Predicting the potentially exacerbation of severe viral pneumonia by MuLBSTA score joint CD4+ and CD8+ cell counts: construction and verification of risk warning model

doi:10.21203/rs.3.rs-3748872/v1

Purpose

This study mainly focuses on the immune function and introduces CD4⁺, CD8⁺ and their ratios based on the MuLBSTA score, a previous viral pneumonia mortality risk warning model, to construct an early warning model of severe viral pneumonia risk.

Methods

A retrospective single-center observational study was operated from January 2021 to December 2022 at the People's Hospital of Liangjiang New Area, Chongqing, China. A total of 138 patients who met the criteria for viral pneumonia were selected and their data, including demographic data, comorbidities, laboratory results, CT scans, immunologic and pathogenic tests, treatment regimens, and clinical outcomes, were collected and statistically analyzed.

Results

41 patients (29.7%) developed severe or critical illness. A viral pneumonia severe risk warning model was successfully constructed, including eight parameters: age, bacterial coinfection, CD4⁺, CD4⁺/CD8⁺, multiple lung lobe infiltrations, smoking, hypertension, and hospitalization time. The risk score for severe illness in patients was set at 600 points. The model had good predictive performance (AUROC = 0.94397), better than the original MuLBSTA score (AUROC = 0.8241).

Conclusion

A warning system constructed based on immune function has a good warning effect on the risk of severe conversion in patients with viral pneumonia.

viral pneumonia

MuLBSTA score

warning system

Viral pneumonia is an important cause of severe pneumonia in adults¹, often accompanied by acute respiratory distress syndrome (ARDS) , which prolong the course of the disease, elevated the mortality rate, and increase the treatment cost^2,3. Even though oseltamivir (for influenza A) and Nirmatrelvir/Ritonavir (for COVID-19) can play the antiviral role, there are still many problems, such as the dependence on the detection of pathogenic microorganisms in the early stage, the control of indications, etc. Moreover, in some cases, it cannot block the immune escape, cytokine storms, and persistent lung injury induced by viral infection. Early identification of patients with a tendency to develop severe illness and actively blocking their progression is a key link in reducing the mortality rate of viral pneumonia⁴. Hypoxia is an important pathophysiological event in viral pneumonia. After all, Non-invasive support for improved oxygenation, lung and diaphragm protection may help avoid tracheal intubation, thereby preventing complications of sedation and invasive mechanical ventilation⁵. And there are also new studies focus on anti-inflammatory therapies, such as blocking NF-κB and activate Nrf2 pathways⁶.

The MuLBSTA score, based on six common parameters is effective in predicting mortality risk in viral pneumonia. Hypo-Lymphocytosis is an important event in viral pneumonia. Since immune deficiency is strongly associated with mortality, assessment immune status could be conductive to estimate prognosis in viral pneumonia. T lymphocytes are key cells of adaptive immune protection and play a critical role in assisting in the production of antibodies and direct clearance of viruses⁷. Single-cell transcriptome analyses showed there were apoptosis in various CD4⁺T cells and CD8⁺T cells subsets^8,9, and some special effect subgroups were associated with convalescence in clinical moderate groups, after respiratory viruses infection(H1N1)⁹and COVID-19¹⁰. Therefore CD4⁺T cells and CD8⁺T cells should be the immune function indicators to promote predictive performance of MuLBSTA score. In order to investigate the above clinical scenario, we established a retrospective single-center research cohort and build a more convenient model to predict the incidence of severe viral pneumonia, providing a basis for early treatment planning and prognostic evaluation.

Study design:

A retrospective single-center observational study was conducted at the People's Hospital of Liangjiang New Area, Chongqing, China, from January 2021 to December 2022. The study was approved by the hospital's ethics committee. We retrospectively analyzed hospitalized patients who tested positive for respiratory viruses using a multiplex real-time reverse transcription-polymerase chain reaction assay. Patients who met the diagnostic criteria for pneumonia in the Chinese Adult Community-Acquired Pneumonia Diagnosis and Treatment Guidelines (2016 edition) were included in the study, while those who were under 18 years old, had liver function failure, were long-term dialysis patients, suspected of having active tuberculosis, had thrombosis or pulmonary embolism, had primary immunodeficiency diseases or were receiving immunosuppressive therapy (such as AIDS, post-organ transplantation or neutropenia), were undergoing anti-tumor drug therapy or radiation therapy, or were unable to undergo the required experimental investigations were excluded. A total of 235 hospitalized patients tested positive for viral infection, among whom 162 cases were diagnosed as pneumonia. A total of 24 patients were excluded :19 patients had other diseases affecting immune function, and 5 patients lacked relevant laboratory tests. Finally, 138 patients with pneumonia who tested positive for the virus were enrolled (Supplement Figure 1).

