Construction of a Novel Prognostic Risk Warning Model for Bunyavirus Patients Based on Inflammatory Indicators

doi:10.21203/rs.3.rs-5190213/v1

Objective To analyze the risk factors for poor prognosis in patients with SFTS and construct an early warning model. Methods 207 patients with SFTS admitted to Huazhong University of Science and Technology Affiliated Tongji Hospital from April 1, 2023 to July 18, 2024 were selected for retrospective analysis. According to the patients' prognosis, they were divided into survival group (n=133) and death group (n=74). The independent risk factors affecting the prognosis of patients were analyzed by univariate analysis and multivariate logistic regression analysis of general information and inflammatory indicators within 24 hours. An early warning model was established based on the independent risk factors. Results Age (OR = 1.162, 95% CI (1.102 ~ 1.236), P <0.001), viral load (OR = 2.669, 95% CI (1.764 ~ 4.239), P <0.001), PCT (OR = 2.758, 95%CI (1.586 ~ 5.231), P = 0.001), and IL-10 (OR = 1.005, 95% CI (1.001 ~ 1.009), P = 0.19) were independent risk factors affecting the prognosis of patients. A nomogram model was constructed based on the four risk factors, and the predictive performance of the model was good (ROC = 0.905, 95%CI (0.862 ~ 0.949), P <0.001). Conclusion The prognostic risk early warning model constructed in this study has good predictive effect, which can be used as a clinical tool for predicting the prognosis of SFTS patients.

Biological sciences/Microbiology

Biological sciences/Microbiology/Infectious disease diagnostics

SFTS

prognosis

early warning model

nomogram

Fever with thrombocytopenia syndrome (severe fever with thrombocytopenia syndrome, SFTS) is an acute epidemic infectious disease caused by the Dabepanda virus (DBV, also known as the novel Bunyavirus). Clinical manifestations include fever and thrombocytopenia, which may be accompanied by nausea, vomiting, malaise, loss of appetite, diarrhea, and muscle pain. Critically ill patients may present with skin petechiae, pulmonary hemorrhage, gastrointestinal hemorrhage, impaired consciousness, and even multi-organ failure [1]. In the early stages of detection of the disease, the morbidity and mortality rate can be as high as 30% [2]. With the advancement of medical technology, the morbidity and mortality rate of SFTS in China has decreased from 10.58% in 2011 to 5.07% in 2021 [3]. However, the morbidity and mortality rate of critically ill patients is still high, and the morbidity and mortality rate of SFTS patients with central nervous system involvement and multiple organ failure can be as high as 44.7% [4]. Currently, there are relatively few predictive models for SFTS disease, and the indicators emphasized by the existing models vary widely and lack uniform standards [5–6]. Therefore, the establishment of predictive risk early warning models for SFTS patients to help clinical staff identify potential critically ill patients at an early stage, detect changes in their conditions promptly, and take active measures and nursing interventions is of great significance to improve the success rate of patient treatment and reduce the morbidity and mortality rate.

Criteria for inclusion and exclusion

This study was a retrospective analysis. The 207 SFTS patients who were hospitalized in the Department of Infection and Intensive Care Unit (ICU) of our hospital from April 1, 2023, to July 18, 2024, were selected as the study subjects, including 89 males and 118 females, with a mean age of (64.309 ± 9.978) years. Inclusion criteria: ① age ≥ 18 years; ② meet the diagnostic criteria of SFTS [1], the positive nucleic acid test of novel Bunyavirus in the case specimen. Exclusion criteria: (1) patients with incomplete laboratory data; (2) patients diagnosed in outpatient clinics but not hospitalized; (3) patients with malignant tumors and hematological diseases. The 28th day after admission was set as the observation window. Patients who died within this period were categorized in the death group, while those who continued to survive were included in the survival group. This study has adhered to all pertinent national regulations and institutional policies, and has been approved by the local ethics committee, with a waiver for informed consent. The ethical review of the study was conducted by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. As this study is a retrospective analysis and the personal information of the patients involved has been anonymized to ensure the protection of individual privacy, the Ethics Committee has granted a waiver for informed consent.

