Retrospective analysis of severe fever with thrombocytopenia syndrome and construction of a nomogram prediction model for mortality risk factors

doi:10.21203/rs.3.rs-4829480/v1

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Retrospective analysis of severe fever with thrombocytopenia syndrome and construction of a nomogram prediction model for mortality risk factors

https://doi.org/10.21203/rs.3.rs-4829480/v1

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Objective: To investigate high mortality risk factors in severe fever with thrombocytopenia syndrome (STFS) and to create a nomogram model for personalized prediction.

Methods: 523 SFTS patients admitted to the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, between January 2020 and December 2023 were retrospectively analyzed. 75 cases were classified in the death group (D group) and 448 cases in the survival group (S group). A predictive nomogram model was developed base on the independent risk factors that were stepwise screened through univariate analysis, least absolute shrinkage and selection operator (LASSO), and multivariate logistic regression analysis.

Results: Based on stepwise variable screening by univariate analysis, LASSO, and multivariate logistic regression. Age(OR=1.06; 95%CI, 1.03–1.10; P＜0.001), hemorrhagic symptoms (OR=3.39; 95%CI; 1.31–8.78; P=0.012), neurologic symptoms (OR=4.89; 95%CI, 2.72–8.77; P＜0.001), platelet (OR=0.99; 95%CI, 0.98-0.99; P=0 .045), PT (OR=1.32; 95%CI;1.11-1.56; P=0.001), APTT (OR=1.02; 95%CI, 1.01–1.03; P=0.007) and viral load ≥107copies/ml(OR=2.66; 95%CI; 1.36 – 5.20; P =0.004) were independent mortality risk factors in patients with SFTS. The area under the curve (AUC) showed excellent predictive power (AUC = 0.87, 95% CI 0.832-0.909). Calibration curves showed the accuracy of the nomograms assessed. Decision curve analysis (DCA) results showed a greater net benefit when the threshold probability of patient death was between 0.02 and 0.75.

Conclusions: A nomogram model consisting of seven risk factors was successfully constructed, which can be used to predict STFS mortality risk factors early.

STFS

risk factors

nomogram prediction model

retrospective analysis

Severe fever with thrombocytopenia syndrome (SFTS) is a typical arboviral zoonosis first reported in Henan Province, China, in 200^[1]. This infectious disease has been successively discovered in 25 other provinces in China, as well as in South Korea^[2], Japan^[3], Vietnam^[4], and other countries, and is becoming widely spread. The causative agent is the SFTS virus, newly named Dabie bandavirus in 2020, which belongs to the genus Phebovirus family Phenuiviridae^[5]. The SFTS virus is transmitted mainly through exposure to and bites from infected ticks. Human-to-human transmission through inhalation of virus-containing aerosols from the blood and body fluids of confirmed cases may be an additional route^[6]. SFTS virus ribonucleic acid (RNA) has been detected in several tick species, including Haemaphysalis longicornis, Amblyomma testudinarium, Ixodes nipponensis, Amblyomma testudinarium and Haemaphysalis flava, and Haemaphysalis longicornis may be the primary vector of SFTSV^[7].

Clinical manifestations of SFTS include fever, vomiting, diarrhea, abdominal pain, cough, malaise, and enlarged lymph nodes, with severe cases exhibiting heart failure, acute respiratory distress syndrome, severe acute pancreatitis, acute kidney injury, disseminated intravascular coagulation, and other multi-organ dysfunction and neurological involvement. With a high mortality rate ranging from 5.2 to 29.8 percent in various studies^{[8, 9]}, SFTS poses a significant threat to public health. It's been listed as one of the top 10 infectious diseases t requiring urgent investigation by the World Health Organization^{[10, 11]}. Currently, no effective antiviral medications or vaccines are available despite the urgent need for them. Therefore, early identification of motility risk factors in STFS and providing targeted intervention strategies can reduce the risk of death and are of great clinical importance. This study retrospectively analyzed the epidemiological characteristics, clinical features, and laboratory indices of 523 confirmed cases from January 2020 to December 2023. We aim to gain a more specific understanding of the epidemiological characteristics and risk factors for mortality in patients with SFTS and to establish a nomogram model for predicting the poor prognosis of STFS, thus providing a reference basis for effective preventive control and clinical diagnosis and treatment of the disease.

1.1Patients and Case Definition

523 patients with SFTS admitted to the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, between January 2020 and December 2023 were enrolled in this study. Those patients who were missing important data were excluded from this study. The diagnosis of SFTS was based on the following criteria: acute fever, thrombocytopenia, and detection of Pathogenetic evidence for SFTS virus. Confirmation of the diagnosis requires one of the pathogenic evidence: ①.positive nucleic acid test of SFTS virus; ②.positive immunoglobulin M (IgM) antibody of SFTS virus; ③. STFS antibody in serum increased 4-fold or more with at least two weeks between collections.

