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. 2024 Jan 8:14:1307960.
doi: 10.3389/fmicb.2023.1307960. eCollection 2023.

A model based on meta-analysis to evaluate poor prognosis of patients with severe fever with thrombocytopenia syndrome

Zishuai Liu #  1 , Zhouling Jiang #  1 , Ligang Zhang #  2 , Xiaoyu Xue  1 , Chenxi Zhao  1 , Yanli Xu  2 , Wei Zhang  1 , Ling Lin  2 , Zhihai Chen  1
Affiliations

A model based on meta-analysis to evaluate poor prognosis of patients with severe fever with thrombocytopenia syndrome

Zishuai Liu et al. Front Microbiol. .

Abstract

Background: Early identification of risk factors associated with poor prognosis in Severe fever with thrombocytopenia syndrome (SFTS) patients is crucial to improving patient survival.

Method: Retrieve literature related to fatal risk factors in SFTS patients in the database, extract the risk factors and corresponding RRs and 95% CIs, and merge them. Statistically significant factors were included in the model, and stratified and assigned a corresponding score. Finally, a validation cohort from Yantai Qishan Hospital in 2021 was used to verify its predictive ability.

Result: A total of 24 articles were included in the meta-analysis. The model includes six risk factors: age, hemorrhagic manifestations, encephalopathy, Scr and BUN. The analysis of lasso regression and multivariate logistic regression shows that model score is an independent risk factor (OR = 1.032, 95% CI 1.002-1.063, p = 0.034). The model had an area under the curve (AUC) of 0.779 (95% CI 0.669-0.889, P<0.001). The validation cohort was divided into four risk groups with cut-off values. Compared with the low-medium risk group, the mortality rate of high-risk and very high-risk patients was more significant (RR =5.677, 95% CI 4.961-6.496, P<0.001).

Conclusion: The prediction model for the fatal outcome of SFTS patients has shown positive outcomes.Systematic review registration:https://www.crd.york.ac.uk/prospero/ (CRD42023453157).

Keywords: SFTS; cohort study; meta-analysis; prediction model; risk factors.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of this study. (A) Flow diagram outlining the literature search and study selection for risk factors of death in patients with SFTS. (B) Process for the selection of patients in the validation cohort.
Figure 2
Figure 2
Pooled RRs of risk factors for mortality in SFTS patients and their corresponding 95% CIs in systematic reviews and meta-analyses. (A) Overall pooled RRs and their 95% CIs of SFTS mortality risk factors. (B) Subgroup study for the risk factors of SFTS mortality.
Figure 3
Figure 3
Validation of prediction model for mortality risk in SFTS patients. (A) The prediction model has an AUC of 0.779 (95% CI 0.669–0.889, p < 0.001). (B) Calibration curves for the prediction model of mortality risk. (C) Decision curve analysis for the prediction model of mortality risk.
Figure 4
Figure 4
Comparison of outcomes in different mortality risk groups of the validation cohort. (A) Mortality rate in the four risk groups stratified by risk score in the validated cohort. (B) Kaplan–Meier curve of survival endpoint for each risk group. High-risk group: RR 2.775 (95% CI 0.921–8.359, P < 0.001); very high-risk group: 10.358 (95% CI 3.962–27.079, P < 0.001).
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