General information collection

The collection of relevant morbidity risk factor information was completed by reviewing the electronic medical records during the hospitalization of the enrolled SFTS. Clinical information of SFTS included: temperature, days from onset to diagnosis, days of hospitalization, anorexia, nausea, vomiting, abdominal pain, diarrhea, swollen lymph nodes, alteration of consciousness, diabetes mellitus, hypertension, coronary artery disease, cerebral infarction, history of smoking, and history of alcohol consumption.

Laboratory test indexes

All study subjects collected relevant inflammatory indexes, such as blood routine, inflammatory factors, CRP, PCT and ferritin, when SFTS patients were first admitted to the hospital, including white blood cells (WBC), neutrophils (NEU#), lymphocytes (LYM#), the ratio of neutrophils to lymphocytes (NLR), red blood cells (RBC), hemoglobin ( HGB, platelet (PLT), interleukin IL-1β, interleukin IL-2R, interleukin IL-6, interleukin IL-8, interleukin IL-10, tumor necrosis factor (TNF-α), C-reactive protein (CRP), procalcitonin (PCT), ferritin (FER), and SFTSV were used. Blood routine-related indexes were detected by SYSMES XE2100 hematology analyzer; biochemical indexes such as CRP, PCT and FER were detected by Roche C8000 biochemical analyzer; cytokines IL-1β, IL-2R, IL-6, IL-8, IL-10 and TNF-α were detected by Solem auto-chemiluminescence analyzer; and SFTSV load was detected by Xi'an Tianlong PANA9600s. The SFTSV load was detected by Xi'an Tianlong PANA9600s extractor and GENTIER 96E amplifier. The amplification reagents were produced by Zhongshan Daan Gene Co. The experimental procedures were performed according to the instruction manual.

Statistical methods

Data were analyzed using SPSS 20.0 statistical software. Shapiro-Wilk test was used to test the normality of the data. Normally distributed data were expressed as mean ± standard deviation, and skewed data were expressed as median (quartiles) M (P25, P75). The independent samples t-test was used for comparison of normally distributed data, and the Mann-Whitney U-test was used for comparison of skewed data. The χ² test was used to compare the two-component ratios; ROC curves were plotted and sensitivity, specificity and AUC were calculated. p < 0.05 was considered statistically significant. Binary multivariate logistic regression analysis was used and the goodness of fit of the model was assessed using the Hosmer-Lemeshow test. The independent risk factors obtained were used to establish a nomogram model using R language (4.3.1). Internal validation was performed using the Bootstrap method and the predictive power of the model was verified using the C index, calibration curves, and ROC curves. p < 0.05 was considered statistically significant.

Comparison of general information between the two groups

There was no statistically significant difference between the two groups in terms of sex ratio, history of diabetes, history of cerebral infarction, history of hypertension, history of smoking, history of alcohol consumption, anorexia, nausea, vomiting, abdominal pain, diarrhea, lymph node enlargement, temperature, and days from onset of disease to admission to the hospital (P > 0.05); whereas, the proportion of age, history of coronary artery disease, and alteration of consciousness were lower in the survival group than that in the death group. The differences were statistically significant (P < 0.05). See Table 1.

Comparison of blood routine and inflammation-related indexes between the two groups

There were no statistically significant differences between the two groups in terms of white blood cells (WBC), neutrophil counts (NEU#), lymphocyte counts (LYM#), neutrophil-to-lymphocyte ratios (NLR), red blood cells (RBC), and hemoglobin (HGB) (P > 0.05); compared with the surviving group, the dead group had platelets ( PLT was significantly lower, and C-reactive protein (CRP), procalcitonin (PCT), ferritin (FER), interleukin-1β (IL-1β), interleukin-2 receptor (IL-2R), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10) and tumor necrosis factor-α (TNF-α) were significantly higher in the death group compared with the survival group. ) were significantly elevated, and the difference was statistically significant (P < 0.05). See Table 2.

