Dynamic lymphocyte-CRP ratio as a predictor: a single-centre retrospective study on disease severity and progression in adult COVID-19 patients

Abstract

Objective

To assess the efficacy of dynamic changes in lymphocyte-C-reactive protein ratio (LCR) on differentiating disease severity and predicting disease progression in adult patients with Coronavirus disease 2019 (COVID-19).

Methods

This single-centre retrospective study enrolled adult COVID-19 patients categorized into moderate, severe and critical groups according to the Diagnosis and Treatment of New Coronavirus Pneumonia (ninth edition). Demographic and clinical data were collected. LCR and sequential organ failure assessment (SOFA) score were calculated. Lymphocyte count and C-reactive protein (CRP) levels were monitored on up to four occasions. Disease severity was determined concurrently with each LCR measurement.

Results

This study included 145 patients assigned to moderate (n = 105), severe (n = 33) and critical groups (n = 7). On admission, significant differences were observed among different disease severity groups including age, comorbidities, neutrophil proportion, lymphocyte count and proportion, D-Dimer, albumin, total bilirubin, direct bilirubin, indirect bilirubin, CRP and SOFA score. Dynamic changes in LCR showed significant differences across different disease severity groups at different times, which were significantly inversely correlated with disease severity of COVID-19, with correlation coefficients of −0.564, −0.548, −0.550 and −0.429 at four different times.

Conclusion

Dynamic changes in LCR can effectively differentiate disease severity and predict disease progression in adult COVID-19 patients.

Keywords

Lymphocyte-C-reactive protein ratio dynamic changes COVID-19 SARS-CoV-2 disease severity disease progression

Introduction

The World Health Organization declared that Coronavirus disease 2019 (COVID-19) no longer constitutes a Public Health Emergency of International Concern as of 5 May 2023.¹ However, its impact on human beings can be long-lasting and far-reaching, including ongoing mutations and infections of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), more transmissible or pathogenic variants, persistent threat and harm to vulnerable populations, long COVID-19 syndrome, and possible recurring pandemics in the near future.^2,3 Although ongoing mutations of SARS-CoV-2 reduce its pathogenicity and virulence, it remains fatal for certain vulnerable populations, such as the elderly or COVID-19 patients with more severe underlying conditions, even after vaccination.^4–6 In the absence of available targeted therapy, timely detection of patients at high risk of disease deterioration and effective tailored interventions at an early stage are important and essential clinical management strategies for COVID-19 patients, in addition to active preventive measures.^7,8 Therefore, every effort should be made to explore simple and effective indicators in clinical practice so as to achieve the above goals and thus improve the prognosis of COVID-19 patients.

In our previous study, lymphocyte-C-reactive protein ratio (LCR) on admission, used as an early representative biomarker reflecting the protective immune activation status of the body and the degree of systemic inflammatory response after SARS-CoV-2 invasion, could differentiate disease severity in adult patients with COVID-19, thus serving as an assistant screening tool for admission to hospital and intensive care unit (ICU).⁹ Since changes in the condition of hospitalized adult patients with COVID-19 are common and frequent, and even in some cases the condition tends to deteriorate rapidly without any clinical clues and hints, dynamic changes in LCR may be a stronger clinical predictor of disease severity and progression than just a single LCR on admission. Accordingly, this current study aimed to explore the efficacy of dynamic changes in LCR on differentiating disease severity and predicting disease progression in adult patients with COVID-19.

Patients and methods

Study design and population

This single-centre retrospective study enrolled consecutive adult patients with COVID-19 admitted to the COVID-19 Treatment Centre at the First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China between January 2020 and March 2021. These enrolled patients were divided into moderate, severe and critical groups according to the Diagnosis and Treatment of New Coronavirus Pneumonia (ninth edition).¹⁰ The inclusion criteria were as follows: (i) adults aged ≥18 years; (ii) patients admitted to the COVID-19 Treatment Centre at the First Affiliated Hospital of Harbin Medical University; (iii) a definite diagnosis of COVID-19 with different disease severity between January 2020 and March 2021. The exclusion criteria included: (i) uncontrolled malignant tumours with multiple metastases; (ii) leukaemia (iii) acquired immunodeficiency syndrome; (iv) chronic organ failure (v) immunotherapy or organ transplant within 6 months; (vi) autoimmune disorder (vii) pregnant or breastfeeding women; (viii) incomplete medical records.

