ORIGINAL ARTICLE


https://doi.org/10.5005/jp-journals-11010-1080
Indian Journal of Respiratory Care
Volume 12 | Issue 4 | Year 2023

Hematologic Parameters and Their Limiting Values as Prognostic Factors in COVID-19


Bhargavi K Nagabhushan1https://orcid.org/0000-0002-1005-0904, Puneet Nagendra2https://orcid.org/0000-0002-9887-3699, HS Sandeepa3https://orcid.org/0000-0002-6424-4915, Supriya Sandeepa4https://orcid.org/0000-0002-9548-3492

1,4Department of Pathology, Dr Chandramma Dayananda Sagar Institute of Medical Education and Research (CDSIMER), Dayananda Sagar University, Ramanagara, Karnataka, India

2Department of Respiratory Medicine, Dr Chandramma Dayananda Sagar Institute of Medical Education and Research (CDSIMER), Dayananda Sagar University, Ramanagara, Karnataka, India

3Department of Pulmonary Medicine, Adichunchanagiri Institute of Medical Sciences (AIMS), BG Nagara, Karnataka, India

Corresponding Author: Supriya Sandeepa, Department of Pathology, Dr Chandramma Dayananda Sagar Institute of Medical Education and Research (CDSIMER), Dayananda Sagar University, Ramanagara, Karnataka, India, Phone: +91 6360738481, e-mail: bonjkn91@gmail.com

Received: 05 January 2023; Accepted: 04 November 2023; Published on: 18 January 2024

ABSTRACT

Introduction: The manifestations of coronavirus disease 2019 (COVID-19) are varied and range from asymptomatic to life-threatening cases requiring intensive care. Currently, an expeditious prediction of disease severity and outcomes in the early stages remains an unmet challenge. This study aims to critically examine the hematologic values predominantly, along with sociodemographic and clinical parameters, to identify their limiting values that can prognosticate the disease.

Materials and methods: A cross-sectional retrospective study was conducted in a rural medical college hospital in Ramanagara, Karnataka. The necessary data of the inpatients were recorded from their respective case files available in the Medical Records Department (MRD).

Results: Of the 442 COVID-19 cases, 402 cases were survivors, and 40 cases were nonsurvivors. A statistical significance (p-value of <0.001) was noted between these two categories for the following parameters in our study: oxygen saturation, total count, neutrophil count and lymphocyte count, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). The limiting values that were derived had an increased risk of mortality with respect to these parameters: oxygen saturation (<86.5%), NLR (>4.5), and PLR (>270.50).

Conclusion: Identification and implementation of these prognostic markers are of substantial value to the physicians who continue to be challenged by COVID-19, more so in countries with limited healthcare resources.

How to cite this article: Nagabhushan BK, Nagendra P, Sandeepa HS et al. Hematologic Parameters and Their Limiting Values as Prognostic Factors in COVID-19. Indian J Respir Care 2023;12(4):303–307.

Source of support: Nil

Conflict of interest: None

Keywords: Coronavirus, Coronavirus disease 2019, Limiting values, Mortality, Neutrophil to lymphocyte ratio, Platelet to lymphocyte ratio

INTRODUCTION

At the end of 2019, there was an outburst of coronavirus disease 2019 (COVID-19)1 overwhelming the healthcare system.2 Death from COVID-19 predominantly results from virus-activated cytokine storms.1 The manifestations of this disease are varied and range from asymptomatic to life-threatening cases requiring intensive care. Currently, an expeditious prediction of disease severity in the early stages remains an unmet challenge.3

The treatment regimens for hospitalized patients are based on the severity of COVID-19. Of much importance is clinical prowess and inter-departmental coordination. The ability to rapidly investigate and detect patients who would benefit from early, invasive therapy at the time of hospital admission is of substantial value.3

The brisk viral spread generated numerous publications to identify the clinical and hematological characteristics of COVID-19 to better detect and stop disease progression to a critical stage.2 Introducing prognostic markers to predict disease outcomes in a timely manner serves as a valuable predictor of COVID-19 mortality.1

This study aims to critically examine the hematologic values predominantly, along with sociodemographic and a few clinical parameters, to aid in risk stratification and direct the intervention studies to target patients with high risk of mortality.2

OBJECTIVES

MATERIALS AND METHODS

Study Design and Participants

A cross-sectional retrospective study was carried out on inpatients from a rural-based medical college hospital in Ramanagara, Karnataka, India, after receiving the consent of the Ethics Committee.

Inclusion Criteria

Patients positive for COVID-19 in nasopharyngeal swab specimens, both male and female adults presenting with mild to severe categories, were considered.

Exclusion Criteria

Pregnant and pediatric patients, case files with missing hematologic and clinical data, and patients transferred to other medical facilities with unknown outcomes.