Correlation Analysis of Independent Variables

Among the 14 independent variables included, Pearson correlation analysis was used to analyze the correlation between the variables, as shown in Figure 1. The analysis shows that there is no significant correlation between the independent variables. Meanwhile, to reduce the effect of homoskedasticity on the model, variance alignment analysis was performed, and it was found that there were no variables with variance inflation factor (VIF) > 10, and there was no need to exclude variables.

Multivariate analysis of prognosis in patients with fever with thrombocytopenia syndrome (SFTS)

A binary Logistic multivariate analysis was performed with the above 14 variables as independent variables and prognosis as dependent variables. Four independent risk factors were finally identified, including viral load (OR = 2.669, 95% CI (1.764 ～ 4.239), P < 0.001), age (OR = 1.162, 95% CI (1.102 ～ 1.236), P < 0.001), procalcitoninogen (PCT) (OR = 2.758, 95% CI ( 1.586 to 5.231), P = 0.001), interleukin-10 (IL-10) (OR = 1.005, 95% CI (1.001 to 1.009), P = 0.019). See Table 3.A regression equation was established: Ln (P/1 - P) = -15.327 + 0.982 × X1 + 0.15 × X2 + 1.015 × X3 + 0.005 × X4.

Establishment of prognostic risk warning model for SFTS patients

The ROC curves of each variable of independent risk factors were plotted separately, as shown in Figure 2, and the AUC of each independent risk factor is shown in Table 4. The AUC of each independent risk factor is shown in Table 4. The AUC for predicting the risk of death in SFTS patients based on age alone was 0.706 (95% CI: 0.628 - 0.779), with an optimal cutoff value of 64 years old (sensitivity 78.4%, specificity 54.9%); the AUC for predicting the severity of SFTS patients based on the logarithm of viral load was 0.822 (95% CI: 0.756 - 0.883), with an optimal cutoff value of 0.822 (95% CI: 0.756 - 0.883). CI: 0.756 - 0.883), with an optimal cut-off value of 4.068 (sensitivity 73.0%, specificity 82.0%), PCT predicted the severity of SFTS patients with an AUC of 0.770 (95% CI: 0.704 - 0.831), with an optimal cut-off value of 0.51 (sensitivity 56.8%, specificity 83.5%), IL-10 predicted the severity of SFTS patients with an AUC of 0.770 (95% CI: 0.704 - 0.831), and the AUC of 0.51 (sensitivity 56.8%, specificity 83.5%). AUC of 0.770 (95% CI: 0.704 - 0.831) with an optimal cut-off value of 66.6 (sensitivity 56.8%, specificity 83.5%) for severity in SFTS patients. Based on the independent risk factors derived from the Logistic regression analysis, a nomogram model of prognosis for SFTS patients was developed, as shown in Figure 3. To validate this model, the calibration curve was close to the diagonal line, indicating that the predictive probability of this model was close to the actual incidence rate, and the constructed nomogram had high accuracy, as shown in Figure 4. The area under the ROC curve of the nomogram was 0.905, 95% CI (0.862 - 0.949), P < 0.001, indicating that the constructed nomogram model has good predictive efficacy, as shown in Figure 5.

Fever with thrombocytopenia syndrome (SFTS) is an infectious disease that occurs frequently in the spring and summer months and is caused by a novel Bunyavirus (SFTSV). The patient's main presentations include fever, decreased white blood cell and platelet counts, decreased lymphocyte counts, enlarged lymph nodes, malaise, and gastrointestinal symptoms. In most cases, the prognosis is favorable. However, the condition of critically ill patients deteriorates rapidly and may lead to shock, respiratory failure, disseminated intravascular coagulation (DIC), multi-organ failure, and death [1–4]. Early assessment of disease prognosis and timely emergency measures are crucial.