This study was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University (IRB number: 2022XS14-02). Written informed consent from participants was not required for this study, in compliance with national legislation and institutional requirements. All details from the enrolled patients were de-identified. The reporting of this study conforms to the STROBE guidelines.¹¹

Demographic and clinical data collection

Demographic information, including age, sex, comorbidities and clinical data, including white blood cell count (WBC), neutrophil proportion (NEUT%), lymphocyte count (LYMPH), lymphocyte percentage (LYMPH%), platelet count (PLT), D-Dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (SCr), albumin (ALB), total bilirubin (TB), direct bilirubin (DBIL), indirect bilirubin (IBIL) and C-reactive protein (CRP) were obtained and collated from medical records on admission, from which sequential organ failure assessment (SOFA) score and LCR were calculated. During hospitalization, LYMPH and CRP were continuously monitored and then utilized for calculation and record of LCR (up to four times), while disease severity of COVID-19 was determined at the same time. Due to the nature of the retrospective study, the number and interval of LCR assays were not uniform and fixed for each enrolled patient. All the above demographic and clinical data were compared among different disease severity groups on admission. These demographic and clinical data were obtained, collated, calculated and recorded by dedicated personnel within the research team. None of the other members the research team was privy to the personal information of the enrolled patients beyond what was required for this study.

Diagnosis and classification of COVID-19

All enrolled adult patients with COVID-19 were confirmed by detection of SARS-CoV-2 nucleic acid on oropharyngeal swabs, nasopharyngeal swabs or lower respiratory tract specimens, after which they were classified into the moderate, severe and critical groups according to the Diagnosis and Treatment of New Coronavirus Pneumonia (ninth edition).¹⁰ For patients with changes in disease severity, the most severe classification of COVID-19 during hospitalization was taken as the final one.

Calculation of LCR

The LCR was calculated by multiplying LYMPH by 10 000 and then dividing by CRP within 24 h of admission.

Statistical analyses

All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 23.0 (IBM Corp., Armonk, NY, USA). Continuous data with a normal distribution are presented as mean ± SD, while those without a normal distribution are presented as median (interquartile range). One-way analysis of variance (ANOVA) and least significant difference method were adopted for three-group and pairwise comparisons of continuous data with a normal distribution, respectively, while Kruskal–Wallis rank sum test and Mann–Whitney U-test were employed for three-group and pairwise comparisons and inter-group comparisons of continuous data without a normal distribution, respectively. ANOVA was used for the inter-group comparisons of LCR at different times of measurement after taking the natural logarithm of LCR to conform to a normal distribution. Spearman’s correlation analysis was used to explore the correlation between dynamic changes in LCR and disease severity of COVID-19 at different times. A P-value <0.05 was considered statistically significant.

Results

A total of 145 adult patients with COVID-19 were enrolled in this study (Figure 1). They were divided into moderate (n = 105), severe (n = 33) and critical groups (n = 7). Age, number of comorbidities, NEUT%, LYMPH, LYMPH%, D-Dimer, ALB, TB, DBIL, IBIL, CRP, LCR, and SOFA score showed significant differences among the three different disease severity groups on admission (P < 0.05 for all comparisons) (Table 1). No significant differences were found in the remaining demographic and clinical data, including sex, hypertension, diabetes mellitus, WBC, PLT, ALT, AST and SCr.

Figure 1.

Flowchart of the enrolment of participants to a study that aimed to explore the value of dynamic changes in lymphocyte-C-reactive protein ratio to differentiate disease severity and predict disease progression in adult patients with Coronavirus disease 2019 (COVID-19).

Table 1.

Comparison of demographic and clinical data among the moderate, severe and critical groups on admission of patients (n = 145) enrolled in a study that aimed to explore the value of dynamic changes in lymphocyte-C-reactive protein ratio (LCR) to differentiate disease severity and predict disease progression in adult patients with Coronavirus disease 2019 (COVID-19).