1.2 Data collection

STFS patients were followed up by telephone within one month after discharge. Patients who were missing important data were excluded from this study. The study included 523 patients, separated into two groups based on prognosis: 75 in the death group (D Group) and 448 in the survival group (S Group). Patient demographic details, tick bite history, underlying diseases, clinical manifestations (Table 1), and laboratory indicators (Table 2) were collected.

1.3 Statistical Analysis

All data analyses were performed using R ver.4.3.0 (R Foundation). Normal-distributed continuous variables were shown as mean ± standard error, and comparisons between groups were tested by independent samples t-test. Non-normally distributed variables were expressed as M [P25, P75], and differences between groups were made using the Mann-Whitney U test. Categorical variables were expressed as frequencies and ratios. Count data were expressed as frequencies (%) using the χ2 test. To determine the pertinent variables for death in SFTS patients, the least absolute shrinkage and selection operator (LASSO) regression approach was employed. The R software's “glmnet” package was used to plot LASSO regression. Then, a multivariate logistic regression analysis was used to identify the independent mortality risk factors in STFS patients. The R software's “rms” package was used to plot the nomogram model and calibration curve. The “pROC” software program was used to plot the receiver operating characteristic（ROC）curves for nomogram models. The “rmda” package was used to plot the decision curve analysis (DCA) curve of the nomogram model. P < 0.05 was considered statistically significant.

2.1 Demographic and Epidemiological Characteristics

In total, 523 laboratory-confirmed cases were accepted in this study, of which 191(14.3%) died of the condition. Demographic characteristics are shown in table 1. The median age of the patients was 68 years, with an age range from 2.5 to 88 years. The median age in the D group was 65 years, six years younger than that of the S group. Age varied statistically significant（Z=-4.396, p<0.001). Male confirmed cases numbered 241 (46.08%), whereas Confirmed cases in females numbered 282 (53.92%). Of the patients, 278 (42.2%) were farmers. Tick bites were a known history for 62 patients (12.05%). The two groups did not differ significantly regarding sex, occupation, or history of tick bites.

From 2020 to 2023, the annual number of confirmed SFTS cases was 72, 64, 149, and 239(Figure 1). In terms of month, the number of confirmed cases increased gradually starting in March and peaked between May and August before declining further. From December to February, there was almost no occurrence; nevertheless, one patient started the condition in January 2023(Figure 2).

2.2 Clinical Manifestations and Underlying Diseases

The probability of occurrence of the symptoms collected at the time of admission is in the order of fever (97.8%), diarrhea (34.61%), vomiting (23.33%), neuropsychiatric symptoms (NS) (23.71%), cough (13.77%), hemorrhage symptoms (HS) (5.74%), abdominal pain (4.78%), lymph node enlargement (3.25%). The occurrence of NS and HS was considerably higher in the D group compared to the S group (p <0.001). Regarding underlying diseases, diabetes and surgical history were not significantly different between the two groups. However, there were significant differences in tumor history (p=0.012) and Cardio-cerebrovascular disease (CCD) (p=0.016). (Table 1).

2.3 Laboratory Examinations

Laboratory testing showed that the D group had considerably lower platelets, albumin, and fibrinogen levels than the S group (P<0.05). In contrast, the levels of prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer, creatinine, blood urea nitrogen (BUN), potassium, and aspartate aminotransferase (AST) were dramatically elevated (P < 0.05) than those in the S group. The nucleic acid test results for the SFTS virus were negative in 146 patients. The other 377 patients tested positive, including 85 patients with serum virus loads less than 10³ copies/ml, 219 cases with virus loads between 10⁴ copies/ml and 10⁶ copies/ml, and 73 cases with viral loads greater than 10⁷ copies/ml, with a notable difference between the two groups（p<0.001）. (Table 2).

2.4 LASSO-based Screening of variables and Multivariate Logistic Regression Analysis

Univariate analyses screened out many statistically significant variables, but the number of patients in group D was small. The variable of tumor history was excluded because of its limited sample size. The LASSO regression algorithm was used for variable screening (Figure 3). Ultimately, eight variables with nonzero coefficients, including age, platelet value, PT value, APTT value, creatinine value, viral load value≥10⁷ copies/ml, NS, and HS, were kept in the LASSO analysis (Table 3). The best suitable parameter λ value for LASSO was 0.038 when the partial likelihood deviance attained its smallest value.

