使用机器学习算法识别发热伴血小板减少综合征的早期预后生物标志物。

Annals of medicine Pub Date : 2025-12-01 Epub Date: 2025-01-13 DOI:10.1080/07853890.2025.2451184
Jie Zhu, Jianmei Zhou, Chunhui Tao, Guomei Xia, Bingyan Liu, Xiaowei Zheng, Xu Li, Zhenhua Zhang
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引用次数: 0

摘要

目的我们旨在确定严重发热伴血小板减少综合征(SFTS)急性期生物标志物,并建立一个预测死亡率结果的模型:方法: 我们对多中心临床数据进行了回顾性分析。方法:对多中心临床数据进行回顾性分析,利用基于组的轨迹建模(GBTM)来展示实验室指标的总体趋势及其与死亡率的相关性。根据急性期的临床特征,采用六种不同的机器学习算法建立预后模型,并用Lasso回归法对其进行还原:结果发现,发病后 7-10 天的七项指标(ALT、AST、BUN、LDH、a-HBDH、DD 和 PLT)及其变化斜率在疾病进展过程中至关重要。利用 Lasso 回归法构建模型时,将这些变量和其他临床特征缩减为 8 个变量。随机森林模型在内部验证(AUC:0.961)和外部验证(AUC:0.948)中表现最佳。结论:a-HBDH及其变化斜率以及发病后7-10天内的中枢神经症状表现可准确预测SFTS患者的死亡率。各种算法可提供疾病进展的全面概况,并构建出更稳定、更高效的模型。
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Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms.

Objective: We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.

Methods: A retrospective analysis was conducted on multicenter clinical data. Group-based trajectory modeling (GBTM) was utilized to demonstrate the overall trend of laboratory indicators and their correlation with mortality. Six different machine learning algorithms were employed to develop prognostic models based on the clinical features during the acute phase, which were reduced using Lasso regression.

Results: Seven indicators (ALT, AST, BUN, LDH, a-HBDH, DD, and PLT) at 7-10 days post-onset and their change slopes were found to be crucial during disease progression. These, along with other clinical features, were reduced to 8 variables using Lasso regression for model construction. The random forest model demonstrated the best performance in both internal validation (AUC: 0.961) and external validation (AUC: 0.948). Decision Curve Analysis indicated a good balance between model benefits and risks.

Conclusions: a-HBDH and its change slope along with central nervous symptom manifestations within 7-10 days after onset accurately predicted mortality in SFTS. Various algorithms provided a comprehensive overview of disease progression and constructed more stable and efficient models.

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