{"id":54382,"date":"2023-12-31T10:36:06","date_gmt":"2023-12-31T10:36:06","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=54382"},"modified":"2024-01-05T06:56:36","modified_gmt":"2024-01-05T06:56:36","slug":"association-between-lymphocyte-to-monocyte-ratio-and-survival-in-covid-19-infected-patients","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol16no4\/association-between-lymphocyte-to-monocyte-ratio-and-survival-in-covid-19-infected-patients\/","title":{"rendered":"Association between Lymphocyte-to-Monocyte Ratio and Survival in COVID-19 Infected Patients"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

The coronavirus disease 2019 (COVID-19) has caused a pandemic and\nextraordinary health impacts. Most patients show mild clinical symptoms, but\nsome can experience severe clinical symptoms with severe acute respiratory\ndistress syndrome.1,2<\/sup>. Death often occurs in patients with\nco-morbidities or the elderly3<\/sup>.<\/p>\n\n\n\n

Because the symptoms of COVID-19 can develop into serious complications,\nit is crucial to identify patients who may experience clinical deterioration at\nan early stage. Routine examinations such as a complete blood count (CBC) can\nbe a quick and easy examination to predict prognosis4,5<\/sup>. Studies\nshow that the worsening of clinical symptoms in COVID-19 is closely related to\nimmune system dysregulation6,7<\/sup>. Inflammatory biomarkers originating\nfrom the peripheral blood, such as neutrophils, lymphocytes, and platelets,\nhave been studied to predict prognosis in ARDS\npatients8<\/sup>.<\/p>\n\n\n\n

The severity of COVID-19 is related to the proportion of circulating\nimmune cells9<\/sup>. Clinical symptoms in COVID-19 are influenced more by\nthe exaggerated inflammatory response than by the direct effects of viral\nreplication. Lymphopenia is the most common hematological change in patients\nwith COVID-1910<\/sup>. In patients with severe clinical symptoms, a\ndecrease in lymphocyte count may be accompanied by an increase in monocyte\ncount11<\/sup>. Lymphocyte-to-monocyte ratio (LMR) can be used as a clinical\nbiomarker to monitor disease progression in COVID-19 infection.<\/p>\n\n\n\n

Few studies have reported the role of the LMR as a prognostic marker in COVID-19.\nThis study aims to determine the association between LMR and survival so that\nLMR can be used to predict prognosis in COVID-19 patients.<\/p>\n\n\n\n

Methods<\/strong><\/p>\n\n\n\n

Study design and sample<\/strong><\/p>\n\n\n\n

This study was conducted at Udayana University Hospital from January to\nApril 2023 using a retrospective cohort design. Data collection is through a\nmedical record review of all the variables studied. The sample was patients\naged 18 years or older treated at Udayana University Hospital from June 2021 to\nJune 2022. Patients who died unrelated to COVID-19 infection, had hematological\ndiseases (such as aplastic anemia, myelodysplastic syndrome, and leukemia), and\npregnancy was excluded from this study.<\/p>\n\n\n\n

Data collection<\/strong><\/p>\n\n\n\n

SARS-CoV-2 infection was confirmed through naso-oropharyngeal swab\nexamination and analyzed by polymerase chain reaction (PCR). The\nlymphocyte-to-monocyte ratio is calculated by comparing the absolute lymphocyte\ncount and absolute monocyte count based on the results of a complete blood\ncount examination. Survival data were collected from patient medical records\nand followed up retrospectively to determine demographic, clinical, and\nlaboratory results. <\/p>\n\n\n\n

Statistical analysis<\/strong><\/p>\n\n\n\n

We used median (interquartile range\/IQR) to present numerical variables\nand frequency (percentage) to present categorical variables. We used the\nMann-Whitney U test to determine the difference in LMR values between\nnon-survivors and survivors. We used the Receiver Operating Characteristic\n(ROC) to determine the cut-off and area under the curve (AUC) of the LMR value\nin predicting death in COVID-19 patients. We performed multiple logistic\nregression analyses to determine factors that influence survival, such as age\nand underlying disease. Statistically significant if p<0.05. SPSS software\nversion 25.0 was used for all statistical analyses.<\/p>\n\n\n\n