Characteristic	Moderate groupn = 105	Severe groupn = 33	Critical groupn = 7	F/X²	Statistical analysis^a
Age, years	62.00 (48.00–67.00)	68.00 (60.50–73.50)^b	70.00 (54.00–81.00)^b	10.750	P = 0.005
Sex, female/male	61/44	17/16	5/2	1.013	NS
Hypertension, yes/no	38/67	12/21	2/5	0.177	NS
Diabetes mellitus, yes/no	15/90	7/27	2/5	1.618	NS
Number of comorbidities	1 (1–3)	3 (1–4)^b	2 (1–5)^b	16.444	P < 0.001
WBC, ×10⁹/l	5.01 (3.85–6.45)	4.80 (3.82–6.41)	4.90 (3.65–5.17)	0.112	NS
NEUT%, %	61.40 ± 11.92	70.90 ± 17.67^b	86.51 ± 5.84^bc	16.330	P < 0.001
LYMPH, ×10⁹/l	1.24 (0.89–1.63)	0.66 (0.49–1.05)^b	0.42 (0.30–0.51)^b	36.767	P < 0.001
LYMPH%, %	27.37 ± 9.85	17.40 ± 9.02^b	8.54 ± 4.08^bc	23.825	P < 0.001
PLT, ×10⁹/l	174.00 (140.00–228.00)	150.00 (116.00–223.00)	144.00 (130.00–170.00)	4.643	NS
D-Dimer, μg/l	0.74 (0.54–1.01)	0.91 (0.61–1.63)	3.76 (2.30–8.40)^bc	19.909	P < 0.001
ALT, U/l	25.60 (17.50–36.75)	28.51 (18.29–35.41)	41.00 (15.92–72.82)	1.235	NS
AST, U/l	25.60 (19.36–37.65)	32.41 (19.80–51.43)	37.00 (25.77–79.57)	4.701	NS
SCr, µmol/l	62.25 (53.20–73.75)	62.80 (48.78–81.80)	67.10 (50.80–118.00)	1.381	NS
ALB, g/l	39.74 ± 5.00	35.45 ± 5.57^b	31.98 ± 6.15^b	14.200	P < 0.001
TB, µmol/l	7.80 (6.13–11.71)	12.60 (9.24–17.70)^b	7.77 (7.25–11.80)	15.275	P = 0.001
DBIL, µmol/l	2.00 (0.00–3.10)	3.40 (0.00–5.80)	0.00 (0.00–2.50)	6.130	P = 0.047
IBIL, µmol/l	6.74 (4.30–8.81)	8.60 (5.71–13.72)^b	7.77 (4.30–11.80)	7.467	P = 0.023
CRP, mg/l	9.54 (2.76–20.75)	29.45 (18.41–61.06)^b	61.90 (53.19–98.40)^b	39.572	P < 0.001
LCR	1404.96 (575.52–4941.62)	241.64 (79.68–503.41)^b	66.24 (28.92–125.96)^b	47.851	P < 0.001
SOFA score	0.00 (0.00–1.00）	3.00 (1.25–4.00)^b	6.00 (5.00–11.00)^b	62.246	P < 0.001

Data are presented as mean ± SD, median (interquartile range) or n of patients.

One-way analysis of variance and least significant difference method were adopted for three-group and pairwise comparisons of continuous data with a normal distribution, respectively, while Kruskal–Wallis rank sum test and Mann–Whitney U-test were employed for three-group and pairwise comparisons and inter-group comparisons of continuous data without a normal distribution, respectively; NS, no significant inter-group difference (P ≥ 0.05); ^bP < 0.05 compared with the moderate group; ^cP < 0.05 compared with the severe group.

WBC, white blood cell count; NEUT%, neutrophil proportion; LYMPH, lymphocyte count; LYMPH%, lymphocyte percentage; PLT, platelet count; ALT, alanine aminotransferase; AST, aspartate aminotransferase; SCr, serum creatinine; ALB, albumin; TB, total bilirubin; DBIL, direct bilirubin; IBIL, indirect bilirubin; CRP, C-reactive protein; SOFA, sequential organ failure assessment.

There were significant differences in LCR among different disease severity groups at different times (P < 0.05 for all comparisons) (Table 2).

Table 2.

Inter-group comparison of lymphocyte-C-reactive protein ratio (LCR) at different times among the moderate, severe and critical groups of patients (n = 145) enrolled in a study that aimed to explore the value of dynamic changes in LCR to differentiate disease severity and predict disease progression in adult patients with Coronavirus disease 2019 (COVID-19).

LCR measurement	Moderate groupn = 105	Severe groupn = 33	Critical groupn = 7	F	Statistical analysis^a
First time	7.34 ± 1.62	5.32 ± 1.29^b	4.01 ± 0.83^bc	33.703	P < 0.001
Second time	7.13 ± 1.65	5.35 ± 1.32^b	3.84 ± 0.57^bc	23.714	P < 0.001
Third time	7.07 ± 1.81	5.38 ± 1.15^b	3.54 ± 0.85^bc	20.188	P < 0.001
Fourth time	6.51 ± 1.63	5.50 ± 1.24^b	4.32 ± 1.09^b	6.904	P = 0.002

Data are presented as mean ± SD.

