/sup><\/strong>.<\/p>\n\n\n\n

3.Evaluated items at the initial hospital visit<\/strong>
At the initial visit, the following data were obtained: age, gender, smoking history, drinking history, time from onset to the initial visit, hospitalization period, BMI, underlying medical conditions, presence\/absence of pneumonia, symptoms at the initial visit, white blood cell count, neutrophil count, lymphocyte count, hemoglobin, platelet count, APTT, PT, D-dimer, albumin, total bilirubin, ALT, AST, LD, CK, BUN, creatinine, glucose, CRP, procalcitonin, ferritin, KL-6, SP-D, SP-A, HbA1c, SpO2, and Ct value \uff08number of cycles\uff09.<\/p>\n\n\n\n

4.Method to evaluate the prognostic factors of COVID-19 in Cohort 1<\/strong>
The statistical analysis to evaluate the prognostic factors of COVID-19 in Cohort 1 was based on comparing good prognosis and poor prognosis. Prognosis should essentially be evaluated based on survival\/death. However, since no deaths were observed in this study, poor prognosis was defined as patients who were hospitalized for 14 days or longer or who were transferred to other facilities due to exacerbation of symptoms after hospitalization, while good prognosis was defined as patients who were hospitalized for shorter than 14 days. A multivariate analysis of prognostic factors was performed, considering the results of univariate analysis and previously reported prognostic factors.<\/p>\n\n\n\n

5.Relationship between the number of cycle threshold \uff08Ct value\uff09 for positive samples and severity<\/strong>
The relationship between Ct value and severity was evaluated in 204 patients \uff08severity: mild: 64 patients, moderate I: 99 patients, and moderate II: 41 patients\uff09 whose Ct value was measured among 235 patients admitted to the National Hospital Organization Omuta Hospital and confirmed to have COVID-19 by SARS-CoV-2 PCR test between July 2020 and July 2021.<\/p>\n\n\n\n

6.Evaluation of MDW to predict prognosis of COVID-19 in Cohort 2<\/strong>
MDW analysis was started from April 2021 when DxH 900 was installed. In Cohort 2, MDW in 80 patients was evaluated to predict prognosis of COVID-19 in reference to the results of the multivariate analysis in Cohort 1.<\/p>\n\n\n\n

7.Statistical analysis<\/strong>
Since the data did not show a normal distribution, the data were presented in medians and quartiles, and the analysis was performed in a nonparametric manner. The Mann-Whitney U and \u03c72<\/sup> tests were performed as the test between 2 groups, and the Kruskal-Wallis test was performed as the test between 3 groups. Considering the results of univariate analysis and previously reported prognostic factors, multivariate analysis was performed using the method of binomial logistic regression analysis to evaluate the good and poor prognosis groups. Factors associated with MDW were analyzed by binomial logistic regression analysis. p<0.05 was regarded as statistically significant. All the statistical analyses were performed using Bellcurve for Excel \uff08version 3.21, Social Survey Research Information Co., Ltd.\uff09.<\/p>\n\n\n\n

III\uff0eResults<\/h2>\n\n\n\n

Cohort 1<\/strong><\/p>\n\n\n\n

1.The background of patients with COVID-19<\/strong>
The background of patients with COVID-19 \uff08Cohort 1\uff09 is shown in Table 1<\/strong>. The median age was 56 years of age \uff0842 years in the good prognosis group and 65 years in the poor prognosis group\uff09, and age was significantly higher in the poor prognosis group \uff08p<0.001\uff09. In addition to this, BMI \uff08p=0.02\uff09, frequency of concurrent pneumonia \uff08p<0.001\uff09, and severity on admission were also significantly higher \uff08p<0.001\uff09.
Treatment was performed significantly more frequently in the poor prognosis group \uff08p<0.001\uff09, partly because patients with severe disease were treated more actively \uff08p<0.001\uff09\uff08Table 1<\/strong>\uff09. Regarding underlying disease, diabetes mellitus \uff08p<0.01\uff09 and hypertension \uff08p<0.01\uff09 were statistically higher in the poor prognosis group \uff08Table 1<\/strong>\uff09.<\/p>\n\n\n\n

