Year :
2024
| Month :
May
| Volume :
18
| Issue :
5
| Page :
EC12 - EC16
Full Version
Assessing the Predictive Value of Haematological Parameters (NLR, LMR, PLR) for COVID-19 Disease Severity as Quantified by CT Severity Scores: A Prospective Cohort Study
Published: May 1, 2024 | DOI: https://doi.org/10.7860/JCDR/2024/69032.19433
Kovuri Umadevi, Lavanya Motrapu, Kasturi Dinesh, Nagarjuna Chary Rajarikam, Mohd Imran Ali
1. Senior Resident, Department of Pathology, Government Medical College, Nizamabad, Telangana, India.
2. Associate Professor, Department of Pathology, Government Medical College, Nizamabad, Telangana, India.
3. Assistant Professor, Department of Pathology, Government Medical College, Nizamabad, Telangana, India.
4. Professor and Head, Department of Pathology, Government Medical College, Nizamabad, Telangana, India.
5. Associate Professor, Department of Pathology, Government Medical College, Nizamabad, Telangana, India.
Correspondence Address :
Dr. Kovuri Umadevi,
Government General Hospital, Beside Bustand, Khaleelwadi, Nizamabad-503001, Telangana, India.
E-mail: dr.umadevik113@gmail.com
Abstract 
Introduction: In the relentless global battle against the Coronavirus Disease-2019 (COVID-19) pandemic, accurate prediction of disease severity remains a critical challenge, with profound implications for patient outcomes and healthcare resource allocation. As the virus continues to evolve and pose new threats, the need for reliable prognostic indicators becomes increasingly urgent. Effective identification of patients at high-risk of developing severe illness not only facilitates timely intervention and personalised treatment strategies but also optimises healthcare resource utilisation. In this context, the exploration of novel biomarkers and predictive models holds immense promise for enhancing ones understanding of disease progression and improving clinical decision-making.
Aim: To study the association between haematological parameters, including Neutrophil-to-Lymphocyte Ratio (NLR), Lymphocyte-to-Monocyte Ratio (LMR), and Platelet-to-Lymphocyte Ratio (PLR), with Computed Tomography Scan Severity Score (CTSS) in COVID-19 patients.
Materials and Methods: A prospective cohort study was conducted from March 2021 to July 2022 at Government General Hospital (GGH) Nizamabad, Telangana, India. The study encompassed all three COVID-19 waves, included a sample size of 159 Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) positive patients, excluding pregnant women and children under 10 years. Upon admission, CTSS and ratios of NLR, LMR, and PLR were recorded in an MS Excel sheet before any medical intervention and then analysed using Statistical Package for Social Sciences (SPSS) software 22.0.
Results: The study comprised 159 patients with a mean age of 50.86±13.89 years (ranging from 16 to 85), predominantly male 90 (56.61%). The highest infection rate 85 (53.45%) was in the 41-60 years age group. The NLR was significantly elevated from a mean value of 4.58 to 11.24 (r value=0.78, p-value=<0.001), and LMR notably reduced from 8.27 to 3.80 (r value=0.67, p-value=0.003) in correlation with the severity as indicated by CTSS. Although PLR values were higher in severe cases, increasing from 173.07 in mild cases to 272.29 in severe cases, there was no significant correlation with CTSS (r-value=-0.78, p-value=0.177).
Conclusion: CTSS emerges as a valuable radiological biomarker for predicting COVID-19 severity. However, due to its cost and limited availability in grassroots-level hospitals, there is a need for alternative severity prediction models. Present study proposes a predictive model using NLR and LMR biomarkers as alternatives to CTSS for assessing COVID-19 severity.
