对静脉血栓栓塞的综合风险评估模型
Mary Sixian Lin1, Hilary Hayssen1, Minerva Mayorga-Carlin1
1Department of Surgery, University of Maryland School of Medicine, Baltimore, MD.
Journal of vascular surgery. Venous and lymphatic disorders
|September 21, 2024
概括
使用102个风险因素的新复合模型改善了超过100万名医院患者的静脉血栓栓塞 (VTE) 预测. 然而,目前的风险评估模型 (RAMs) 尚未可靠用于广泛的临床应用.
科学领域:
- 医疗信息学 医疗信息学
- 临床风险管理临床风险管理
- 流行病学 流行病学
背景情况:
- 静脉血栓塞栓症 (VTE) 构成与住院相关的发病率和死亡率的重大风险.
- 准确的VTE风险分层对于预防至关重要,联邦授权进行普遍风险评估.
- 像卡普里尼和帕多瓦这样的现有模型显示一般医院入院的预测准确性有限.
研究的目的:
- 评估是否结合23个可用的VTE风险评估模型 (RAM) 的风险因素可以提高VTE风险预测.
- 评估一种包含众多风险因素的新型复合RAM的预测性能.
主要方法:
- 分析了超过118万名退伍军人事务医院住院病人的情况 (2016-2021年).
- 后勤回归用于预测VTE在90天内,利用23个VTERAM的风险因素.
- 在优化值时使用AUC,灵敏度,特异性,PPV和NPV评估模型性能.
主要成果:
- 与卡普里尼因子 (AUC 0.63) 相比,具有102个风险因素的复合RAM显示了改善的静脉瘤预测 (AUC 0.74).
- 复合模型在灵敏度和特异性方面显示了适度的增长,NPV高但PPV低.
- 在复合模型中最大限度地提高PPV只能带来微不足道的改进,由于对NPV和灵敏度的权衡而受到限制.
结论:
- 一个综合模型整合了102个风险因素,在一个大型的国家队列中显著改善了VTE预测.
- 尽管有了改进,但复合模型和现有模型都没有足够的灵敏度或PPV来进行可靠的VTE预测.
- 目前的VTE风险评估工具不足以在一般医院群体中广泛实施.
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