Definitions

  • According to the Indian Council of Medical Research, the mild COVID-19 category is defined as peripheral oxygen saturation (SpO2) of ≥94%, without shortness of breath or hypoxia. Moderate cases have SpO2 of 90–93% on room air, along with cough, fever, and dyspnea. Severe cases have SpO2 of <90% on room air with severe respiratory distress and pneumonia.

  • Neutrophil-to-lymphocyte ratio (NLR) formula: (absolute neutrophil count)/(absolute lymphocyte count); a normal value of <3.13.4

  • Platelet-to-lymphocyte ratio (PLR) formula: (platelet count)/(absolute lymphocyte count); a normal value of <180.22.5

  • Peripheral oxygen saturation (SpO2): Measured on room air at presentation by a fingertip pulse oximeter.

Procedure

The demographic, clinical, and hematologic data of the patients were acquired from their respective case files available at the Medical Records Department (MRD). The same was entered into an Excel sheet and analyzed.

Statistical Analysis

The data was entered into Microsoft Excel, imported, and analyzed using Statistical Package for the Social Sciences (SPSS) (version 20.0 for Windows; SPSS Inc., Armonk, New York, Unites States of America: IBM Corp). The continuous data were expressed either in mean and standard deviation or median and range, depending on their distribution, and the categorical data were expressed in proportions. Different mean values of certain parameters among the outcomes of survivors and nonsurvivors were compared using an independent t-test, and the median values were compared using the Mann–Whitney U test. The association between the categorical variables was assessed using the Chi-squared test and Fisher’s exact test. The difference in proportions of outcomes and severity as assessed by different systems were compared using Z-test. A receiver operating characteristic (ROC) curve was plotted to assess the predictive accuracy of NLR and PLR in assessing the outcome of COVID-19 and was interpreted based on the area under the curve. Youden’s index was used to assess the sensitivity and specificity. Statistical significance was considered with a p-value of <0.05.

RESULTS

The data of COVID-19-positive patients who were admitted between April 2021 and July 2021 were collected and analyzed. Out of 442 COVID-19-positive cases, 402 cases were survivors, and 40 cases were nonsurvivors.

The mean age of the study subjects among survivors was 44.8 ± 15.5 years, and nonsurvivors were 54.2 ± 14.2 years. A higher percentage of nonsurvivors had comorbidities (13.2%) and were of blood group A (21.7%). All 40 mortality cases had complications, and 39 of such cases fell into the severe COVID-19 category (Table 1).

Table 1: Distribution of the study subjects based on sociodemographic profile and some clinical parameters
Variables Survivors (n = 402) Nonsurvivors (n = 40)
n (%) n (%)
Age-group in years
 ≤30 89 (98.9) 01 (1.1)
 31–50 181 (91.0) 18 (9.0)
 51–70 101 (87.1) 15 (12.9)
 71–90 31 (83.8) 06 (16.2)
Gender
 Male 258 (91.8) 23 (8.2)
 Female 144 (89.4) 17 (10.6)
Comorbidities
 Yes 164 (86.8) 25 (13.2)
 No 238 (94.1) 15 (5.9)
Severity of COVID-19
 Mild and moderateSevere 274 (99.6) 01 (0.4)
 Severe 128 (76.6) 39 (23.4)
Complications
 Yes 246 (86.0) 40 (14.0)
 No 246 (86.0) 40 (14.0)
Blood group
 A 36 (78.3) 10 (21.7)
 B 54 (94.7) 03 (5.3)
 AB 07 (87.5) 01 (12.5)
 O 95 (89.6) 11 (10.4)

The mean oxygen saturation in the survivors was 90.5%, and in the nonsurvivors, it was 71.2% (Table 2).

Table 2: Average values of certain clinical and hematologic variables
Variables Survivors Nonsurvivors
Oxygen saturation (%) 90.5 ± 9.8 71.2 ± 14.8
Hemoglobin (gm/dL) 13.7 ± 2.6 13.7 ± 2.0
NLR¥ 3.7 (0.5–31.7) 9.0 (1.1–30.3)
PLR¥ 168.3 (19.7–872.6) 229.3 (72.7–846.5)
Total count (cells/mm3)¥ 6740.0 (1351.0–29902.0) 8495.0 (880.0–24970.0)
Neutrophil (%) 70.9 ± 14.2 84.3 ± 8.9
Lymphocyte (%)¥ 20.0 (2.0–62.0) 9.0 (3.0–46.0)
Eosinophil (%)¥ 1.0 (0.0–21.0) 1.0 (1.0–2.0)
Monocytes (%)¥ 5.0 (1.0–18.0) 4.0 (2.0–6.0)
Platelet (lakh/mm3)¥ 2.2 (0.5–7.4) 2.0 (0.6–8.1)
Prothrombin time (seconds) 13.7 ± 1.6 15.5 ± 3.9
aPTT (seconds)¥ 32.5 (0.0–46.6) 35.6 (25.0–121.0)

Mean ± SD; ¥median (range)

Of all the clinical and hematological parameters compared between the survivors and nonsurvivors, oxygen saturation, neutrophil and lymphocyte percentage, NLR, PLR, and total count showed statistical significance, as shown in Tables 3 and 4.

