
==== Front
BMC Infect Dis
BMC Infect Dis
BMC Infectious Diseases
1471-2334
BioMed Central London

39272027
9898
10.1186/s12879-024-09898-6
Research
Establishment and validation of a clinical risk scoring model to predict fatal risk in SFTS hospitalized patients
Zhong Fang 1
Lin Xiaoling 2
Zheng Chengxi 1
Tang Shuhan 1
Yin Yi 1
Wang Kai 1
Dai Zhixiang 1
Hu Zhiliang huzhiliang@njucm.edu.cn

234
Peng Zhihang zhihangpeng@njmu.edu.cn

156
1 https://ror.org/059gcgy73 grid.89957.3a 0000 0000 9255 8984 School of Public Health, Nanjing Medical University, Nanjing, China
2 grid.89957.3a 0000 0000 9255 8984 Department of Infectious Disease, the Second Hospital of Nanjing, School of Public Health, Nanjing Medical University, Nanjing, China
3 https://ror.org/04523zj19 grid.410745.3 0000 0004 1765 1045 Nanjing hospital, Nanjing University of Chinese Medicine, Nanjing, China
4 https://ror.org/059gcgy73 grid.89957.3a 0000 0000 9255 8984 Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
5 https://ror.org/04wktzw65 grid.198530.6 0000 0000 8803 2373 National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
6 https://ror.org/04wktzw65 grid.198530.6 0000 0000 8803 2373 Division of Infectious disease, Chinese Center for Disease Control and Prevention, Beijing, China
13 9 2024
13 9 2024
2024
24 97511 6 2024
9 9 2024
© The Author(s) 2024
2024
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Background

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infection with a high case fatality rate. Significant gaps remain in studies analyzing the clinical characteristics of fatal cases.

Methods

From January 2017 to June 2023, 427 SFTS cases were included in this study. A total of 67 variables about their demographic, clinical, and laboratory data were collected. Univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) method was used to screen predictors from the cohort. Multivariate logistic regression was used to identify independent predictors and nomograms were developed. Calibration, decision curves and area under the curve (AUC) were used to assess model performance.

Results

The multivariate logistic regression analysis screened out the four most significant factors, including age > 70 years (p = 0.001, OR = 2.516, 95% CI 1.452–4.360), elevated serum PT (p < 0.001, OR = 1.383, 95% CI 1.143–1.673), high viral load (p < 0. 001, OR = 1.496, 95% CI 1.290–1.735) and high level of serum urea (> 8.0 μmol/L) (p < 0.001, OR = 4.433, 95% CI 1.888–10.409). The AUC of the nomogram based on these four factors was 0.813 (95% CI, 0.758–0.868). The bootstrap resampling internal validation model performed well, and decision curve analysis indicated a high net benefit.

Conclusions

The nomogram based on age, elevated PT, high serum urea level, and high viral load can be used to help early identification of SFTS patients at risk of fatality.

Keywords

SFTS
Mortality
Prediction model
Logistic regression
Nomogram
http://dx.doi.org/10.13039/501100012166 National Key Research and Development Program of China 2023YFC2306004 National Key Research and Development Program of China,China2022YFC2304000 National Natural Science Foundation of China82320108018 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Severe fever with thrombocytopenia syndrome (SFTS), a serious acute illness caused by the SFTS virus (SFTSV), is characterized by fever, fatigue, thrombocytopenia, leukopenia, and gastrointestinal and central nervous system symptoms [1]. SFTSV can be transmitted through tick bites, aerosols, contact with host animals, or the blood and body fluids of SFTS cases [2–4]. SFTS was first reported in rural areas of Hubei and Henan provinces in China in 2009 [5] and has since been reported in several other Asian countries, including South Korea, Japan, Vietnam, and Myanmar, with a fatality rate ranging from 12–50% [6–8].

The main clinical manifestations of SFTS include fever, thrombocytopenia, leukopenia and gastrointestinal symptoms, and in severe cases, patients may present with multipleorgan failure (MOF) symptoms such as shock, respiratory failure, disseminated intravascular coagulation (DIC) and death [6, 9]. Therefore, early identification of early risk factors for disease associated with fatal outcomes in SFTS is critical. An early Meta-analysis showed that risk factors for severe disease in SFTS included bleeding tendencies, central nervous system manifestations, elevated serum enzymes, and high viral load[10]. Several single-center studies have subsequently examined early risk factors for death in SFTS, including factors such as viral load, platelet count, albumin, coagulation, lactate dehydrogenase (LDH), creatine phosphokinase (CK), creatinine, urea, monocyte count, and lymphocyte count [11–16].

