三种预测模型对腰椎椎间盘的患者深静脉血栓的有效性
Shuai Yang1, Qingfeng Guo1, Yaqing Xing1
1Department of Traditional Chinese Medicine, The First Hospital of Hebei Medical University Shijiazhuang 050091, Hebei, China.
American journal of translational research
|January 17, 2025
概括
这项研究开发了深静脉血栓塞 (DVT) 风险的预测模型,用于腰椎椎间盘 (LDH) 患者. 随机森林模型在预测DVT发生方面表现最好.
科学领域:
- 医疗信息学 医疗信息学
- 临床医学 临床医学
- 生物统计学 生物统计学
背景情况:
- 腰椎间盘 (LDH) 患者面临深静脉血栓症 (DVT) 的风险增加.
- 准确的风险评估对于及时干预和改善LDH群体患者结果至关重要.
- 现有的预测工具可能无法完全捕捉到在这个特定的患者群体中DVT风险的复杂性.
研究的目的:
- 开发和评估基于机器学习的DVT风险预测模型,用于LDH患者.
- 为了比较后勤回归,梯度增强和随机森林模型在预测DVT方面的性能.
- 在LDH患者中确定与DVT发展相关的关键临床指标.
主要方法:
- 对798名LDH患者进行了回顾性分析,数据分为训练 (n=558) 和测试 (n=240) 组.
- 单变量分析确定了重要的临床变量 (年龄,血小板,胆固醇,甘油三,HbA1c,D-二次数,纤维素素,APTT,PT,TT) 用于模型开发.
- 三种预测模型 (物流回归,梯度提升,随机森林) 被构建并使用ROC曲线和校准图表进行评估.
主要成果:
- 在DVT和非DVT组之间观察到年龄和各种血液学参数 (PLT,TC,TG,HbA1c,D-D,FIB,APTT,PT,TT) 的显著差异.
- 在训练组中,随机森林模型实现了最高的曲线下面积 (AUC) 0.978,其次是梯度提升 (0.943) 和后勤回归 (0.919).
- 随机森林模型在测试组中也显示出优异的性能 (AUC=0.952),超过了梯度增强 (0.941) 和物流回归 (0.908).
结论:
- 开发的后勤回归,梯度提升和随机森林模型表明,在LDH患者中对DVT具有显著的预测价值.
- 这些模型可以帮助优化该队列中DVT预防的临床管理策略.
- 随机森林模型在评估的模型中表现出最强的预测能力.
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