Table 3: Comparison of mean values of certain laboratory and clinical parameters among the survivors and nonsurvivors in the study subjects
Variables Nonsurvivors (n = 40) Survivors (n = 402) t-value (95% CI) p-value
Oxygen saturation (%) 71.2 ± 14.8 90.5 ± 9.8 −11.2 (−22.8 to −15.9) <0.001*
Hemoglobin (gm/dL) 13.7 ± 2.0 13.7 ± 2.6 −0.05 (−0.8 to −0.8) 0.96
Neutrophil (%) 84.3 ± 8.9 70.9 ± 14.2 5.8 (8.9–17.9) <0.001*

*Indicates statistical significance at p < 0.05

Table 4: Comparison of median values of certain laboratory parameters among the survivors and nonsurvivors in the study subjects
Variables Mean ranks z-value p-value
Survivors Nonsurvivors
NLR 211.1 325.6 −5.40 <0.001*
PLR 216.8 268.8 −2.5 0.01*
Total count (cells/mm3) 214.2 295.1 −3.8 <0.001*
Lymphocyte (%) 232.8 107.4 −5.9 <0.001*

*Indicates statistical significance at p < 0.05

The percentage of survivors showed a decreasing trend with age from 31 years onward, which showed a statistical significance of 0.01 (p value). A statistical significance was noted in the nonsurvivor group for the presence of complications, comorbidities, and severe COVID-19 category. The percentage of mortality was higher in the patients with blood group A/AB than in blood group B/O, which is statistically significant (Table 5).

Table 5: Association of sociodemographic factors and certain clinical parameters with the outcome of COVID-19 among the study subjects
Variables Survivors (n = 402) Nonsurvivors (n = 40) χ2-value
(p-value)
n (%) n (%)
Age-group in years
 ≤30 89 (98.9) 01 (1.1) 11.32
 31–50 181 (91.0) 18 (9.0) (0.01)*
 51–70 101 (87.1) 15 (12.9)
 71–90 31 (83.8) 06 (16.2)
Gender
 Male 258 (91.8) 23 (8.2) 0.70
 Female 144 (89.4) 17 (10.6) (0.40)
Comorbidities
 Yes 164 (86.8) 25 (13.2) 7.00
 No 238 (94.1) 15 (5.9) (0.008)*
Severity of COVID-19
 Mild and moderate 274 (99.6) 01 (0.4) (<0.001)*
 Severe 128 (76.6) 39 (23.4)
Complications
 Yes 246 (86.0) 40 (14.0) (<0.001)*
 No 156 (100.0) 00 (0.0) (<0.001)*
Blood group
 A/AB 43 (79.6) 11 (20.4) 5.52
 B/O 149 (91.4) 14 (8.6) (0.02)*

*Indicates statistically significant association; Fisher’s exact test applied

Figures 1 and 2 exhibit the predictive accuracy of NLR, PLR, total count, and oxygen saturation in predicting the outcome of COVID-19 disease using the ROC curve.

Fig. 1: Predictive accuracy of NLR, PLR, and total count in predicting the outcome of COVID-19 disease using ROC curve [NLR: area under the curve (AUC) (95% CI): 0.75 (0.68–0.82); PLR: AUC (95% CI): 0.61 (0.53–0.70); total count: AUC (95% CI): 0.68 (0.59–0.77)]

Fig. 2: Predictive accuracy of oxygen saturation in predicting the outcome of COVID-19 disease using ROC curve [AUC (95% CI): 0.87 (0.83–0.92)]

Of the parameters shown in Table 6, the highest sensitivity is seen in NLR (92%), followed by oxygen saturation (90%).

Table 6: The limiting values of NLR, PLR, and oxygen saturation in predicting the outcome of COVID-19 disease with the respective sensitivity and specificity values
Variables Limiting value Sensitivity Specificity
NLR 4.50 92.0% 56.0%
PLR 270.50 47.0% 76.0%
Oxygen saturation (%) 86.5 90.0% 75.9%

DISCUSSION

The primary target of COVID-19 is the lung, causing severe acute respiratory syndrome and subsequent multiorgan damage,6 especially in organs that have numerous angiotensin-converting enzyme-2 receptors, such as the respiratory, hepatic, cardiovascular, and renal systems.7