Yu Dong et al [7] studied the epidemiologic distribution, clinical characteristics, and prognostic risk factors of SFTS patients and concluded that the risk factors for poor prognosis in patients with severe SFTS were advanced age, history of multiple underlying medical conditions, high viral load, severely reduced platelet count, and delayed treatment; Gai ZT et al [8] found that 7–13 days after the onset of the disease is an important period in determining disease regression and that high viral load is one of the risk factors for case fatality. High viral load is one of the risk factors for case fatality. The possible mechanism of these two factors, advanced age, and high viral load is that, on the one hand, as people age, the immune system of the elderly gradually ages, leading to an increase in systemic inflammation. After the decline of immune function, when the virus enters the body of elderly patients, the immune system reacts slowly or incorrectly, promoting the massive replication of the virus, inducing severe viremia and a storm of inflammatory factors, leading to immune damage of tissues and organs as well as multi-organ dysfunction [9–10]. Meanwhile, SFTSV is pansophilic and can affect multiple organs and tissues, leading to severe complications. In addition, the elderly are often most likely to develop severe complications and ultimately comorbid infections with other pathogens, such as bacteria and fungi, as severe viral infections lead to a further decline in the patient's immunity [11]. In the present study, advanced age and high viral load were independent risk factors for death in SFTS patients, which is consistent with previous studies.

Under normal conditions, procalcitoninogen (PCT) is produced by thyroid C cells and remains at low levels in plasma (< 0.5 ng/mL). Endotoxins produced by bacterial infections and cytokines produced by the body's inflammatory response are the main causes of induced PCT production and elevation [12]. In the present study, the median level of PCT was relatively low in both groups, and the median CRP was also low, indicating that endotoxin produced by bacterial infection is less likely and that inflammatory factors that cause inflammatory responses in the body may be the main cause of inducing PCT production and elevation. Some studies [13] have shown that virus-induced systemic inflammatory response syndrome can also increase PCT levels, but the elevation of PCT is not as high as in bacterial infections, and PCT can also be used for prognostic assessment of viral infections. In this study, PCT was found to be a risk factor for death in patients with SFTS, which is consistent with previous findings.

In a study by Su Yeon Kang et al [14], increased interleukin − 10 (IL − 10) was shown to be a risk factor for death in patients with severe fever with thrombocytopenia syndrome (SFTS). A study by Ming Huang et al [15] further indicated that high levels of procalcitoninogen (PCT), interleukin − 6 (IL − 6), IL − 10, tumor Tumor necrosis factor-alpha (TNF-alpha), activated partial thromboplastin time (APTT), prothrombin time (TT), and the occurrence of hemophagocytic lymphohistiocytosis were all indicators of poor prognosis in SFTS. A study by Hou H et al [16] also confirmed that calcitonin and interleukin-10 were independent risk factors for predicting the prognosis of poor prognosis in SFTS patients. In addition, a study by Zhiquan He et al [17] indicated that serum levels of interleukin-6 (IL-6), IL-10, gamma-interferon-inducible protein-10 (IP-10), and monocyte chemotactic protein-1 (MCP-1) could reflect the severity of the disease. These findings provide important biomarkers for clinical management and prognostic assessment of SFTS patients.

In this study, we showed that the early warning model included four indicators: age, viral load, PCT, and IL-10, which is consistent with the early warning indicators reported in previous studies [15–16]. The calibration curves of the prognostic nomogram model for patients with fever with thrombocytopenia syndrome (SFTS) can be concluded that the model has a good predictive and discriminative effect, and the predictive effect of the model has a high degree of overlap with the actual prognosis of the patients. Meanwhile, the nomogram constructed in this study uses high-scoring and low-scoring line segments to transform complex regression equations into simple and intuitive line segments, which improves the practical value [18].