2.Symptoms of patients with COVID-19<\/strong>
Pharyngitis, headache, taste disturbance, and olfactory disturbance were statistically different in the good prognosis group, while there were no significant differences in fever between the 2 groups \uff08Table 1<\/strong>\uff09.<\/p>\n\n\n\n

3.Laboratory findings in patients with COVID-19 \uff08Cohort 1\uff09<\/strong>
Laboratory findings at the initial visit in the poor prognosis group showed significant increases in neutrophil count \uff08p=0.03\uff09, D-dimer \uff08p<0.01\uff09, total bilirubin \uff08p<0.01\uff09, ALT \uff08p<0.01\uff09, AST \uff08p<0.01\uff09, LD \uff08p<0.01\uff09, CK \uff08p<0.01\uff09, BUN \uff08p<0.01\uff09, creatinine \uff08p<0.01\uff09, glucose \uff08p<0.01\uff09, CRP \uff08p<0.01\uff09, procalcitonin \uff08p<0.01\uff09, ferritin \uff08p<0.01\uff09, KL-6 \uff08p<0.01\uff09, SP-A \uff08p=0.03\uff09, and HbA1c \uff08p<0.01\uff09, and significant decreases in lymphocyte count \uff08p<0.01\uff09, albumin \uff08p<0.01\uff09 and SpO2\uff08p<0.01\uff09. The Ct value did not show statistical difference between the good and poor prognosis groups. Furthermore, the relationship between the Ct value and severity was evaluated. The Ct showed higher values as the severity increased \uff08Table 2<\/strong>\uff09. In particular, Ct values were not statistically different between patients with moderate I and moderate II, however were statistically higher in patients with moderate I and moderate II than in those with mild in severity \uff08Fig.1<\/strong>\uff09.<\/p>\n\n\n\n

4.Factors related to poor prognosis in patients with COVID-19<\/strong>
Multivariate analysis of prognostic factors considering the results of univariate analysis and previously reported prognostic factors showed that age \uff08p<0.01\uff09, BMI \uff08p=0.01\uff09, diabetes mellitus \uff08p=0.04\uff09, and concurrent pneumonia \uff08p=0.02\uff09 were independent prognostic factors \uff08Table 3<\/strong>\uff09.<\/p>\n\n\n\n

Cohort 2
Background and prognostic factors of patients with COVID-19<\/strong>
Since age, BMI, diabetes mellitus, and concurrent pneumonia were found to be independent prognostic factors of COVID-19 in Cohort 1, we also evaluated MDW as a prognostic factor in addition to age, BMI, diabetes mellitus, and concurrent pneumonia. The results showed significantly higher values for age and MDW \uff08Table 4, Fig. 2<\/strong>\uff09.<\/p>\n\n\n\n

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IV\uff0eDiscussion<\/h2>\n\n\n\n