The mortality rate in our study was 9.04%. However, in a study by Djaharuddin et al., the rate was 17.18%.7 The highest mortality was seen between 71 and 90 years in our study, which is in concordance with the study by Pijli et al., which showed that patients over 70 years had a higher risk of infection, severity, and death compared to patients younger than 70 years.8 Due to immunosenescence, defective mucociliary clearance, and impaired mucosal barrier, geriatric patients have an increased risk of respiratory infections. A reduction in the number and activity of cilia in the upper respiratory tract decreases mucociliary function.7

Out of 442 cases, 63.5% were males and 36.5% were females in our study, which was comparable with another study showing men having a statistically significant 8% higher risk of acquiring COVID-19.8 In contrast, a study by Djaharuddin with a sample size of 454 patients, consisted of 225 (49.56%) males and 229 (50.44%) females.7 However, there was no statistical significance between the mortality rate of male and female patients in our study.

The common comorbidities encountered in our study were diabetes mellitus and hypertension. The relationship between the presence of comorbidities and mortality showed a statistical significance. These findings were akin to a study by Biswas et al., which showed a significantly increased risk of mortality (p < 0.00001) in diabetes, hypertension, renal, cerebrovascular, cardiovascular, and respiratory disease.9

Our study showed p < 0.001 in oxygen saturation between survivors and nonsurvivors, with a mean value of 90.5 and 71.2%, respectively. This is similar to a study by Bairwa et al. showing mean SpO2 of 95.98 and 89.21% in survivors and nonsurvivors, respectively.10

There were no significant changes in hemoglobin levels in COVID-19 mortality in studies conducted by Wan et al.11 and Wang et al.,12 which is similar to our study. A study by Asma Rahman et al. in 2021 demonstrated reduced hemoglobin levels in some severe COVID-19 patients. However, more exploratory studies are necessary to validate the same.13

A higher value of total count was found in the nonsurvivors in our study. This is in accordance with another study showing a higher median total count of 8,000 and 5,000 cells/mm in nonsurvivors and survivors, respectively.5 A statistical significance of lymphopenia was noted in the nonsurvivors in our study. This is in congruence with a study by Li et al. with p < 0.001.14 The favored explanation for lymphopenia includes direct viral cytotoxic effect causing apoptosis, infection of hematopoietic stem cells, lactic acidosis, infection of the spleen, and lymphoid organs. The cytokine storm negatively affects T-cell number and function. In patients requiring intensive care, cluster of differentiation (CD) 4+ and CD8+ T-lymphocytes were found to be markedly reduced.13,15

Neutrophil to lymphocyte ratio (NLR) is a prognostic biomarker that can be used to indicate systemic inflammation.16 In our study, NLR showed statistical significance in determining the mortality in patients (<0.001). This is in concordance with the study by Yan et al., which identified it as a marker for prolonged hospital stay, severe COVID-19 disease, and in-hospital mortality.15

Likewise, the p-value obtained in our study for PLR was 0.01. Chan et al. conducted a meta-analysis of five studies and found PLR to be raised in severe as compared with nonsevere COVID-19 patients. The specificity and sensitivity were 44 and 77%, which is consistent with our study.17 Yadav et al. conducted a retrospective study on 303 COVID-19 patients. There was a significantly higher mean of NLR (14.46 ± 5.84) in nonsurvivors compared to NLR (8.43 ± 4.33) in recovered patients. However, a significant mean difference in PLR was not obtained.18 The highest sensitivity for the limiting values in our study was seen in NLR (92%), followed by oxygen saturation (90%).

A higher percentage of nonsurvivors were of blood group A (21.7%) in our study. A study by Samra et al. with a sample size of 507 inferred that mechanically ventilated patients and nonsurvivors had blood group A predominantly.19

This study has elaborated that age, presence of comorbidities, low oxygen saturation, leukocytosis, elevated NLR, and PLR are crucial predictors for developing severe illness in COVID-19 patients. Given the strong prognostic value of abnormal hematologic parameters and their relative ease in monitoring, they should be followed meticulously in all patients, which can aid in providing crucial intensive care referrals for critical patients.20

CONCLUSION

The clinical manifestations of COVID-19 demonstrate substantial variations in terms of symptoms, severity, and duration of disease.21 This invariably necessitates the identification and implementation of prognostic markers to accurately predict COVID-19 outcomes.1 These hematologic parameters are simple, rapid, and inexpensive,1 which are of substantial value to physicians who continue to be challenged by this disease, more so in nations with limited healthcare resources, especially in pandemic situations.3

LIMITATIONS OF THE STUDY

It was a retrospective study, and researchers were not blinded to the outcome when they analyzed the data.

ORCID

Bhargavi K Nagabhushan https://orcid.org/0000-0002-1005-0904

Puneet Nagendra https://orcid.org/0000-0002-9887-3699

Sandeep HS https://orcid.org/0000-0002-6424-4915

Supriya Sandeepa https://orcid.org/0000-0002-9548-3492

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