Most single-center retrospective studies are small and have small sample sizes, resulting in insufficient evidence to describe clinical characteristics and identify risk factors, so comprehensive new models incorporating larger samples are yet to be developed. This study recruited a larger sample based on previous studies to further identify early risk factors for fatal SFTS using demographics, clinical presentation, laboratory indicators, and a virological indicator to help physicians proactively standardize the treatment of SFTS.

Materials and methods

Study design and participants

Between January 2017 and June 2023, 484 patients who were hospitalized at the Second Hospital of Nanjing and diagnosed with SFTS were included. Diagnostic criteria were as follows: (1) acute fever (temperature > 38.0 °C) with thrombocytopenia (platelet count < 100 × 109/L); (2) positive serum nucleic acid test and/or virus-specific IgM antibody (SFTSV) positive. The exclusion criteria were as follows: (1) age under 18 years; (2) laboratory-confirmed infections by other pathogens, such as hantavirus, dengue virus, and rickettsia; and (3) hospitalization of less than 24 h. 427 patients with SFTS were finally included. Informed consent was not required as the study was retrospective and analyzed anonymously. This study was approved by the Ethics Committee of Nanjing Medical University, China (Ethics No. (2020) 211) and was carried out according to the Helsinki II Declaration.

Data collection and definitions

The study collected data on 427 patients from the electronic medical records. A total of 67 factors were recorded, including demographic information, comorbidities, clinical and laboratory indicators, and outcomes. The clinical and laboratory indicators were recorded within three days after admission for SFTS. Upon admission, hematologic and inflammatory indexes were calculated using the following formulas: SIRI = neutrophil * monocyte/lymphocyte counts; PLR = platelet/lymphocyte count; MLR = monocyte/lymphocyte count; NLR = neutrophil/lymphocyte count. Serum urea level was defined as elevated based on > 8.0 μmol/L. All the data were entered into Excel files and reviewed by two trained researchers. The study endpoint was defined as death or survival at 28 days of admission. Critically ill patients who discontinued treatment and were automatically discharged from the hospital were contacted by phone to confirm their clinical outcomes.

Statistical analysis

Sixty-seven variables were obtained from the hospital data collected at the time of admission, including clinical presentation, demographic variables, epidemiology, laboratory results, and medical history. During the data processing, the maximum percentage of missing values for any variable was 20%. A new data set was created by replacing all missing values with estimated values using the mean imputation method. To minimize potential covariance and overfitting of variables from the same participant, we screened variables using least absolute shrinkage and selection operator (LASSO) regression. In constructing the model, model development was performed with all patient data (avoiding data segmentation), and internal validation of model discrimination and calibration was performed using bootstrap with 1000 resamples [17]. Significant variables from univariate analyses (p < 0.05) were then incorporated into multivariate logistic models when constructing the model to identify appropriate variables with the highest discriminant power. These variables were eventually introduced into the final nomogram. Model prediction accuracy and consistency were assessed using calibration curves, run characteristic curves (ROC), and area under the ROC curve (AUC), and a decision curve analysis (DCA) was used to demonstrate the overall effectiveness of the model.

In descriptive statistical methods, continuous variables are presented as medians and ranges, while categorical variables are presented as frequencies and proportions. Comparisons of categorical variables were made using the chi-square test or Fisher’s exact test, and comparisons of continuous variables were made using the independent samples t-test or the Mann-Whitney U-test. All data were statistically evaluated with R software (version 4.2.1) using the rms, glmnet, rmda, pROC, and caret packages (R foundation for statistical computing, Vienna, Austria). All tests were two-tailed, and p < 0.05 was considered statistically significant. The power value (1-β) in power analysis generally needs to be greater than 80%, and the target for this study was more than 90%.

Results

Demographics and clinical characteristics of the study population

Between January 2017 and June 2023, 427 out of 484 SFTS inpatients met the inclusion criteria, of which 86 patients (40 males and 46 females) died. The median age of the patients was 67.0 (58.0-72.5) years, and the median time from symptom onset to admission was 5.0 (4.0–7.0) days. There were significant differences in some of the symptoms such as consciousness disturbance, breath rate, and muscle pain between the patients in the survival and fatal group (Table 1). All independent variables listed in Table 1 were used in LASSO and univariate logistic regression analyses.