Prognostic factors and markers of aggravation of COVID-19 have been investigated in many studies 24\uff09- 34\uff09<\/sup><\/strong>. However, there have been no reports on studies of the presence\/absence of pneumonia based on chest CT images or studies including viral load at the initial visit to the hospital. There are not many studies of MDW related to COVID-19.
Prognosis should essentially be evaluated based on survival\/death. However, since no deaths were observed in this study, poor prognosis was defined as patients who were hospitalized for 14 days or longer or who were transferred to other facilities due to exacerbation of symptoms after hospitalization, while good prognosis was defined as patients who were hospitalized for shorter than 14 days. As a result of multivariate analysis, age, BMI, diabetes mellitus, and concurrent pneumonia are found as independent prognostic factors in our study.
Advanced age was consistently found to be a prognostic factor in most of the previously reported studies in COVID-19 24\uff09, 28\uff09, 30\uff09, 33\uff09<\/sup><\/strong>. High BMI 25\uff09, 28\uff09<\/sup><\/strong> and diabetes mellitus 24\uff09<\/sup><\/strong> are also often reported to be poor prognostic factors.
Malik P et al. investigated biomarkers of COVID-19 aggravation and reported that lymphopenia, thrombocytopenia, elevated D-dimer, elevated CRP, elevated procalcitonin, elevated AST, elevated ALT, elevated creatinine, and elevated LD were significantly associated with mechanical ventilation and death 34\uff09<\/sup><\/strong>. This report is almost consistent with the results of univariate analysis in our study. Therefore, it was considered that the definitions of the poor and the good prognosis groups might be reasonable in view of the analysis of prognostic factors.
The severity of concurrent pneumonia is assessed as moderate or higher according to the 6.2th version of the Clinical Practice Guidelines 2\uff09<\/sup><\/strong>, however it is unknown whether concurrent pneumonia on admission is related to subsequent aggravation. In some cases, pneumonia will be observed after hospitalization and become severe. COVID-19 pneumonia is considered to appear a few days after the onset of COVID-19. However, since the median time from the onset of COVID-19 infection to hospitalization in the patients investigated in this study was 5 days, the patients in whom COVID-19 infection became severe may have had pneumonia at the time of hospital admission.
According to a systematic review by Shah VP. et al., hospitalized patients with lower Ct value \uff08i.e., higher levels of viral RNA\uff09 have higher disease severity and mortality, however it is also noted that the results of this analysis should be interpreted with caution, given the limitations and lack of assay standardization 35\uff09<\/sup><\/strong>.
Our results showed that the viral load \uff08Ct value\uff09 on admission was not associated with prognosis, and was statistically lower in patients with higher severity. It has already been reported that the viral load is the highest immediately after onset, and that there becomes no isolation of the virus from the 7th day after onset 36\uff09<\/sup><\/strong>. The results of the Ct value in this study may suggest that the immune response plays a stronger role in the aggravation of COVID-19 than the virus itself, and that COVID-19 pneumonia is caused by the immune response. The Ct value does not only provide useful information for infection control since the viral concentration in specimens and the estimation of time of onset, but also might be used actively for the selection of antiviral and anti-inflammatory drugs.
Although many studies have been conducted on markers of aggravation, it is desirable to predict the prognosis of COVID-19 easily within the scope of routine clinical examinations.
MDW generated by DxH 900 which was evaluated in this study, is a new cytometric parameter that reflects monocyte changes in cell volume caused by the activation of monocytes. The result is obtained in around 1 minute with routine CBC and WBC differential testing without the need for ordering additional tests. Therefore, MDW values can be easily confirmed as a routine clinical examination. MDW may be considered a promising screening test parameter for COVID-19, if it\u2019s prognostic prediction for COVID-19 is superior.
Originally, monocytes play an important role in the innate immune system against infection, and are believed to be involved in phagocytosis, antigen presentation, cytokine production, and activation of acquired immune system. Additionally, activation of monocytes is considered to result in the expression of various functions and the diversity of morphology 37\uff09-42\uff09<\/sup><\/strong>. Similarly, neutrophil volume and distribution width are changed, however, Crouser et al.43\uff09-46\uff09<\/sup><\/strong> evaluated MDW in patients in the emergency department and reported that MDW was superior in detecting sepsis patients and effective as the initial biomarker to aid in diagnosis and severity assessment of infection.
It is also reported that MDW may be effective as a diagnostic aid and a severity assessment biomarker for COVID-19. Ognibene et al.47\uff09<\/sup><\/strong> reported an observational study analyzing 147 patients suspected of having COVID-19 who visited the emergency room. The mean MDW was 27.3\u00b14.9 in the SARS-CoV-2 -positive patients \uff08n=41\uff09 and 20.3\u00b13.3 \uff08p<0.005\uff09 in the SARS-CoV-2 -negative patients \uff08n=106\uff09. In addition, when the MDW of 23 patients admitted to the ICU and 18 patients not admitted to the ICU were compared among SARS-CoV-2 positive patients, the mean values were 28.8\u00b15.3 and 25.4\u00b13.6 \uff08P<0.05\uff09, respectively, and was significantly higher in the patients admitted to the ICU, suggesting that MDW is useful for the diagnosis and severity evaluation of COVID-19. However, it was not evaluated as a biomarker of aggravation in that study. Investigations as biomarkers of severe disease have been reported by Riva G, et al.48\uff09<\/sup><\/strong> and Frugoli A, et al.49\uff09<\/sup><\/strong>
Riva G, et al. studied 71 good prognosis cases \uff0881.6%\uff09 and 16 poor prognosis cases \uff08deaths\uff09 \uff0818.4%\uff09 and reported that the last MDW value detected during the follow-up after diagnosis in each patient \uff08n=87\uff09 was significantly higher in the poor prognosis group with a median value of 26.1 than in the good prognosis group. Frugeli et al. also studied 284 good prognosis cases \uff0885.54%\uff09 and 47 poor prognosis cases \uff08deaths\uff09 \uff0814.16%\uff09 and reported that the median value at the first visit in the poor prognosis group was 24.9, significantly higher than in the good prognosis group. In our study, MDW was evaluated as a biomarker of aggravation, and MDW was significantly higher in the poor prognosis group. Riva G, et al. showed higher values than our results because they looked at the last value of MDW detected during the follow-up after diagnosis, but were in close agreement with the results of Frugeli et al.
Frugoli A, et al. also showed the usefulness of MDW as a predictive marker of sepsis in COVID-19 patients. In the present study, PCT was significantly higher in the poor prognosis group in cohort 1, suggesting that poor prognosis cases may have concurrent sepsis. This point may need further investigation with a larger number of cases.
These results suggest that MDW may be used as a biomarker of aggravation at the time of hospital admission.
This study has some limitations. First, prognosis should essentially be evaluated based on survival\/death. However, since no deaths were observed in this study, poor prognosis was defined as patients who were hospitalized for 14 days or longer or who were transferred to other facilities due to exacerbation of symptoms after hospitalization, while good prognosis was defined as patients who were hospitalized for shorter than 14 days. Second, in this study, the prognostic factors were analyzed from July 2020, however since DxH 900 was installed in April 2021, the number of patients in whom MDW was analyzed was small and it was hard to examine in the same cohort. Third, the study was retrospective in design, used data obtained from a single center study. Finally, it did not include severe patients.<\/p>\n\n\n\n

