BMC infectious diseases · 2025
Intensive care unit-based mortality risk model construction for severe fever with thrombocytopenia syndrome patients: a retrospective study.
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Liu Puhui, Liu Fangyuan, Wang Chunhui, Mu Aimin, Niu Chuanzhen, Zhu Shihong, Wang Ji
Аннотация
OBJECTIVE: This study develops a predictive model to evaluate mortality risk in severe fever with thrombocytopenia syndrome (SFTS) patients in intensive care units (ICU) to improve the accuracy of prognosis and guide the optimization of treatment strategies.
METHODS: In this study, a retrospective analysis was conducted on severe SFTS patients admitted to the ICU between July 2019 and October 2023. Patients were categorized into survival and mortality groups. Multivariate logistic regression was performed to determine independent risk factors (IRFs) for mortality. In addition, the nomogram model was constructed and its performance was assessed through ROC curves.
RESULTS: The study comprised 218 severe SFTS patients. The mortality group showed significantly lower Glasgow Coma Scale (GCS) scores, oxygenation indices, and higher levels of several serological markers, log10(virus loads), and lactic acid. Multivariate analysis identified GCS score [odds ratio (OR) = 0.66, P < 0.001], log10(virus loads) [OR = 2.24, P = 0.001], lactic acid [OR = 1.60, P = 0.01], and cystatin C [OR = 1.80, P = 0.049] as IRFs for mortality. A nomogram incorporating these IRFs demonstrated excellent predictive accuracy (AUC = 0.92, 95% CI: 0.88-0.96), with a sensitivity of 76% and a specificity of 91%. This model showed adequate fit and good clinical applicability.
CONCLUSION: The nomogram model, based on GCS score, log10(virus loads), lactic acid, and cystatin C, offers clinical utility in predicting 28-day mortality for severe SFTS patients, though further validation is needed.
METHODS: In this study, a retrospective analysis was conducted on severe SFTS patients admitted to the ICU between July 2019 and October 2023. Patients were categorized into survival and mortality groups. Multivariate logistic regression was performed to determine independent risk factors (IRFs) for mortality. In addition, the nomogram model was constructed and its performance was assessed through ROC curves.
RESULTS: The study comprised 218 severe SFTS patients. The mortality group showed significantly lower Glasgow Coma Scale (GCS) scores, oxygenation indices, and higher levels of several serological markers, log10(virus loads), and lactic acid. Multivariate analysis identified GCS score [odds ratio (OR) = 0.66, P < 0.001], log10(virus loads) [OR = 2.24, P = 0.001], lactic acid [OR = 1.60, P = 0.01], and cystatin C [OR = 1.80, P = 0.049] as IRFs for mortality. A nomogram incorporating these IRFs demonstrated excellent predictive accuracy (AUC = 0.92, 95% CI: 0.88-0.96), with a sensitivity of 76% and a specificity of 91%. This model showed adequate fit and good clinical applicability.
CONCLUSION: The nomogram model, based on GCS score, log10(virus loads), lactic acid, and cystatin C, offers clinical utility in predicting 28-day mortality for severe SFTS patients, though further validation is needed.