Table 1 Comparison of clinical characteristics of patients with SFTS between the survival and fatal groups

Variable	Total (n = 427)	Nonfatal (n = 341)	Fatal (n = 86)	p-value	
Male n (%)	189 (44.3)	149 (43.7)	40 (46.5)	0.727	
Age (years)	67.00 (58.00, 72.50)	66.00 (57.00, 71.00)	71.00 (66.00, 76.00)	< 0.001	
Time from onset to admission (days)	5.00 (4.00, 7.00)	5.00 (4.00, 7.00)	5.00 (4.00, 7.00)	0.200	
Respiratory rate (breaths/min)	20.00 (18.00, 20.00)	20.00 (18.00, 20.00)	20.00 (20.00, 21.00)	0.001	
Pulse (beats/min)	82.00 (73.00, 90.00)	80.00 (72.00, 90.00)	84.50 (76.00, 90.00)	0.097	
Systolic blood pressure (mmHg)	118.00 (105.00, 130.00)	117.95 (105.00, 130.00)	121.50 (104.00, 130.75)	0.464	
Diastolic blood pressure (mmHg)	70.00 (64.00, 78.00)	70.00 (64.00, 77.00)	70.50 (63.25, 79.75)	0.552	
Smoking n (%)	47 (11.0)	36 (10.6)	11 (12.8)	0.690	
Alcohol n (%)	25 (5.9)	18 (5.3)	7 (8.1)	0.452	
Operation n (%)	75 (17.6)	60 (17.6)	15 (17.4)	1.000	
Hypertension n (%)	122 (28.6)	90 (26.4)	32 (37.2)	0.064	
Diabetes n (%)	37 (8.7)	29 (8.5)	8 (9.3)	0.984	
Epidemiology n (%)				0.267	
History of farming	135 (31.6)	101 (29.6)	34 (39.5)		
Contacted confirmed patients	13 (3.0)	11 (3.2)	2 (2.3)		
Contacted with companion animals	7 (1.6)	7 (2.1)	0 (0.0)		
Be bitten by ticks	85 (19.9)	67 (19.6)	18 (20.9)		
Clinical manifestation n (%)					
Fever	421 (98.6)	335 (98.2)	86 (100.0)	0.468	
Tired	397 (93.0)	316 (92.7)	81 (94.2)	0.798	
Anorexia	386 (90.4)	305 (89.4)	81 (94.2)	0.259	
Vomit	151 (35.4)	112 (32.8)	39 (45.3)	0.041	
Muscle pain	78 (18.3)	70 (20.5)	8 (9.3)	0.024	
Diarrhea	167 (39.1)	125 (36.7)	42 (48.8)	0.052	
Dizzy	80 (18.7)	62 (18.2)	18 (20.9)	0.668	
Headache	39 (9.1)	30 (8.8)	9 (10.5)	0.787	
Rash	5 (1.2)	5 (1.5)	0 (0.0)	0.57	
Lymph	17 (4.0)	11 (3.2)	6 (7.0)	0.200	
Laboratory tests					
WBC (×109/L)	3.19 (2.06, 5.81)	3.46 (2.11, 6.00)	2.69 (1.83, 4.65)	0.050	
NEU (×109/L)	2.19 (1.21, 4.46)	2.36 (1.21, 4.53)	1.92 (1.22, 4.15)	0.354	
LYM (×109/L)	0.62 (0.42, 1.02)	0.64 (0.47, 1.04)	0.49 (0.32, 0.72)	< 0.001	
MON (×109/L)	0.13 (0.07, 0.30)	0.16 (0.08, 0.33)	0.08 (0.05, 0.16)	< 0.001	
NEUP (%)	73.00 (60.25, 84.60)	72.20 (57.70, 84.40)	78.05 (66.05, 85.42)	0.032	
LYMP (%)	21.10 (11.70, 32.10)	21.60 (11.90, 33.00)	18.05 (10.67, 28.08)	0.085	
MONP (%)	4.60 (2.40, 7.80)	4.90 (2.60, 8.70)	3.75 (1.63, 5.60)	< 0.001	
RBC (×1012/L)	4.26 (3.91, 4.64)	4.26 (3.90, 4.60)	4.30 (3.94, 4.87)	0.173	
HB (g/L)	130.00 (119.00, 143.00)	130.00 (119.00, 142.00)	135.00 (120.25, 147.75)	0.138	
HCT (%)	38.40 (35.00, 41.65)	37.90 (34.80, 41.40)	39.20 (35.45, 43.90)	0.116	
MCV (fL)	89.50 (87.05, 92.30)	89.50 (87.20, 92.30)	89.55 (86.65, 92.15)	0.750	
MCH (pg)	30.60 (29.55, 31.50)	30.70 (29.50, 31.50)	30.50 (29.60, 31.37)	0.675	