Data collection:

Various viruses, including influenza (Flu), coronavirus (CoV), adenovirus (AdV), bocavirus, human rhinovirus (HRV), respiratory syncytial virus B (RSVB),

parainfluenza virus (PIV), enterovirus (EV)respiratory syncytial virus A (RSVA), and human metapneumovirus (HMPV), were Validated through RT-PCR. Information was collected upon admission, including demographic data, comorbidities, laboratory results, CT scans, etiological and immunological testing. Positive bacterial cultures from blood and sputum specimens were used as a criterion for co-infection with bacteria, and antiviral and steroid regimens were recorded. Viral pneumonia patients were divided into severe and non-severe groups, with the severe group including critically ill and severely ill patients. Critically ill patients present one of the following symptoms: (1) Dyspnea (>30 breaths/minute), (2) resting oxygen saturation <93%, or (3) oxygenation index (PaO2/FiO2) <300mmHg; critically ill patients exhibited any of the following: (1) respiratory failure requiring invasive ventilation, (2) shock, or (3) other organ failures requiring ICU care. The MuLBSTA score was counted for each patient, and the hospitalization time and prognosis were recorded. The result of the mortality rate was defined as the total mortality rate within 90 days.

Statistical analysis:

Normality was tested using the Shapiro-Wilk normality test statistic; normally distributed data is denoted by ±s; intergroup comparisons were performed using t-tests. The comparison of unordered count data between groups was performed using the chi-square test or Fisher's exact test. The division point was determined based on the Youden's index of the receiver operating characteristic (ROC) curve or clinically relevant cutoff points. Variables with p<0.10 were considered potential risk factors and included in the multivariate regression analysis. The predictive performance of the score was assessed by measuring the area under the ROC curve (AUROC). Statistical analyses were performed using SPSS version 22.0 and R 3.5. All tests were two-sided, and p values <0.05 were considered statistically significant.

1. Description of demographic characteristics

The baseline characteristics of the complete case are shown in Table 1. The average age of the patients with viral pneumonia in this study was 64.40 years, with the majority being elderly, of which 60.14% were male. There were 41 patients with severe or critical illness, accounting for 29.7%. Nearly half of the patients had a history of smoking (49.27%, of which 10.14% had quit smoking before the onset of the disease). A total of 78 (56.52%) patients suffered from comorbidities, of which hypertension was the most common, accounting for approximately 36.96%, followed by diabetes, nephropathy, coronary heart disease, and chronic obstructive pulmonary disease (COPD). The virus types of the patients with viral pneumonia were shown in supplementary Table 1, which mainly consisted of novel coronavirus pneumonia. Multiple logistic regression analysis suggested a significance of 0.879 (greater than 0.5), which had no effect on the risk of severe transformation. Among the patients with viral pneumonia, 50 patients (36.23%) were co-infected with bacteria. The types of bacteria were shown in supplementary Table 2, among which Klebsiella pneumoniae, Acinetobacter baumannii and Escherichia coli were the three most common pathogens. Of all patients, 95 (68.84%) received antiviral treatment, 41