Results<\/strong><\/p>\n\n\n\n

A total of 502 subjects were involved in this study. 71.5% of patients\nin the non-survivors group had an underlying disease. There are significant\ndifferences in LMR values in the survivors group compared to non-survivors\n(Table 1).<\/p>\n\n\n\n

Table 1: Characteristics of the subject<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

Non-survivors <\/strong><\/p>\n

(n=14)<\/strong><\/p>\n

Median (IQR)<\/strong><\/p>\n<\/td>\n

\n

Survivors<\/strong><\/p>\n

(n=488)<\/strong><\/p>\n

Median (IQR)<\/strong><\/p>\n<\/td>\n

\n

p-value<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Age, years<\/p>\n<\/td>\n

\n

61 (44 \u2013 74)<\/p>\n<\/td>\n

\n

42 (18 \u2013 84)<\/p>\n<\/td>\n

\n

<0.001<\/p>\n<\/td>\n<\/tr>\n

\n

Sex, n (%)<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

Male<\/p>\n<\/td>\n

\n

11 (78.6)<\/p>\n<\/td>\n

\n

298 (60.4)<\/p>\n<\/td>\n

\n

0.266<\/p>\n<\/td>\n<\/tr>\n

\n

Female<\/p>\n<\/td>\n

\n

3 (21.4)<\/p>\n<\/td>\n

\n

195 (39.6)<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

Underlying disease<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

Without underlying disease<\/p>\n<\/td>\n

\n

4 (28.6)<\/p>\n<\/td>\n

\n

395 (80.1)<\/p>\n<\/td>\n

\n

<0.001<\/p>\n<\/td>\n<\/tr>\n

\n

With underlying disease<\/p>\n<\/td>\n

\n

10 (71.4)<\/p>\n<\/td>\n

\n

98 (19.9)<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

Hemoglobin, gr\/dl<\/p>\n<\/td>\n

\n

13.1 (11.6 \u2013 15.9)<\/p>\n<\/td>\n

\n

13.90 (7.9 \u2013 17.4)<\/p>\n<\/td>\n

\n

0.195<\/p>\n<\/td>\n<\/tr>\n

\n

WBC, \u00d7109<\/sup> cells\/L<\/p>\n<\/td>\n

\n

9.75 (5.78 -17.25)<\/p>\n<\/td>\n

\n

6.70 (2.36 \u2013 15.98)<\/p>\n<\/td>\n

\n

0.001<\/p>\n<\/td>\n<\/tr>\n

\n

Neutrophil count<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

Absolute, \u00d7109<\/sup> cells\/L<\/p>\n<\/td>\n

\n

8.11 (4.53 \u2013 15.50)<\/p>\n<\/td>\n

\n

3.99 (1.99 \u2013 13.78)<\/p>\n<\/td>\n

\n

<0.001<\/p>\n<\/td>\n<\/tr>\n

\n

Relative, %<\/p>\n<\/td>\n

\n

84.05 (73.60 \u2013 95.20)<\/p>\n<\/td>\n

\n

60.60 (40.20 \u2013 93.50)<\/p>\n<\/td>\n

\n

<0.001<\/p>\n<\/td>\n<\/tr>\n

\n

Lymphocyte count<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

Absolute, \u00d7109<\/sup> cells\/L<\/p>\n<\/td>\n

\n

0.70 (0.31 \u2013 1.53)<\/p>\n<\/td>\n

\n

1.64 (0.31 \u2013 5.92)<\/p>\n<\/td>\n