MCHC (g/L)	341.00 (333.00, 349.00)	342.00 (334.00, 349.00)	341.00 (332.00, 348.75)	0.840	
RDW (%)	13.10 (12.65, 13.60)	13.00 (12.60, 13.60)	13.20 (12.83, 13.90)	0.019	
PLT (×109/L)	50.00 (36.00, 68.00)	54.00 (37.00, 71.00)	40.50 (28.25, 53.00)	< 0.001	
Hs-CRP (mg/L)	5.41 (2.20, 10.50)	5.07 (2.18, 10.50)	6.46 (2.53, 10.50)	0.078	
CRP (mg/L)	9.00 (9.00, 11.00)	9.00 (9.00, 11.00)	9.00 (9.00, 11.00)	0.191	
ALB (g/L)	34.60 (31.70, 37.65)	35.00 (32.10, 37.90)	33.40 (30.02, 36.62)	0.003	
UA (μmol/L)	259.00 (207.00, 342.00)	251.00 (199.00, 328.00)	320.50 (238.00, 413.75)	< 0.001	
TP (g/L)	60.70 (56.30, 64.75)	60.70 (56.30, 64.70)	61.30 (56.50, 65.05)	0.716	
Urea (μmol/L)	5.59 (3.98, 7.30)	5.24 (3.80, 6.95)	6.96 (5.16, 11.27)	< 0.001	
Na (mmol/L)	135.00 (132.00, 137.90)	135.00 (132.00, 137.90)	135.00 (132.00, 137.90)	0.976	
K (mmol/L)	3.70 (3.36, 4.00)	3.67 (3.34, 3.95)	3.80 (3.51, 4.14)	0.009	
CL (mmol/L)	101.90 (98.60, 104.35)	101.70 (98.60, 104.30)	102.30 (98.28, 104.47)	0.776	
ALP (U/L)	65.00 (51.00, 82.00)	64.00 (50.00, 81.00)	68.00 (55.92, 84.75)	0.152	
ALT (U/L)	56.80 (35.90, 95.15)	52.00 (33.50, 84.30)	89.95 (47.60, 148.02)	< 0.001	
AST (U/L)	127.20 (72.95, 242.50)	111.00 (68.10, 202.60)	249.90 (145.85, 550.15)	< 0.001	
CK (U/L)	496.00 (195.50, 872.25)	395.00 (162.00, 872.25)	872.25 (337.00, 1615.25)	< 0.001	
GGT (U/L)	34.40 (21.65, 68.35)	33.00 (20.90, 61.10)	42.50 (23.60, 72.90)	0.084	
LDH (U/L)	590.00 (381.00, 935.00)	537.00 (369.00, 878.00)	927.50 (564.75, 1648.75)	< 0.001	
GLB (g/L)	25.40 (22.25, 28.80)	25.20 (21.90, 28.10)	26.95 (24.13, 30.95)	0.002	
IBIL (μmol/L)	4.60 (2.80, 6.25)	4.60 (2.80, 6.40)	4.45 (2.70, 6.00)	0.681	
TT (s)	20.90 (17.55, 26.65)	20.50 (17.60, 25.40)	23.05 (17.52, 33.35)	0.017	
PT (s)	11.10 (10.30, 11.90)	11.00 (10.20, 11.60)	11.70 (10.93, 12.78)	< 0.001	
APTT (s)	41.10 (35.45, 47.45)	40.40 (35.00, 46.80)	49.20 (39.57, 61.67)	< 0.001	
FIB (g/L)	2.20 (1.88, 2.50)	2.25 (1.90, 2.54)	2.00 (1.67, 2.34)	< 0.001	
TBIL (μmol/L)	8.40 (6.15, 11.50)	8.30 (6.00, 11.50)	8.80 (6.82, 11.88)	0.364	
DBIL (μmol/L)	3.80 (2.90, 5.23)	3.70 (2.80, 5.23)	4.15 (3.00, 5.40)	0.100	
CRE (μmol/L)	75.00 (61.00, 92.00)	73.00 (59.10, 86.16)	90.25 (70.62, 120.80)	< 0.001	
Log-transformed viral load (copies/ml)	5.92 (3.56, 7.70)	5.58 (3.08, 6.92)	7.61 (6.51, 7.72)	< 0.001	
SIRI	0.48 (0.22, 1.13)	0.57 (0.24, 1.15)	0.28 (0.14, 0.74)	0.001	
PLR	75.00 (46.90, 127.72)	73.97 (46.84, 127.08)	83.10 (47.79, 155.18)	0.403	
MLR	0.22 (0.14, 0.36)	0.23 (0.15, 0.37)	0.17 (0.11, 0.31)	0.001	
NLR	3.43 (1.88, 7.21)	3.33 (1.72, 7.09)	4.36 (2.33, 8.13)	0.061	
Note: Continuous variable data are presented as median (IQR); Categorical variable data are presented in frequency (%)