Table1：Population description and comparison

	Total（N=138）	No-Severe（N=97）	Severe（N=41）	p
Gender/Male	83(60.14%)	56（57.73%）	27（65.85%）	0.4264
Age	64.40±16.43	59.98±15.63	74.63±13.38	0.0001
Acute-smoker	54（39.13%）	32（32.99%）	22（53.666%）	0.0378
Quit-smoker	14（10.14%）	9（9.28%）	5（12.20%）
Non-smoker	70（50.72%）	56（57.73%）	14（34.15%）
Comorbidity
Hypertension	51（36.96%）	28（28.87%）	23（56.10%）	0.0025
COPD	24（17.39%）	16(16.49%)	8（19.51）	0.8062
Diabetes	15（10.87%）	9（9.28%）	6（14.63%）	0.3778
Coronary heart disease	8（5.78%）	5（5.15%）	3（7.32%）	0.6946
Cerebral infarction	7（5.07%）	3（3.09%）	4（9.76%）	0.1958
renal disease	5（3.26%）	3（3.09%）	2（4.87%）	0.6335
history of malignancy	4（2.90%）	1（10.31%）	3（7.32%）	0.0785
Hepatitis	2（1.45%）	0	2（4.87%）	0.0867
Treatment
High flow/noninvasive	41（29.71%）	12（12.37%）	29（70.73%）
invasive mechanical ventilation	12（8.79%）	0	12（29.27%）
antiviral treatment	95（68.84%）	60（68.04%）	29（70.73%）
steroid treatment	60（43.48%）	35（36.08%）	25（60.96%）
antibiotic therapy	59（42.75%）	27（27.84%）	32（78.05%）
vasoactive agent	15（10.87%）	0	15(36.59%)
ICU days	2.405±5.81	0	8.09±8.279	<0.0001
Hospital days	17.86±34.8	11.71±5.383	32.39±61.15	0.0012
CD4+	414.4±247.9	449.8±233.5	283.7±192.2	<0.0001
CD8+	261.6±131.3	308.6±165.8	245.7±111.4	0.0242
CD4/CD8	1.672±0.8903	1.865±0.866	1.207±0.756	<0.0001
lymphocyte count	1.073±0.758	1.167±0.66	0.858±0.920	<0.0001
Multilobar infiltrates	46（33.33%）	22（22.68%）	24（58.54%）	0.0024
Co-infection with bacteria	50（36.23%）	21（24.13%）	29（70.73%）	0.0002

Table 1 ：Population description and comparison. P-value represented the comparison between Severe group and non-severe group. p-values < 0.05, indicated signifificant difference between Severe group and non-severe group. COPD：chronic obstructive pulmonary disease

received high-flow/non-invasive ventilation, and 12 received invasive ventilation. The average length of hospital stay for patients with viral pneumonia was 17.86 days. T-test and chi-square test were used to analyze the statistical differences in age, smoking, hypertension, ICU days, and hospital days, CD4⁺, CD8⁺, CD4⁺/CD8⁺, lymphocyte count, multiple lung lobe infiltrations, and bacterial coinfection between severe and non-severe groups of viral pneumonia.

Figure 1: According to the results of the LASSO regression analysis, 9 features were identified as the potential predictors.

2. Determination of Early Warning Model Indicators

Meaningful indicators from preliminary statistical analysis were selected using LASSO regression analysis and logistic multiple regression analysis to screen the model's independent variables. LASSO regression analysis was performed using R code, and as shown in Figure 1, the dashed line on the right corresponds to a parameter of 9, indicating that the coefficients of 9 genes can be used as model parameters. The specific coefficient estimates for each independent variable obtained from the LASSO regression analysis are shown in supplementary Table 3. Among them, the independent variables with non-zero coefficient estimates are the selected independent variables, including age, Coinfection, CD4⁺, CD4⁺/CD8⁺ratio, multiple lung infiltrates, smoking, hypertension, ICU days, and hospital days. The independent variables with larger coefficient estimates correspond to more important indicators, while those with smaller coefficient estimates can be screened out. Logistic multiple regression was then performed using R language, with severity as the independent variable for this prediction model. The analysis results are shown in supplementary Table 4: except for ICU hospitalization time, there were significant differences (P<0.05) between the other variables and the target variable (severity), including age, coinfection, CD4⁺, CD4⁺/CD8⁺, multiple lung infiltrates, smoking, hypertension, and hospitalization time, which were included in the model construction.

3.Building a Prediction Model and Evaluating its Performance

In this study, we chose to use 70% of the original data as the training set and the 30% as the test set. The model prediction results of the training set and test set were evaluated using ROC value, K-S chart, and Lift chart. As shown in Figure 2, the model result ROC of the training set data is 0.94118(Figure 2A), the K-S curve is more obvious towards the upper left (Figure 2B), and the Lift chart curve is relatively flat (Figure 2C), indicating that the model has high accuracy in predicting severe cases. The test set model prediction results are shown in Figure 2, with a ROC of 0.94397(Figure 2D), which is better than the ROC of the training set model prediction results. The K-S curve is more obvious towards the upper left (Figure 2E), and the Lift chart curve is relatively flat (Figure 2F), indicating that the model prediction performance is stable and has high accuracy in predicting severe cases. Compared with the original MulBSTA score (Figure 2G), this model has higher efficiency in predicting the risk of severe conversion (AUROC=0.94937vs.0.0.8241).