\n

<0.001<\/p>\n<\/td>\n<\/tr>\n

\n

Relative, %<\/p>\n<\/td>\n

\n

10.20 (3.0 \u2013 14.20)<\/p>\n<\/td>\n

\n

26.40 (1.29 \u2013 53.60)<\/p>\n<\/td>\n

\n

<0.001<\/p>\n<\/td>\n<\/tr>\n

\n

Monocyte count<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n

\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

Absolute, \u00d7109<\/sup> cells\/L<\/p>\n<\/td>\n

\n

0.55 (0.05 \u2013 1.15)<\/p>\n<\/td>\n

\n

0.57 (0.09 \u2013 1.66)<\/p>\n<\/td>\n

\n

0.031<\/p>\n<\/td>\n<\/tr>\n

\n

Relative, %<\/p>\n<\/td>\n

\n

6.10 (0.70 \u2013 14.2)<\/p>\n<\/td>\n

\n

8.90 (0.32 \u2013 25.00)<\/p>\n<\/td>\n

\n

0.011<\/p>\n<\/td>\n<\/tr>\n

\n

Platelet count, \u00d7109<\/sup> cells\/L<\/p>\n<\/td>\n

\n

267 (122 \u2013 446)<\/p>\n<\/td>\n

\n

235 (54 \u2013 672)<\/p>\n<\/td>\n

\n

0.448<\/p>\n<\/td>\n<\/tr>\n

\n

LMR<\/p>\n<\/td>\n

\n

1.49 (0.37 \u2013 6.20)<\/p>\n<\/td>\n

\n

2.98 (0.44 \u2013 9.66)<\/p>\n<\/td>\n

\n

0.001<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n

Using ROC analysis, we obtained a cut-off value of LMR less than 2.0 to\npredict the occurrence of death in hospitalized COVID-19 patients (Graph 1 and\nTable 2).<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Graph 1: ROC analysis of the LMR variable.<\/strong><\/p>\n

<\/p>\n

Click here to view Graph<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 2: Cut-off values, sensitivity, specificity, and AUC of LMR<\/strong><\/p>\n\n\n\n\n\n\n
\n

 <\/strong><\/p>\n<\/td>\n

\n

Cut-off value<\/strong><\/p>\n<\/td>\n

\n

Sensitivity<\/strong> <\/strong><\/p>\n<\/td>\n

\n

Specificity<\/strong> <\/strong><\/p>\n<\/td>\n

\n

Area under curve<\/strong><\/p>\n<\/td>\n

\n

95% confidence interval<\/strong><\/p>\n<\/td>\n

\n

p-value<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

LMR<\/p>\n<\/td>\n

\n

<2.0<\/p>\n<\/td>\n

\n

76.5%<\/p>\n<\/td>\n

\n

72.7%<\/p>\n<\/td>\n

\n

0.797<\/p>\n<\/td>\n

\n

0.639 \u2013 0.956<\/p>\n<\/td>\n

\n

0.001<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

By including the variables age and presence of underlying disease in\nmultiple logistic regression analysis, we obtained an adjusted odds ratio (OR)\nof LMR of 3.62 (95% CI 1.92-14.25; p=0.046) (Table 3). <\/p>\n\n\n\n

Table 3: Crude odds ratio and adjusted odds ratio of LMR, age, and underlying disease<\/strong><\/p>\n\n\n\n\n\n\n\n\n
\n