Keywords
Coronavirus disease-2019, Lymphocyte-to-monocyte ratio, Neutrophil-to-lymphocyte ratio, Platelet-to-lymphocyte ratio
Introduction
The world has been grappling with the COVID-19 pandemic for over two and a half years, a crisis that has now transitioned into an endemic stage. Since the virus’s emergence in December 2019, extensive research has been conducted to understand its structure, mechanism, family, origin, and variants (1),(2),(3),(4). Vaccination against COVID-19 has significantly altered the virus’s impact, but the longevity of vaccine protection is still under investigation (5). Despite a global stabilisation in the virus’s impact, the emergence of vaccine-resistant variants continues to be a concern (6). The World Health Organisation (WHO) emphasises testing and isolation as key strategies to curb the virus’s transmission (7). Testing is crucial not only for identifying COVID-19 but also for assessing the severity of the infection. Diagnostic methods like Polymerase Chain Reaction (PCR) and Rapid Antigen Tests (RAT) are pivotal for detecting SARS-CoV-2, but their scope is limited to disease identification and they are plagued by issues like false negatives and the need for advanced laboratories and skilled technicians (7),(8). Computed Tomography (CT) scans provide a direct assessment of viral impact on the lungs and alveolar damage. The CTSS is a valuable diagnostic tool in this regard, using a 25-point scale to categorise disease severity: <7 as mild, 7-18 as moderate, and >18 as severe (9),(10). CT scans are integral for initial lung assessments, monitoring virus replication, complication prediction, treatment planning, and post-diagnosis follow-up (11),(12). While CT imaging is quick, safe, and painless, it faces practical limitations such as limited availability in primary health centres, the need for specialised equipment and personnel, and a radiation dose of approximately 8mSv per full chest scan (13),(14). Given the limitations of CT scans and the unpredictable nature of COVID-19 complications, there is a growing need for a cost-effective, easy-to-use diagnostic tool for daily patient monitoring (15). Short-term prognostic markers have emerged as essential tools in tracking disease progression (16). Biomarkers from peripheral blood specimens have shown significant potential in COVID-19 diagnosis. COVID-19 is a multisystem syndrome involving complex immunological, inflammatory, and coagulative responses (17),(18). Biomarkers, categorised into haematological, inflammatory, coagulation, cardiac, hepatic, muscle, renal, and electrolytic types (19), play a crucial role in understanding the disease’s impact on the body (20). This study, conducted in Nizamabad, India, brings a fresh perspective by evaluating haematological markers (NLR, LMR, PLR) as predictors for COVID-19 severity, offering an innovative alternative to CTSS. Focusing on accessible blood biomarkers, it addresses the need for practical severity assessment tools in resource-limited settings. The research enriches the global understanding of COVID-19 haematological effects from a unique regional standpoint, advancing the field by providing valuable insights into alternative diagnostic strategies.
Material and Methods
The study was a prospective cohort study analysis conducted at Government General Hospital, Nizamabad, Telangana, India from March 2021 to July 2022, including 159 patients. Approval for the study was granted by the Institutional Ethical Committee of Government Medical College Nizamabad, Telangana State, India, with the registration number ECR/144/INST/TG/2019.
Inclusion criteria: COVID-19 patients confirmed positive by RT-PCR testing were included in the study.
Exclusion criteria: Pregnant women and individuals below the age of 10 years were excluded from the study.
Data collection: Patient data collected included demographic information (age, gender), clinical symptoms (fever, cough, myalgia, headache, eye burning sensation), medical history (diabetes, hypertension), and personal, travel, and contact history. Haematological parameters were measured using Sysmex xn-1000, and peripheral smears were examined on admission day before any treatment intervention. Data were recorded in a Microsoft Excel sheet for analysis. Ratios for NLR, LMR, and PLR were calculated based on their absolute counts. CTSS was assessed in accordance with WHO criteria using a Siemens 16-slice CT scanner (21).
CTSS, as per WHO guidelines, was categorised into three groups: mild (CTSS 1-<7/25), moderate (CTSS 7-18/25), and severe (CTSS >18-25/25) as shown in (Table/Fig 1). In this study, the extent of lung involvement was quantified by assigning 5 points to each affected lobe, with a total of 5 lobes in the lungs, culminating in a maximum score of 25 points (21).
The NLR normal reference range is 1-3, LMR normal reference is 2-6, and PLR normal reference is 100-200 (3). These ratios were then categorised according to the WHO chest CTSS Score.
Statistical Analysis
Statistical analysis was performed using SPSS software version 22.0. Categorical and continuous variables were expressed as percentages and mean±standard deviation, respectively. The analysis included age-wise and gender-wise comparisons. Descriptive statistical analyses were conducted, including the calculation of the mean, standard error of mean, standard deviation, and the minimum and maximum values, as well as the lower and upper bounds for NLR, LMR, and PLR, utilising SPSS software. The mean differences in NLR, LMR, and PLR across the mild, moderate, and severe categories of CTSS were examined using the one-way ANOVA, and correlation was assessed using Spearman rank analysis A p-value of <0.05 is deemed statistically significant. Subsequently a Games-Howell post-hoc test was conducted to compare mean differences between three different groups (grades) in the study. In this context, CTSS Mild is considered as Grade 1, Moderate as Grade 2, and Severe as Grade 3.
Results
COVID-19 patients with varying degrees of COVID-19 severity, as classified by their CTSS, were analysed and are presented in (Table/Fig 2). The cohort comprised 90 (56.61%) males and 69 (43.39%) females, with the highest infection rate observed in the 41-60 years age group, accounting for 86 (54.1%). Regarding the CTSS categories, the majority of patients, 98 (61.64%), fell into the moderate category. The mild and severe categories included 27 (16.98%) and 34 (21.38%) of the patients, respectively.