Abbreviations: SFTS: severe fever with thrombocytopenia syndrome; WBC: white blood cell; NEU: neutrophil; LYM: lymphocyte; MON: monocyte; NEUP: neutrophil percentage; LYMP: lymphocyte percentage; MONP: monocyte percentage; RBC: red blood cell; HB: hemoglobin; HCT: hematocrit; MCV: mean corpusular volume; MCH: mean corpusular hemoglobin; MCHC: mean corpusular hemoglobin concerntration; RDW: red blood cell volume distribution width; PLT: platelet count; Hs-CRP: hypersensitive C-reactive protein; CRP: C-reactive protein; ALB: albumin; UA: uric acid; TP: total protein; Na: sodium; K: kalium; CL: chloride; ALP: alkaline phosphatase; ALT: alanine aminotransaminase; AST: aspartate aminotransferase; CK: creatine phosphokinase; GGT: γ-glutamyl transferase; LDH: lactate dehydrogenase; GLB: globulin; IBIL: indirect bilirubin; TT: thrombin time; PT: prothrombin time; APTT: activated partial thromboplastin time; FIB: fibrinogen; TBIL: total bilirubin; DBIL: direct bilirubin; CRE: creatinine; SIRI: Systemic inflammation response index; PLR: platelet-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; NLR: neutrophil-to-lymphocyte ratio; IQR: interquartile range

Establishment of the nomogram

All patients were used in the construction of the model based on clinical data obtained within three days of admission. The 67 variables measured at admission were first included into the LASSO regression, and of these variables, 17 were considered significant predictors of the risk of fatal SFTS (Fig. 1), including age > 70 years, time from onset to admission, vomiting, diarrhea, rash, lymph node enlargement, lymphocyte count, percentage of monocyte, uric acid, serum urea level, aspartate transaminase (AST), LDH, globulin, prothrombin time (PT), activated partial thromboplastin time (APTT), creatinine, log-transformed viral load (lg vl), and PLR. Univariate logistic regression showed that 27 of the 67 variables were significantly different (p < 0.05) between the survival and death groups, including age > 70 years, five clinical signs: pulse, history of hypertension, vomiting, myalgia, and diarrhea, as well as 21 laboratory indicators (Table 2). The 17 variables derived from LASSO were included in the univariate significant results, and after including these 17 variables in a multivariate logistic regression, four independent predictors of SFTS mortality outcomes that were statistically significant were identified and included in the risk score. The four selected variables were age > 70 years (p = 0.001, OR = 2.516, 95% CI 1.452–4.360), elevated serum PT (p < 0.001, OR = 1.383, 95% CI 1.143–1.673), elevated lg viral load (p < 0.001, OR = 1.496, 95% CI 1.290–1.735), and high serum urea level (> 8.0 μmol/L) (p = 0.008, OR = 2.247, 95% CI 1.240–4.071) (Table 2). These four independently correlated risk factors were used to construct a nomogram that predicted the risk of fatal SFTS (R2 = 0.285) (Fig. 2).