V. Conclusions<\/h2>\n\n\n\n

Viral load \uff08Ct value\uff09 at the initial visit to the hospital may be not related to prognosis. However, it does not only provide useful information for infection control since viral concentration in the specimens and the time of onset may be estimated, but also might be used actively for the selection of antiviral and anti-inflammatory drugs.
MDW is a test that can be performed quickly and easily in peripheral blood testing, which is collected in a routine clinical examination and it is not necessary to use nasopharyngeal swab samples, etc. MDW is expected to be widely used clinically as a supplementary test to predict the prognosis of COVID-19 in various medical institutions such as small and medium-sized hospitals and clinics.<\/p>\n\n\n\n

Declarations<\/h3>\n\n\n\n

Ethics approval and consent to participate
This study was conducted after being approved by the ethics committee of National Hospital Organization Omuta National Hospital \uff08Approval No.: 3-19\uff09.<\/p>\n\n\n\n

Competing interests<\/h3>\n\n\n\n

The authors declare that they have no conflicts of interest and no competing interests.<\/p>\n\n\n\n

Funding<\/h3>\n\n\n\n

No funding was received for conducting this study.<\/p>\n\n\n\n

Authors\u2019 contributions<\/h3>\n\n\n\n

K. W. assisted with study design and interpretation of the data, had full access to the study data, assumes responsibility for the integrity of the data and the accuracy of the analysis, and drafted the manuscript. Z. N., K. K., M. Y., S. K., A. K., J. O., S. M., N. N., M. K., S.A. and H. Y. assisted with study design and interpretation of the data and edited the initial draft of the manuscript. I. F. and R. K. contributed to data collection and management. H. K. contributed to the categorization of patients with COVID-19.<\/p>\n\n\n\n

Acknowledgements<\/h3>\n\n\n\n

None.<\/p>\n\n\n\n

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