(Table/Fig 3) presents a comparison of the mean values of NLR, LMR, and PLR across different CTSS categories: mild, moderate, and severe. In the mild category, the mean NLR value is 4.58±2.17, while it is 7.01±4.66 in the moderate category and 11.24±8.29 in the severe category. This increasing trend in NLR with the escalating severity of the disease was statistically significant, as indicated by a p-value of 0.000, determined through ANOVA testing, and an r-value of 0.78. (Table/Fig 4)a illustrates this rising trend of NLR corresponding to an increase in disease severity. Similarly, the mean LMR value in the mild category is 8.27±6.85, but it decreases to 6.19±5.43 in the moderate category and further to 3.80±3.10 in the severe category with a p-value of 0.003 and an r-value of 0.67 (Table/Fig 4)b visually represents this decreasing trend of LMR with the increasing severity of the disease.
In the mild category, the study observed a mean Platelet-Lymphocyte Ratio (PLR) value of 173.07±128.25, which increased to 245.25±215.11 in the moderate category and further to 272.29±256.42 in the severe category. Although there is an upward trend in PLR values in more severe cases, this increase did not show a significant correlation with the severity of the disease, evidenced by a p-value of 0.177 and an r-value of -0.78. (Table/Fig 4)c displays the trend line for PLR values across mild, moderate, and severe categories.
A Games-Howell post-hoc test was conducted to compare mean differences between three different groups (grades) in the study. (Table/Fig 5) provides mean differences between each pair of groups, along with their standard errors, significance levels (Sig.), and 95% Confidence Intervals (CI). For NLR, the mean difference between grade 1 and grade 2 is 2.39599, with a standard error of 0.90434.
Similarly, the mean difference between Grade-1 and Grade-3 was 6.24062, with a standard error of 1.27534. This difference was also statistically significant (p-value <0.001). For LMR, the mean difference between Grade-1 and Grade-2 is 2.41559, with a standard error of 0.96579. This difference was statistically significant at the 0.05 level (p-value=0.040). Similarly, the mean difference between Grade-1 and Grade-3 was 4.80183, with a standard error of 1.11752. This difference was also statistically significant p-value <0.001.
Discussion
This study involved 159 COVID-19 patients from the first, second, and third waves in India. The Government General Hospital, Nizamabad, saw a rapid surge in cases and hospitalisations, posing a challenge in predicting disease severity. CTSS was used as a benchmark for comparison. Managing severely affected patients was particularly challenging due to the common risk of multiple organ failures noted throughout the disease course. During the second wave, existing treatment protocols and patient risk assessment models proved inadequate.
The average age of COVID-19 positive patients in this study was 50.86±13.89 years, ranging from 16 to 85 years. This finding was compared with other researchers as shown in (Table/Fig 6) (22),(23),(24).
In present study cohort, 110 (69.18%) of the patients presented with symptoms like fever, cough, and sore throat, while 30.82% experienced other symptoms such as eye burning sensation, diarrhoea, anosmia, shortness of breath, etc. Among those admitted, 50 out of 159 patients had co-morbidities, with hypertension present in 42% of these patients, diabetes in 32%, and other conditions in 26%. (Table/Fig 7) compares our study’s findings with those of Hashem MK et al., who categorised patients into non-severe and severe based on WHO interim guidance and Chinese COVID-19 treatment guidelines (24).
Comparing present findings with those of other researchers, Prakash Rao VV reported mean NLR values of 5.6 for mild and 9.2 for moderate disease upon admission (25). Kurri N et al., observed NLR values of 7.9 in survivors versus 11.8 in non-survivors (26). Toori KU et al., noted a progressive increase in NLR from 1.92 in asymptomatic patients to 9.9 in severe cases (27). Present study aligns with these findings, indicating a significant rise in NLR with increased disease severity. The uniqueness of present study lies in providing NLR mean values across mild, moderate, and severe categories in comparison with CTSS, a facet scarcely explored in existing research. Treatment largely depended on independent predictors, including clinical and CT topographic biomarkers. An ideal severity prediction model would optimally use hospital resources. Several researchers have developed prediction models, considering CTSS as a standard radiographic scoring, associating it with individual haematological and inflammatory biomarkers. However, these models mainly focused on mild cases and were unreliable for moderate and severe cases, where clinical progression often extends beyond lung involvement to affect the heart, kidneys, and other organs. Therefore, robust alternative disease prediction models are needed to analyse virus severity and identify patients at high-risk of multiple organ failure or mortality. Present study focuses on haematological biomarkers such as NLR, LMR, and PLR ratios, derived from complete blood counts, a basic screening procedure in any diagnostic protocol. Authors have associated these ratios with CTSS, proposing a novel approach that provides effective calibration in predicting disease severity in COVID-19 patients. While most Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections are mild to moderate, a small number progress to severe pneumonia, pulmonary oedema, Acute Respiratory Distress Syndrome (ARDS), or multiple organ failure, necessitating Intensive Care Unit (ICU) admission and resulting in high mortality (28). The immune response to SARS-CoV-2 infection is crucial, as inadequate adaptive responses and uncontrolled inflammatory innate responses can cause tissue damage. Acute COVID-19 is often the result of tissue-directed immunopathology, especially in the lungs, rather than the virus itself (29),(30). Excessive inflammation, driven by a dysregulated immune response, contributes significantly to coronavirus-mediated lung damage and systemic pathology. Neutrophils (NEU) play a critical role in immune system activation and migration, producing reactive oxygen species that can cause cellular DNA damage and free the virus, leading to antibody-dependent cell-mediated cytotoxicity (31).