Figure 2: Building a Prediction Model and Evaluating its Performance. A.ROC diagram of the training set model; B: K-S diagram of the training set model ;C: Lift diagram of the test set model. D:ROC diagram of test set model; E： K-S diagram of test set model；F：Lift diagram of test set model；G：ROC curve of MulBSTA score prediction.

4.Production and evaluation of a severe risk scorecard

Based on the above analysis, the predictive model used this time has good predictive ability for whether patients will convert to severe cases. The evaluation card of the

Table2.Results of scorecard model bins

variate	Range	sample size	woe	Score
age	[-Inf,66)	40	1.156	-80
	[66,70)	15	1.598	-110
	[70,76)	16	-0.531	37
	[76,86)	12	-2.140	148
	[86,Inf)	9	-0.818	56
Co-infection	No	61	0.849	-60
Co-infection	Yes	31	-1.106	78
CD4	[-Inf,140)	10	-1.889	42
	[140,180)	6	-1.041	23
	[180,340)	21	0.405	-9
	[340,360)	5	-1.447	32
	[360,Inf)	50	0.774	-17
CD4_CD8_ratio	[-Inf,0.9)	20	-1.447	76
	[0.9,1.6)	21	0.405	-21
	[1.6,1.7)	8	-0.531	28
	[1.7,Inf)	43	0.987	-52
Multi-lobe	No	63	0.512	-14
Multi-lobe	Yes	29	-0.834	23
smoking	No	43	0.596	-32
	Once	9	0.211	-11
	Yes	40	-0.531	28
hypertension	[-Inf,2)	57	0.506	14
hypertension	[2,Inf)	35	-0.636	-18
Hospital days	[-Inf,8)	15	-0.348	23
	[8,12)	29	1.561	-101
	[12,16)	26	0.394	-26
	[16,22)	10	-1.447	94
	[22,32)	7	-0.125	8
	[32,Inf)	5	-2.428	158

model is shown in Table 2. The score for whether the patient has a severe risk is set to 600 points. The visualization of the independent variables participating in the model is shown in Figure 3A. Finally this study used the PSI (Population Stability Index) index to evaluate the stability of the scorecard. The evaluation card indicators established in the model were calculated and visualized by PSI using R language, as shown in Figure 3B. The PSI result for whether the patient has severe risk is 0.1776, which is less than 0.25, indicating that the changes are small and the model has good stability.

Figure 3: Evaluation of a severe risk scorecard. A. Model nomogram;B .Evaluation card PSI evaluation result plot.

Viral pneumonia is a lung inflammation caused by different types of viruses invading lung tissue and the incidence of viral pneumonia requiring ICU admission fluctuated between 16% and 49%¹¹. Over the past two decades, 4 respiratory viruses have causing particular concern because of the high proportion of critically ill and acute respiratory failure among patients with viral infections: influenza, especially influenza A H1N1 2009; and 3 novel coronaviruses, Middle Eastern respiratory syndrome coronavirus, SARS coronavirus and severe acute respiratory syndrome coronavirus 2

(SARS-CoV-2) ¹². Although the survival rate of hospitalized patients with virus-associated pneumonia varies with the pathogenic virus subtype¹³, ARDS, sepsis, hypercapnia associated with pulmonary fibrosis, and secondary bacterial infection¹⁴ were the main causes for the process of becoming more serious. Virus infection will activate the host immune system. In severe cases, it can cause immune cells to secrete a large number of cytokines, trigger inflammatory storms that exacerbates immune disorders, coagulation dysfunction, and endothelial cell injury, leading to ARDS, sepsis, and multiple organ insufficiencies¹⁵ , resulting in higher ICU occupancy rates and mortality rates. Currently, clinical study on the novel coronavirus suggested that antiviral treatment should be initiated in a timely manner to delay disease progression and improve prognosis¹⁶. However, the diversity of virus types makes it difficult to standardize antiviral treatment plans. Therefore, there is an urgent need for clinicians to more accurately predict morbidity and mortality in critically ill patients with viral pneumonia¹⁷. In this study we found that immunocompetent status, age, and comorbidities were strongly associated with the severity of viral pneumonia. In fact, age is another independent risk factor for severe viral pneumonia¹⁸. The severity and risk of death increases significantly with age, especially in elderly patients over 65 years of age¹⁹ , partly due to an significantly immune responses of extreme age¹⁸.