One-way analysis of variance was used for inter-group comparisons of LCR at different times of measurement after taking the natural logarithm of LCR to conform to the normal distribution; ^bP < 0.05 compared with the moderate group; ^cP < 0.05 compared with the severe group.

Dynamic changes in LCR were significantly inversely correlated with disease severity of COVID-19, with correlation coefficients of −0.564, −0.548, −0.550 and −0.429 at four different times, respectively (Table 3).

Table 3.

Correlation between dynamic changes in lymphocyte-C-reactive protein ratio (LCR) at different times and disease severity of Coronavirus disease 2019 (COVID-19) in patients (n = 145) enrolled in a study that aimed to explore the value of dynamic changes in LCR to differentiate disease severity and predict disease progression in adult patients with COVID-19.

LCR measurement	Spearman’s R	Statistical analysis^a
First time	−0.564	P < 0.001
Second time	−0.548	P < 0.001
Third time	−0.550	P < 0.001
Fourth time	−0.429	P < 0.001

Spearman’s correlation analysis was used to explore the correlation between the dynamic changes in LCR and disease severity of COVID-19 at different times, in which LCR was processed by natural logarithm to conform to a normal distribution, whereas disease severity of COVID-19 was regarded as a grade variable (1 = moderate group, 2 = severe group, 3 = critical group).

Discussion

Currently, there is an urgent need for the general public from all walks of life to resume the normalization of work and life under the premise of minimizing the transmission of SARS-CoV-2 and the fatality rate of COVID-19 patients, especially vulnerable populations. Pre-existing immunity acquired through vaccination substantially contributes to preventing the spread of SARS-CoV-2, thus having a positive impact on hospitalization rate, disease progression, mechanical ventilation ratio and fatality rate in COVID-19 patients.^12,13 However, the rapid evolution and constant mutation of SARS-CoV-2 have enabled it to evade pre-existing immunity acquired through vaccination or natural infection and potentially induce breakthrough or recurrent infections of different variants, thus making it impossible to completely control the COVID-19 epidemic by vaccination alone.^14–16 In this context, timely detection of COVID-19 patients at high risk of disease deterioration during hospitalization is a prerequisite for prompt and appropriate interventions to reduce the proportion of critically ill patients and even fatality rate, especially when COVID-19 outbreaks overwhelm relatively limited medical resources. Considering the cost and clinical operability, some cost-effective and easy-to-obtain routine clinical indicators are more conducive to achieving the above goals.

Lymphocytes are cells with an immune recognition function produced by lymphoid organs and they are thus an important part of the body’s immune responses. They are also part of the first line of defence against the invasion of pathogenic microorganisms. Invasion of SARS-CoV-2 into the body can rapidly trigger a cytokine storm followed by lymphopenia through a variety of known mechanisms, including T-cell redistribution, over-activation, direct destruction, apoptosis, severe depletion and termination of normal T cell activation.^16–23 Therefore, lymphopenia has been widely established as a critical factor associated with disease severity and progression, and poor prognosis in COVID-19 patients.^23–26 It has been demonstrated that a systemic inflammatory response plays a key role in the occurrence and development of COVID-19, although several other mechanisms are also involved.²⁷ CRP, one of the non-specific acute phase reactants in the systemic inflammatory response, has been found to be significantly elevated after SARS-CoV-2 invasion, especially in COVID-19 patients with disease deterioration, with similar predictive value to lymphopenia and an important clinical value in identifying secondary bacterial infections and guiding antibiotic therapy.^28–31 CRP can also promote the progression of a systemic inflammatory response by activating the complement system to induce the production and release of pro-inflammatory cytokines and apoptosis.³²

The LCR, as the ratio of the above two clinical indicators, reflects the activity of the body’s immune system against SARS-CoV-2 invasion and the degree of the systemic inflammatory response, which is closer to the complex pathogenesis of COVID-19, and therefore may be a stronger predictor than either of the above two clinical indicators for COVID-19 patients. LCR was initially utilized as a promising immuno-inflammatory biomarker associated with tumour immune microenvironment in malignancies to predict post-operative and long-term outcomes or identify patients at high risk of recurrence.^33–35 However, its clinical application has been extended to many diseases involving the body’s immune function and inflammatory response, including infectious diseases such as COVID-19.^36,37 Several studies have explored the predictive and prognostic potentials of LCR in COVID-19 patients, but neither previous nor ongoing trials have focused on dynamic changes in LCR during hospitalization.^9,38,39 This current study is the first attempt to explore the efficacy of dynamic changes in LCR on differentiating disease severity and predicting disease progression in adult patients with COVID-19.