Fig. 1 Demographic and clinical features were selected into the LASSO binary logistic regression model

Table 2 Univariate and multivariate logistic regression analyses of risk factors for fatal outcome in patients with SFTS

Variables	Univariate logistic regression	Multivariate logistic regression	
OR (95%CI)	p-value	aOR (95%CI)	p-value	
Age > 70	2.761 (1.703, 4.494)	< 0.001	2.516 (1.452–4.360)	0.001	
Pluse	1.016 (1.001, 1.032)	< 0.05			
Hypertension	1.653 (0.997, 2.712)	< 0.05			
Vomit	1.697 (1.046, 2.743)	< 0.05			
Muscle pain	0.397 (0.170, 0.815)	< 0.05			
Diarrhea	1.649 (1.023, 2.660)	< 0.05			
LYM	0.544 (0.305, 0.883)	< 0.05			
MON	0.175 (0.041, 0.572)	< 0.05			
NEUP	1.017 (1.002, 1.033)	< 0.05			
MONP	0.888 (0.825, 0.947)	< 0.001			
RDW	1.479 (1.137, 1.928)	< 0.05			
PLT	0.986 (0.976, 0.996)	< 0.05			
ALB	0.921 (0.875, 0.967)	< 0.05			
UA	1.004 (1.002, 1.006)	< 0.001			
K	1.797 (1.133, 2.866)	< 0.05			
ALT	1.005 (1.002, 1.007)	< 0.001			
AST	1.003 (1.002, 1.004)	< 0.001			
LDH	1.001 (1.001, 1.002)	< 0.001			
GLB	1.054 (1.011, 1.098)	< 0.05			
TT	1.002 (1.000, 1.003)	< 0.05			
PT	1.445 (1.218, 1.738)	< 0.001	1.383 (1.143–1.673)	< 0.001	
APTT	1.045 (1.029, 1.063)	< 0.001			
FIB	0.466 (0.285, 0.728)	< 0.05			
CRE	1.010 (1.005, 1.016)	< 0.001			
Log-transformed viral load	1.575 (1.363, 1.85)	< 0.001	1.496 (1.290–1.735)	< 0.001	
Elevated serum CK	3.986 (1.892, 9.79)	< 0.001			
Elevated serum urea	3.475 (2.058, 5.849)	< 0.001	2.247 (1.240–4.071)	0.008	

Fig. 2 Nomogram for predicting the fatal risk of SFTS. Lg vl, log-transformed viral load; PT, proth rombin time

Predictive accuracy and net benefit of the nomogram

The nomogram demonstrated good accuracy in predicting fatal risk of SFTS patients, with the ROC showing an AUC of 0.813 (95% CI, 0.758–0.868) (Fig. 3). In addition, the resulting model was internally validated using bootstrap validation, and the calibration curve showed agreement between the risk estimates from the nomogram and the actual observed risk of death (Fig. 4). In the validation cohort, the calibration curve for the risk estimates was also good (Fig. 2C). ROC showed an AUC of 0.801 (95% CI, 0.736–0.865) (Fig. 4). In addition, DCA showed good net benefits of predictive modeling (Fig. 5), indicating good consistency and reliability.

Fig. 3 Receiver operating characteristics (ROC) curve of the multivariate logistic regression model

Fig. 4 Calibration curve for the nomogram predicting fatal outcomes in SFTS patients

Fig. 5 Decision curve analysis (DCA) for the nomogram predicting fatal outcomes in SFTS patients

Discussion

SFTS is an emerging infectious disease that is mainly transmitted by ticks, and interpersonal transmission also exists [18], characterized by high mortality and widespread transmission. The main clinical manifestations of SFTS include fever, muscle pain, malaise, diarrhea, leukopenia, gastrointestinal hemorrhage, thrombocytopenia, and hepatic and renal dysfunction [1], and in severe cases, can lead to multiorgan failure or even death, report The case fatality rate (CFR) ranges from 15.1 to 50% [19]. However, there is no effective treatment available, so early identification of patients in need of intensive therapy at the time of hospital admission is crucial.

In this study, we described the baseline and clinical characteristics of SFTS cases, screened for predictors of fatal outcomes in SFTS, and developed a nomogram to predict the risk of fatal outcomes in SFTS. According to LASSO regression and multivariate logistic regression analyses, SFTS patients with older age(> 70 years), prolonged serum PT, elevated lg viral load, and high urea level (> 8.0 μmol/L) were associated with a higher risk of death. The ROC curve showed that the nomogram including these variables performed well in differentiating SFTS patients with fatal outcomes (AUC = 0.813).