MuLBSTA score is a multidimensional index score developed by Shanghai Ruijin Hospital to predict the risk of death from viral pneumonia, which includes age, smoking status, hypertension, multiple lobar infiltration, lymphocyte ≤ 0.8∗10⁹ /L, and bacterial coinfection²⁰. Subsequent studies have also validated its usefulness in identifying high-risk patients with severe COVID-19 pneumonia^21,22. The ratio, number and function of different subpopulations of lymphocytes directly affect the immune status of the organism, and better reflect the immune function than the lymphocyte count, and helps to observe the therapeutic effect and evaluate the prognosis. Therefore, we introduced lymphocyte subpopulations, analyzed and compared the role of both in predicting viral pneumonia.

The pathogenesis of severe respiratory tract infections caused by viruses mainly involves the dysregulation of pro-inflammatory/anti-inflammatory responses and immune imbalance, such as overreaction of the immune system, excessive apoptosis and functional exhaustion of immune cells, immune suppression or immune paralysis²³ . CD8⁺ and CD4⁺ T cells are essential for maintaining the balance between cellular and humoral immunity²⁴，and they are both involved in the deterioration of COVID-19 pneumonia. Studies showed that the changes in peripheral blood CD4⁺ and CD8⁺ T cells decreased significantly in severe and critical patients with COVID-19[(In Chinese)]. The absolute value and percentage of CD8+ T cells showed a downward trend in ICU patients with viral pneumonia²⁵. In this study, the number of CD4⁺ and CD8⁺ T cells in patients with severe and critical viral pneumonia were significantly decreased (Table1). The ratio of CD4⁺ and CD8⁺T cells represents the ratio of Th cells and suppressive T cells and the decrease in the ratio indicates a higher degree of immune aging and a higher risk of death²⁶. However, this change was different after viral infection.

In the critical patients of COVID-19, some researchers found that lymphocyte subsets were significantly decreased and CD4⁺/CD8⁺ T cell ratio remained normal²⁷. However, in another group of COVID-19 infected patients, the CD4+/CD8+ T cell ratio may also be elevated due to a more pronounced reduction in CD8⁺ T cells than in CD3⁺, CD4⁺ and CD8⁺ T lymphocytes²⁸ . There are also reports revealing the ratio of CD4⁺/CD8⁺ T cell decreased in viral pneumonia, which may be due to the suppression of CD4⁺ T cells, the decrease in the absolute number of lymphocytes, and the activation of cytotoxic CD8+ T cells²⁹. Actually, different changes in CD4⁺/CD8⁺ ratio is a reflection to the immune function in different stages of viral pneumonia. This study introduced CD4⁺, CD8⁺ and their ratio, which could more stably and comprehensively reflect the patient's systemic immune status. Results showed that compared with the original MulBSTA score, this model has higher efficiency in predicting the risk of severe conversion (AUROC=0.94937vs.0.8241)（Figure2G）. The viral pneumonia severity risk prediction model constructed in this study includes indicators of different dimensions such as demographic characteristics, comorbidities, immune function, imaging, and has good predictive ability （AUROC=0.94937vs.0.8241). Hence, it may help control the risk of severe conversion of viral pneumonia and may become a screening tool for severe viral pneumonia. This study also has some limitations. The retrospective single-center design led to missing partial data and inevitable bias.

We constructed a risk score incorporating eight previously identified parameters routinely used in hospitals that are highly predictive of severe disease in viral pneumonia. Hospitalized patients were stratified according to the level of risk of critical illness to guide further clinical decisions.

Ethics approval and consent to participate：The study was approved by Ethics Committee of People's Hospital of Chongqing Liangjiang New Area, Chongqing。This study was a retrospective database study, based on existing data and did not involve patient intervention. The Ethics Committee of People's Hospital of Chongqing Liangjiang New Area Chongqing had agreed to exempt patients from informed consent.

Consent for publication：Not applicable

Availability of data and materials：The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests：All authors declare that there is no conflict of interest.

Funding：Chongqing science and health joint medical research project (2022MSXM107)：Xi Chen；Chongqing science and health joint medical research project（2023QNXM005）：Bei Ma.

Authors' contributions：The experimental design was completed by X C, B M,

and F X. X C, B M, Y Y , M Z are responsible for all experimental operations and data analysis. The manuscript was jointly finished by X C and FX then finally revised by FX and Mu Zhang. All authors read and approved the final manuscript.

Acknowledgement ：Not applicable

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No competing interests reported.

Predicting the potentially exacerbation of severe viral pneumonia by MuLBSTA score joint CD4+ and CD8+ cell counts: construction and verification of risk warning model

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