开发和验证APEX-HBD得分:在接受肩关节整形手术的患者中进行术后并发症的多变量预测模型
Majd Mzeihem1, Wedam Nyaaba1, James Oosten1
1Department of Orthopaedic Surgery, University of Illinois at Chicago, Chicago, IL, USA.
Journal of shoulder and elbow surgery
|August 16, 2025
概括
一个新的APEX-HBD SCORe工具使用11个患者因素和机器学习来预测肩膀关节整形术 (SA) 后的短期并发症. 这种经过验证的模型有助于患者风险分层和个性化术前规划.
科学领域:
- 整形外科手术 整形外科手术
- 医疗信息学 医疗信息学
- 预测分析是一种预测分析.
背景情况:
- 肩膀关节整形术 (SA) 的发生率正在增加,这是由于扩大了适应症.
- 准确预测短期并发症对于患者管理至关重要.
- 现有的风险评估工具可能无法完全捕捉患者的复杂性.
研究的目的:
- 导出和验证一个预测模型,用于肩膀关节整形术后的短期并发症.
- 为了比较后勤回归 (LR) 和机器学习 (ML) 模型的性能.
- 为临床使用开发一个实用的风险分层工具.
主要方法:
- 从NSQIP数据库 (2005-2022) 中分析了39,028个SA病例.
- 开发了一个后勤回归模型和几个机器学习模型 (渐变增强,AdaBoost,随机森林,额外随机树木).
- 创建了APEX-HBD SCORe,这是一个以点为基础的系统,用于将风险分层分为低风险,中等风险和高风险类别.
主要成果:
- 后勤回归确定了11种并发症预测因素,包括白蛋白,血红素和ASA分类.
- 在APEX-HBD SCORe中,风险类别的并发症率逐渐增加.
- 梯度提升在ML模型中实现了最高的AUC,超过LR和修改后的脆弱性指数 (MFI_5).
结论:
- APEX-HBD SCORe是一个经过验证,ML增强的工具,可以预测SA后30天的并发症.
- 该得分利用了11个关键的患者因素来有效地分层风险.
- 该工具支持改善患者咨询,术前规划和量身定制的术后护理.
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