Previous studies have shown that among demographic characteristics, older age is an important risk factor for death in patients with SFTS [20, 21]. With advancing age, the immune function declines and the likelihood of developing underlying health conditions increases, contributing to a higher risk of severe illness from viral infections [22]. A meta-analysis showed that risk factors for SFTS severity included old age, bleeding tendency, central nervous system manifestations, elevated serum enzymes, and high viral load [10]. SFTSV-induced activation of the coagulation pathway increases peripheral blood platelet consumption, which is associated with disseminated intravascular coagulation and endothelial damage due to severe systemic inflammatory response [23]. Another study also showed that bleeding was more severe in patients who died compared to those who survived SFTS, illustrating the association of bleeding tendency with mortality outcomes in SFTS patients [10]. Abnormalities in coagulation indices are also prevalent in patients with SFTS, as evidenced by significant differences in PT, TT, APTT, International Normalized Ratio (INR), and D-dimer levels between the deceased and surviving groups from day 5–6 onwards [24]. In a previous risk-scoring study, age > 65 years, elevated serum PT, TT, and bicarbonate were chosen to construct a predictive model for SFTS critical illness [25], and its good performance indicated that age and serum PT were effective predictors for the occurrence of critical illness in SFTS. These results emphasize the importance of coagulation indices in assessing the severity and prognosis of patients with SFTS, but the correlation between coagulation indices and disease severity, as well as the molecular mechanisms of pathogenesis, have not been well resolved. High viral load has been shown to be an important risk factor for death in SFTS patients [26]. High viral loads may lead to excessive cytokine secretion, triggering severe inflammatory responses and extensive tissue and organ damage, which can further exacerbate the disease [27]. Cytokine-induced inflammatory storm may lead to multiple organ failure, of which renal failure is also an independent risk factor for death in SFTS patients. Studies have confirmed that the pathologic lesions predominantly involve the kidneys. Immunohistochemical studies performed on SFTS patients showed the presence of SFTSV antigens in all organs of the patients, with higher levels of renal antigens [28].

Therefore, the model in this study combined clinical manifestation, virologic and laboratory indicators as predictors of patient mortality, resulting in a more comprehensive assessment of the patient’s condition than previous models that combined only laboratory indicators. The nomograms had good predictive and discriminatory power, suggesting that these general indicators are good predictors of prognosis in SFTS. These indicators are easily accessible, inexpensive, and reproducible, allowing us to identify high-risk patients early, intervene early, and improve patient survival.

Strengths and limitations

The strengths of this study include a long time span, a larger sample size than previous single-center studies of SFTS, a comprehensive assessment of the prognostic risk factors of SFTS patients by considering clinical manifestations, virologic indexes, and laboratory indexes, and the establishment of an internally validated prediction model that has the ability to accurately discriminate the risk of death from in-hospital SFTS. The limitations are that this was a single-center retrospective study, which may affect the quality and generalizability of the data. Second, some potentially meaningful predictors, such as calcitoninogen, troponin, cytokines, imaging data, and viral load were not evaluated because of the lack of data. Third, the model was not externally validated. Fourth, the possible coexistence of other tick-borne viral infections was not excluded.

Conclusions

Age > 70 years, prolonged serum PT, high level of serum urea, and high viral load were independent risk factors for fatal outcomes in SFTS patients. The nomogram based on these 4 predictors performed well in differentiating patients at fatal risk of SFTS and might be used as a simple tool for clinicians to make decisions.

Author contributions

Zhihang Peng and Zhiliang Hu conceived and designed the study. Fang Zhong, Xiaoling Lin, Chengxi Zheng, Shuhan Tang, Yi Yin, Kai Wang and Zhixiang Dai collected and organized the data. Fang Zhong and Xiaoling Lin analyzed the data and drafted the manuscript. All authors provided critical review and final approval of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (82320108018) and National Key R&D Program of China (2023YFC2306004, 2022YFC2304000).

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to secrecy but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was performed according to the Helsinki II Declaration and was approved by the Ethics Committee of Nanjing Medical University, China (Ethics No. (2020) 211). The requirement for informed consent by individual patients was waived by the Ethics Committee of Nanjing Medical University due to the retrospective nature of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Fang Zhong and Xiaoling Lin contributed equally to this work.
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