In addition, this paper is only a single-center retrospective study, and prospective studies such as multicenter studies and case-cohort studies have not been conducted. Only the first laboratory indicators at the time of patient admission were collected, and the experimental data were not continuously and dynamically monitored for changes. The sample size selected was relatively small and there were limitations in that the severity of the cases and the prognosis of the different age groups were not stratified and analyzed. In addition, this study did not use actual data to validate the predictive effect, and no prospective study was conducted to validate the predictive effect of the model. In future studies, it is expected that more indicators affecting disease changes will be found, large-sample, multicenter prospective studies will be conducted, and the accuracy and stability of the model will be continuously improved.

In conclusion, by retrospectively analyzing the clinical data of 207 patients with novel Bunyaviruses, this study constructed a prognostic risk warning model for patients with novel Bunyaviruses and concluded that high age, high viral nucleic acid load, high procalcitoninogen (PCT), and high interleukin-10 (IL-10) were the independent risk factors for death in SFTS patients. The model has a good predictive effect and clinical utility and provides a specific basis for clinical staff to judge the prognosis of patients. In clinical practice, special attention should be paid to the levels of viral load and inflammatory indicators in elderly SFTS patients for early intervention to prevent adverse outcomes.

Acknowledgements

XX, YQD made significant contributions to the intellectual content of this paper and were involved in drafting the manuscript.SL and YQD contributed to the collection and analysis of experimental data, as well as critically revising the manuscript. All authors have assumed responsibility for the entire content of this manuscript and have given their approval for its submission.

We would like to extend our sincere appreciation to Professor Cheng Liming and the entire staff of the Department of Laboratory Medicine at Tongji Hospital for their invaluable assistance.

Ethical approval and Informed consent:

The study has adhered to all pertinent national regulations and institutional policies, obtained approval from the local ethics committee, and received a waiver of informed consent.

Competing interests:

All authors state no conflict of interest.

Data availability statement

The datasets supporting the findings of this study are obtainable from the corresponding author upon reasonable request.

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Table 1 General data of patients in the two groups of SFTS

Variable (units), RI	Survival group(n=133）	Death group(n=74）	t/z/χ²value	P value
Age(years)	61.84±9.72	68.74±8.82	-5.030	<0.001
Sex(male)	53(39.850)	36(48.649)	1.502	0.220
Presence of DM	12(9.023)	9(12.162)	0.514	0.473
Presence of Hypertension	37(27.820)	25(33.784)	0.806	0.369
Presence of CHD	5(3.759)	10(13.514)	6.730	0.009
Presence of AOC	22(16.541)	33(44.595)	19.178	<0.001
Presence of Cerebral infarction	6(4.511)	8(10.811)	2.992	0.084
Smoking	24(18.045)	15(20.270)	0.154	0.695
Alcoholism	18(13.534)	11(14.865)	0.070	0.791
anorexia	6(4.511)	6(8.108)	1.126	0.289
nausea	26(19.549)	23(31.081)	3.500	0.061
vomiting	29(21.805)	25(33.784)	3.539	0.060
abdominal pain	18(13.534)	6(8.108)	1.366	0.243
diarrhea	64(48.120)	43(58.108)	1.899	0.168
swollen lymph nodes	24(18.045)	10(13.514)	0.711	0.399
Body temperature( ℃)	36.900(36.500,37.500)	36.900(36.500,37.900)	-0.346	0.729
Days from onset to admission (d, M (P25,P75)	7.000(5.000,8.000)	7.000(5.000,9.000)	-0.218	0.825

RI, reference interval; n, number; DM, diabetes mellitus; CHD, coronary heart disease; AOC, alteration of consciousness;

Table 2 Comparison of blood routine and inflammation-related indexes between the two groups of patients