 <\/strong><\/p>\n<\/td>\n

\n

Odds ratio<\/strong><\/p>\n<\/td>\n

\n

95% confidence interval<\/strong><\/p>\n<\/td>\n

\n

P value<\/strong><\/p>\n<\/td>\n

\n

Adjusted odds ratio<\/strong><\/p>\n<\/td>\n

\n

95% confidence interval<\/strong><\/p>\n<\/td>\n

\n

P value<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

LMR<\/p>\n<\/td>\n

\n

8.62<\/p>\n<\/td>\n

\n

2.241 -33.142<\/p>\n<\/td>\n

\n

0.002<\/p>\n<\/td>\n

\n

3.62<\/p>\n<\/td>\n

\n

1.92 \u2013 14.25<\/p>\n<\/td>\n

\n

0.046<\/p>\n<\/td>\n<\/tr>\n

\n

Age<\/p>\n<\/td>\n

\n

5.15<\/p>\n<\/td>\n

\n

1.59 \u2013 16.69<\/p>\n<\/td>\n

\n

0.006<\/p>\n<\/td>\n

\n

1.77<\/p>\n<\/td>\n

\n

1.29 \u2013 11.03<\/p>\n<\/td>\n

\n

0.038<\/p>\n<\/td>\n<\/tr>\n

\n

Underlying disease<\/p>\n<\/td>\n

\n

7.11<\/p>\n<\/td>\n

\n

2.031<\/p>\n<\/td>\n

\n

0.002<\/p>\n<\/td>\n

\n

5.17<\/p>\n<\/td>\n

\n

1.19 \u2013 22.43<\/p>\n<\/td>\n

\n

0.028<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

LMR:<\/p>\n\n\n

Discussion<\/strong><\/p>\n\n\n\n

We found a significant association between LMR and survival in\nCOVID-19. This association remained significant after the multivariate analysis\nincluded age and underlying disease variables. COVID-19 is a disease with systemic\nmultiorgan disorders caused by SARS-CoV-2. The inflammatory mediators release can\nincrease the activation of the immune system, which causes an inflammatory\nstorm and tissue damage7<\/sup>. Inflammatory biomarkers can predict\ndisease severity and assess therapy’s effectiveness. <\/p>\n\n\n\n

SARS-CoV-2 causes a\ndecrease in the number of lymphocytes in infected patients. Lymphocyte\napoptosis due to immune-mediated mechanisms or direct viral effects on\nlymphocytes is the pathogenesis leading to lymphopenia12<\/sup>. Lymphocytes\nand monocytes are essential in the inflammatory cascade of COVID-19 infection.\nStudies show that in COVID-19 infection, the lymphocyte ratio is a better\npredictor for assessing the severity of infection than the total leukocyte\ncount13<\/sup>.<\/p>\n\n\n\n

Monocytes can be an\nexcellent marker to assess the severity of infection in COVID-19 patients.\nStudies show that monocyte counts are higher in COVID-19 patients compared to\nflu patients14<\/sup>. Monocytes are essential in phagocytosis, antigen\npresentation, and inflammatory response15<\/sup>. The study by Zhou et al.\nin severe COVID-19 shows a significant increase in monocytes that produce\ninterleukin-6 in the peripheral blood, indicating that monocytes have a\nsignificant role in the occurrence of cytokine storms16<\/sup>. Lower LMR\nvalues are associated with severe clinical symptoms and the need for mechanical\nventilation in COVID-19 patients11,16<\/sup>. <\/p>\n\n\n\n

LMR can be easily\ncalculated from a complete blood count, provides fast results, is cheap, and is\navailable in all health facilities, including areas with limited resources.\nIdentification of patients at high risk at early onset of the disease is\ncritical so that appropriate and aggressive therapy can be given to prevent\ncomplications and death.<\/p>\n\n\n\n

This study is a retrospective\ncohort with a relatively large sample size. The limitation of this study is\nthat it did not record the pattern of changes in LMR values and was a\nsingle-center study.<\/p>\n\n\n\n

Conclusions<\/strong><\/p>\n\n\n\n

Lymphocyte-to-monocyte ratio is significantly lower in non-survivors. LMR can reflect the disease severity and will assist clinicians in identifying patients at risk for complications and death. More studies are needed to confirm these findings.<\/p>\n\n\n\n

Acknowledgments<\/strong><\/p>\n\n\n\n

The author would like to thank the Chairperson of the Research and Community Service Institute of Udayana University and all those who have assisted in carrying out this study.<\/p>\n\n\n\n

Conflict of Interest <\/strong><\/p>\n\n\n\n

The authors declare that there are no conflicts of interest. <\/p>\n\n\n\n

Funding Source<\/strong> <\/p>\n\n\n\n

This research was funded by Udayana University PNBP Grants, Fiscal Year 2023 (Grant number: B\/1.175\/UN14.4.A\/PT.01.03\/2023) <\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

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