BMC infectious diseases · 2024
Establishment and validation of a clinical risk scoring model to predict fatal risk in SFTS hospitalized patients.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhong Fang, Lin Xiaoling, Zheng Chengxi, Tang Shuhan, Yin Yi, Wang Kai, Dai Zhixiang, Hu Zhiliang, Peng Zhihang
Аннотация
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.
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.