Multivariate logistic regression analyses were performed with the eight variables found by the LASSO regression serving as independent variables. The ultimate results indicated that seven factors including age(OR=1.06; 95%CI, 1.03–1.10; P＜0.001), HS (OR =3.39; 95%CI, 1.31–8.78; P = 0.012), NS (OR=4.89; 95%CI, 2.72–8.77; P＜0.001), platelet (OR=0.99; 95%CI, 0.98-0.99; P=0 .045), PT (OR =1.32; 95%CI, 1.11-1.56; P=0 .001), APTT (OR=1.02; 95%CI, 1.01–1.03; P = 0.007), and viral load ≥10⁷copies/mL(OR = 2.66; 95%CI, 1.36 – 5.20; P =0 .004) were independent mortality risk factors in patients with SFTS. (Table 4).

2.5 The Development of the Nomogram Model

A predictive nomogram model for SFTS mortality risks was developed based on the seven risk factors obtained from multivariate logistic regression analyses (Figure 4). For each SFTS patient, a higher total score indicated a higher risk of death, according to the nomogram. A score is obtained by drawing a vertical line upward from the position of each independent predictor. The total points are calculated by adding all seven predictors' scores. The Hosmer-Lemshaw test was applied to this model, and p=0.1536 suggests goodness of fitness. The area under the curve (AUC) showed excellent predictive power (AUC = 0.87, 95% CI 0.832–0.909) (Figure 5A). The calibration curve shows the nomogram assessment accuracy (Figure 5B). The decision curve analysis (DCA) curve for the model is displayed in Figure 5C. When the patient's threshold probability of death was between 0.02 and 0.75, the multiple regression model added a greater net benefit to the Decision Curve Analysis curve than the "treat all" or "no treatment" strategies.

Numerous research studies have been conducted on SFTS since it was first reported; however, the pathological mechanism is still unclear. The 14.3% mortality rate in our data is much higher than the 5.2% surveillance rate from China^[12], consistent with Li Hao's study(16.2%)^[13]. Mortality rates vary considerably and may be related to differences in the number of cases, geographical distribution, level of medical care, and statistical calibre. Patients who ultimately die from STFS may have some features suggestive of a poor prognosis in the early stages of the disease. Therefore, assessing the prognostic factors during patient admission is crucial, enabling clinicians to make optimal treatment decisions promptly.

In recent years, this study found a fluctuating upward trend of SFTS cases in the surrounding areas of Nanjing, China. The occurrence of SFTS is seasonal, primarily occurring between April and October. The incidence of the disease is predominantly among middle-aged and elderly farmers. Previous studies have shown that all age groups are susceptible to SFTS infection^[14]. However, similar to previous studies^{[8, 15, 16]}, this work found that the D group was older than the S group, and there was a statistically significant difference between the two groups. Multifactorial analysis also revealed that age was an independent risk factor for the outcome of patient death. Regarding underlying diseases, cardio-cerebrovascular and neuropsychiatric diseases were statistically significant in univariate analyses but excluded in multivariate analyses.

This study shows that 97.13% of the patients were observed to have fever, and other relatively common clinical manifestations included diarrhea, vomiting, abdominal pain, cough, etc. However, no significant differences were found between the two groups in these clinical manifestations, which suggests that it is difficult to assess the prognosis of the patients only by the common clinical manifestations. Among the clinical features we studied, HS and NS were the two mortality risk factors. HS manifests as skin petechiae, gingival bleeding, and epistaxis in patients with mild symptoms and gastrointestinal bleeding and pulmonary bleeding in patients with severe symptoms. NS includes transient disorders of consciousness, such as drowsiness, some of which resolve or disappear as the disease progresses, and in severe cases, disorientation, delirium agitation, disorientation, recurrent grand mal seizures, and even coma. Patients with HS and NS had 3.39 and 4.89 times higher increased risk of death compared to those without these symptoms, respectively. This is similar to previous studies^[17–19]. HS is associated with many factors, such as thrombocytopenia, abnormalities of the coagulation system, damage to vascular endothelial cells caused by infection^[20], and platelet phagocytosis by macrophages^[21]. NS is attributed to STFS-related encephalopathy/encephalitis^]22].