In this current study, multiple demographic and clinical data showed significant differences among different disease severity groups on admission, suggesting once again that the mechanisms of disease deterioration in COVID-19 may be complex and closely related to the patient’s underlying condition, inflammatory response, immune function, blood coagulation, multi-organ function and their interactions.²⁷ Significant differences were found in LCR among different disease severity groups at different times after admission, which indicated that LCR had the persistent and stable ability to distinguish disease severity of COVID-19 patients in the early stage of admission in the current study. Dynamic changes in LCR were found to be significantly inversely correlated with disease severity of COVID-19, revealing that the smaller the value of LCR, the more severe the condition of COVID-19, and vice versa. Therefore, dynamic changes in LCR contribute to distinguishing disease severity, determining the direction of disease progression, identifying patients at high risk of disease deterioration as early as possible, and accordingly providing timely and appropriate interventions in adult patients with COVID-19. As the correlation coefficients continued to decline, so did the ability of LCR to distinguish disease severity in adult patients with COVID-19. In other words, LCR on admission had the strongest discriminative and predictive potentials.

This current study had several limitations. It is a single-centre retrospective study, which inherently limits the reliability and generalizability of our findings. Due to the lack of dynamic LCR surveillance, the exclusion of asymptomatic and mild COVID-19 cases led to a reduction in sample size, and thus the current results need to be interpreted with caution. Additionally, this current study did not compare LCR with other clinical parameters and existing scoring systems that reflect disease severity of COVID-19, pointing to a potential direction for future research. Lastly, this current study did not explore the underlying specific mechanisms on the occurrence and development of COVID-19, indicating an area for further investigation. These limitations suggest that the current conclusions are only preliminary and require further validation through more comprehensive future studies.

In conclusion, LCR is essentially a cost-effective and easy-to-obtain routine clinical immuno-inflammatory score. Dynamic changes in LCR may serve as a simple and effective clinical indicator to differentiate disease severity and predict disease progression in adult patients with COVID-19. These current findings provide another simple and effective clinical indicator for the timely identification of adult COVID-19 patients at high risk of disease deterioration, which needs to be further validated in future studies with larger sample sizes.

Footnotes

Acknowledgements

The authors are grateful to all colleagues who worked with them in the COVID-19 Treatment Centre at the First Affiliated Hospital of Harbin Medical University and all those who provided selfless advice and help for this article. We pay tribute to the medical staff who lost their lives in the national fight against the COVID-19 epidemic.

Author contributions

Dan Wang, Yang Gao, Qi-Qi Lai, Jian-Nan Zhang, Dong-Sheng Fei, and Kai Kang took part in the literature search, conception, study design, statistical analysis, analysis and discussion of results, and manuscript preparation, editing, and review. Di Wu, Hui-Ying Liu, Huan Meng, Xin-Tong Wang, Yu-Jia Tang, Jia-Xi Xu, Jia-Ning Zhang, and Bo-Wen Liu provided assistance for literature search, data acquisition and collation, statistical analysis, analysis and discussion of results, and manuscript preparation. All authors read and approved the final article. Dan Wang, Yang Gao, and Qi-Qi Lai contributed equally to this work.

Declaration of conflicting interest

The authors declare that there are no conflicts of interest.

Funding

This study was supported by grants from the National Natural Science Foundation of China (no. 82372172), the Key Research and Development Plan Project of Heilongjiang Province (no. GA23C007), the Heilongjiang Province Postdoctoral Start-up Fund (no. LBH-Q20037), the Research Project of Heilongjiang Provincial Health Commission (no. 20231717010461), the Special Fund for Clinical Research of Wu Jie-Ping Medical Foundation (no. 320.6750.2022-02-16) and the Scientific Research Innovation Fund of the First Affiliated Hospital of Harbin Medical University (no. 2021M08).

ORCID iDs

Yang Gao

Jian-Nan Zhang

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