Variable (units), RI	Survival group（n=133）	Death group（n=74）	t/z/χ²value	P value
Virus Load(lg copies/mL )	3.009（2.072,3.726）	4.607（3.910,5.686）	-7.682	<0.001
WBC（10⁹/L）	3.530（2.110,5.700）	3.520（2.090,6.220）	-0.459	0.647
NEUT（10⁹ /L）	2.130（1.110,3.900）	2.490（1.300,4.620）	-1.168	0.243
LYM（10⁹/L）	0.740（0.450,1.200）	0.610（0.330,1.010）	1.474	0.141
NLR	3.187（1.543,6.071）	3.740（1.750,9.461）	-1.673	0.095
RBC（10¹² /L）	4.270（3.960,4.650）	4.130（3.790,4.620）	0.827	0.409
HGB (g/L)	127.474±18.336	128.459±20.780	-0.351	0.726
PLT（10⁹/L）	50.000（35.000,69.000）	36.000（27.000,54.000）	3.569	<0.001
PCT（ng/mL）	0.180（0.080,0.360）	0.600（0.250,1.240）	-6.423	<0.001
CRP (mg/L)	2.900（1.400,7.700）	5.600（2.500,19.300）	-3.058	0.002
FER(ug/L)	5442.400（2308.500,12653.000）	19652.000（7597.100,44922.000）	-5.526	<0.001
IL1β（pg/ml）	5.000（5.000,8.000）	7.400（5.000,12.600）	-3.541	<0.001
IL2R（U/ml）	1032.000（770.000,1378.000）	1766.000（1226.000,2280.000）	-6.676	<0.001
IL6（pg/ml）	22.070（9.940,46.080）	85.330（38.160,228.900）	-6.761	<0.001
IL8（pg/ml）	19.000（13.800,31.100）	47.400（28.900,129.000）	-6.179	<0.001
IL10（pg/ml）	24.500（11.000,66.900）	114.000（51.100,199.000）	-6.481	<0.001
TNF（pg/ml）	21.500（15.500,29.300）	43.400（27.100,72.900）	-7.102	<0.001

RI: reference interval; n: number; WBC: White Blood Cell; NEUT: Neutrophil，LYM: Lymphocyte; NLR: Neutrophil-to-Lymphocyte Ratio; RBC: Red Blood Cell; HGB: Hemoglobin; PLT: Platelet; PCT: Procalcitonin; CRP: C-reactive Protein; FER: Ferritin; IL1β: Interleukin-1β; IL2R: Interleukin-2 Receptor; IL6: Interleukin-6; IL8: Interleukin-8; IL10: Interleukin-10; TNF: Tumor Necrosis Factor

Table 3 Multifactorial Binary Logistic Regression Analysis of Prognosis in SFTS Patients

Variable	Regression coefficient	Standard error	Wald χ2 value	P value	OR value(95%CI)
(Intercept)	-15.327	2.39	-6.414	<0.001	0.0
Virus load（X1）	0.982	0.222	4.425	<0.001	2.669（1.764-4.239）
AGE（X2）	0.15	0.029	5.177	<0.001	1.162（1.102-1.236）
PCT（X3）	1.015	0.298	3.408	0.001	2.758（1.586-5.231）
IL10（X4）	0.005	0.002	2.352	0.019	1.005（1.001-1.009）

OR, odds ratio; CI, confidence interval; PCT, Procalcitonin; IL-10: Interleukin-10

Table 4 AUC of ROC in subjects

Variable	AUC (95%CI)	Cut-Off value	Sensitivity (%)	Specificity (%)	Accuracy (%)
AGE	0.706 (0.628-0.779)	64.000	78.4%	54.9%	63.29%
Virus load	0.822 (0.756-0.883)	4.068	73.0%	82.0%	78.74%
PCT	0.770 (0.704-0.831)	0.510	56.8%	83.5%	73.91%
IL10	0.772 (0.705-0.832)	66.600	68.9%	74.4%	72.46%
Nomogram model	0.905 (0.862-0.949)	0.347	83.8%	85.7%	84.50%

AUC, area under the curve; CI, confidence interval; PCT, Procalcitonin; IL-10: Interleukin-10

No competing interests reported.

Construction of a Novel Prognostic Risk Warning Model for Bunyavirus Patients Based on Inflammatory Indicators

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Abstract

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Introduction

Materials and Methods

Results

Discussion

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