NEU also interact with various cell populations, producing cytokines and effector molecules like Vascular Endothelial Growth Factor (VEGF), which stimulate tumour angiogenesis, growth, and metastasis (32),(33). Elevated levels of VEGF-A and VEGF-C expression have been reported in COVID-19 patients (33). Lymphopenia, characterised by a decrease in lymphocyte counts, particularly in CD4+ T cells and B cells, is a common feature in COVID-19 patients, indicating abnormal immune function (34),(35),(36). André S et al., noted that CD4 and CD8 T cells in COVID-19 patients are prone to apoptosis (37). Microbial infection induces neutrophil recruitment (38), and impaired lymphocytes in COVID-19 patients can lead to microbial infection, promoting NEU activation and recruitment (39). Some critically ill patients develop bacterial superinfections, exacerbating the disease. COVID-19 infections typically start with droplet inhalation and upper respiratory airway infection. The virus initially targets Angiotensin Converting Enzyme 2 (ACE2) expressing nasopharyngeal epithelium, with local tissue macrophages responding to infected cells via cytokine responses (40). Monocytes, dendritic cells, and tissue macrophages can bind the virus via lectin-like receptors such as CD169 and transport it to regional lymph nodes (41). Thrombocytopenia in COVID-19 patients has been linked to lung damage, with lung tissue and pulmonary endothelial cell injuries causing platelet activation, aggregation, and thrombus formation (42),(43),(44). Huang C et al., suggested that patients with COVID-19 exhibit high levels of cytokines like Interleukin (IL-1), Interferon (IFN) and others, contributing to Th1 activation (45). However, severe cases show higher concentrations of cytokines like G-CSF, IP-10, MCP-1, MIP-1, and TNF-alpha, indicating that a cytokine storm is associated with disease severity. Infections with 2019-nCoV cause cytokine storms, exacerbating the inflammatory response and stimulating platelet release, indicating a poor prognosis. The objective of this study was to develop a novel predictive model correlating NLR, LMR, and CTSS for assessing the severity of COVID-19 in patients. By examining how these haematological biomarkers change across different CTSS categories, the study intends to develop a comprehensive predictive model that can accurately predict disease severity upon admission. Further research is suggested to explore the relationship between PLR and CTSS, potentially leading to a triple haematological biomarker combination for predicting disease severity in future waves of COVID-19 or similar emerging epidemics. Understanding risk factors for mortality and predicting severe COVID-19 cases upon admission are emphasised as crucial for patient isolation and early preventive measures.
Limitation(s)
The study relies on data from a single centre, potentially limiting generalisability, as well as the possibility of selection bias and a limited sample size, which could compromise the representativeness and statistical power of the findings.
Conclusion
This study highlights the potential of haematological biomarkers, particularly NLR and LMR, as accessible tools for predicting the severity of COVID-19. Authors found significant correlations between changes in NLR and LMR with disease severity, as measured by CTSS, suggesting their usefulness in risk stratification, especially in resource-limited settings. While PLR did not show a significant correlation in present study analysis, further investigation is warranted. Present study findings emphasise the importance of ongoing research to refine predictive models incorporating these biomarkers, aiding clinicians in early identification and management of severe COVID-19 cases, ultimately improving patient outcomes and guiding public health interventions.
Acknowledgement
Dr. Kovuri Umadevi extends special thanks to Dr. Dola Sundeep, a research scholar from IIITDM Kurnool, for his contributions in drafting the manuscript.
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DOI: 10.7860/JCDR/2024/69032.19433
Date of Submission: Dec 11, 2023
Date of Peer Review: Feb 02, 2024
Date of Acceptance: Mar 28, 2024
Date of Publishing: May 01, 2024
AUTHOR DECLARATION:
• Financial or Other Competing Interests: None
• Was Ethics Committee Approval obtained for this study? Yes
• Was informed consent obtained from the subjects involved in the study? Yes
• For any images presented appropriate consent has been obtained from the subjects. NA
PLAGIARISM CHECKING METHODS:
• Plagiarism X-checker: Dec 12, 2023
• Manual Googling: Mar 22, 2024
• iThenticate Software: Mar 25, 2024 (10%)
ETYMOLOGY: Author Origin
EMENDATIONS: 6
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