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一个新的风险计算器,用于下肢绕行术后非家庭排放的风险计算器
Jonathan A Cunha1, Jeremy Albright2, Brian T Fry1
1University of Michigan, Center for Healthcare Outcomes and Policy, Ann Arbor, MI 48109, USA; University of Michigan, Institute for Healthcare Policy and Innovation, Ann Arbor, MI 48109, USA.
Annals of vascular surgery
|February 6, 2026
概括
预测下肢旁路术后的非家庭排放是非常重要的. 后勤回归和XGBoost模型都能准确预测患者出院的目的地,从而改善手术前的规划.
科学领域:
- 血管外科 血管外科
- 医疗服务研究 医疗服务研究
- 机器学习在医学中的应用
背景情况:
- 下肢旁路手术后的非家庭排泄 (NHD) 与显著的延迟,并发症和降低患者生活质量有关.
- 准确预测NHD对于优化患者护理至关重要,包括共享决策,术前准备和出院规划.
研究的目的:
- 开发和比较下肢旁路手术后非家庭出院 (NHD) 的预测模型.
- 评估包括外科手术期间变量对NHD预测模型准确性的影响.
主要方法:
- 从全州血管外科注册 (2016-2023) 中分析了接受下肢旁路术的成年患者.
- 开发了两个预测模型:多变量逻辑回归和XGBoost机器学习.
- 模型仅使用术前变量进行训练和测试,然后使用术前和术后变量. 使用AUC和校准图表来评估性能.
主要成果:
- 在9789名患者中,与NHD相关的因素包括年龄较大,女性性别,黑人种族,非吸烟身份,组织损失,静脉移植使用,紧急呈现,停留时间较长,脆弱性较高和并发症.
- 包括术后变量显著改善了对逻辑回归 (AUC 0.760到0.855) 和XGBoost (AUC 0.755到0.854) 的模型歧视.
- 在逻辑回归和XGBoost模型 (p=0.308) 之间没有观察到预测准确度的显著差异.
结论:
- 后勤回归和XGBoost模型都准确地预测了下肢绕道后的非家庭排放.
- 整合外科手术期间的数据大大提高了这些模型的预测性能.
- 开发的风险计算器有助于手术前的规划,并提高排放效率.
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