SFTS patients have lower platelet counts, impaired liver, kidney, and cardiac function, and coagulation problems in their serum. Previous studies showed that platelets, AST, AST, lactate dehydrogenase (LDH), Creatine kinase (CK), APTT, BUN, and creatinine were risk factors associated with the death of SFTS patients^{[18, 23, 24]}. The present study showed statistical differences between the two groups in univariate analysis in platelets, albumin, PT, APTT, creatinine, BUN, and AST. However, in multivariate analysis, statistical differences existed only in platelets, PT, and APTT, suggesting that these three laboratory markers are independent risk factors for the outcome of SFTS death. In this study, the D and S groups showed different levels of thrombocytopenia, PT, and APTT prolongation, with more remarkable changes in the D group. A survey of dynamic monitoring of laboratory markers in patients with STFS found that PT, APTT, and platelet recovery gradually in the surviving group, but patients in the death group had further worsened PT, APTT, and platelet levels with the deterioration of the disease^[25]. Based on an in vitro study, the SFTS virus attaches itself to mouse platelets and encourages primary mouse macrophages to phagocytose the platelets^[26]. Additionally, platelets are crucial for maintaining the integrity of the endothelium cell barrier^[27]. Vascular endothelial dysfunction in patients with STFS activates endogenous coagulation, causing abnormal coagulation and consuming large amounts of platelets, leading to thrombocytopenia and disseminated intravascular coagulation(DIC) ^{[28, 29]}.

SFTS viral replication is reflected in viral load, one of the laboratory diagnostic markers of STFS. This study demonstrated that patients in the D group had higher viral loads, and SFTS viral load ≥ 107 copies/ml was an independent risk factor for death. Similarly, previous studies have shown that patients with high viral load are more likely to develop serious infections ^[30–32]. According to another prospective study, dynamic changes in viral load positively correlated with disease severity and serum PLT, WBC, LDH, AST, and CK levels^[33]. Continuous monitoring of changes in viral load may help determine the prognosis of SFTS. Serum viral load in SFTS patients is associated with a host cytokine storm induced by IL-1RA, IL-6, IL-10, G-CSF, IP-10, and MCP-134, and the exact mechanism needs to be further investigated^[34]. Previous studies have reported that some cytokines^{[24, 35–37]}, as well as lymphocyte subsets^{[31, 36]}and pancreatic enzymes^{[24, 38]} are risk factors. This study did not consider these factors because of the lack of data.

Nomogram provides a comprehensive, at-a-glance, and easy-to-use data visualization tool that accurately calculates the probability of an individual patient's specific outcome. In the present study, we constructed a novel nomogram model on the poor prognosis of STFS patients with seven high-risk screened factors. We validated the model using ROC, calibration, and decision curves, suggesting that the nomogram model has satisfactory predictive ability. This model identifies high-risk STfFS patients at admission, allowing for active interventions and better patient outcomes and survival.

This study has several limitations. First, our nomogram model was based on retrospective data collection from a single center. Secondly, there were relatively few samples in Group D, so we could not divide the data into validation and training sets to verify the internal validity of the model. Thirdly, this study incorporated viral load into the model, but nucleic acid tests cannot be performed in primary hospitals.

Age, NS, HS, viral load ≥10⁷copies/ml, platelet, PT, and APTT were independent risk factors for mortality outcomes in SFTS patients at admission. We constructed a risk-predictive nomogram based on LASSO regression and logistic regression analysis. This visualized prediction model provides an effective tool to determine patients' poor prognosis after admission.

LASSO Regression

least absolute shrinkage and selection operator regression

Hemorrhagic symptoms

Neuropsychiatric symptoms

CCD

Cardio-cerebrovascular disease

prothrombin time

APTT

activated partial thromboplastin time

AST

aspartate aminotransferase

ALT

aspartate aminotransferase

BUN

blood urea nitrogen.

Ethics statement

This study was conducted according to the guidelines of the Declaration of Helsinki, and approved and authorized by the ethics committee of the Second Hospital of Nanjing, Nanjing University of Chinese Medicine(2024-LS-ky031). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author Contributions

G.Ch., Data curation, Formal analysis, original draft, Writing – review & editing, Software. Y.D., Data curation, Formal analysis, Writing – original draft, Writing. Y. L., Formal analysis, Methodology, review & editing. A.C., Writing– review & editing. J.W. Methodology, Writing– review & editing. J.W., Methodology, Formal analysis. X.M., Methodology, Software. W.Q., Software. Z. H., Data curation. Y.Zh., Resources, Writing – review & editing. M.T., Conceptualization, Project administration, Supervision, Writing – review & editing. C.Y., Conceptualization, Project administration, Supervision, Writing – review & editing. The article has been read and approved by all writers.

Funding

This work was funded by the Key Scientific Research Project of Jiangsu Commission of health (ZDB2020036), the Innovation and Entrepreneurship Talent Project of Jiangsu (0313505), the High-level Talents Research Initiation Project of Nanjing Second Hospital (0313504), the National Major Research Program Incubation Project (92169106).

Data Availability Statement

Patient data comes from the hospital's electronic medical record system, so the data is not publicly available.

Additional Information

Competing interests: The authors declare no conflict of interest.

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Tables 1 to 4 are available in the Supplementary Files section

No competing interests reported.

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Retrospective analysis of severe fever with thrombocytopenia syndrome and construction of a nomogram prediction model for mortality risk factors

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