系统开发和验证一个预测模型,用于重大术后并发症在外科期质量改善项目 (PQIP) 数据集的术后质量改善项目 (PQIP)
C M Oliver1,2, D Wagstaff2,1, J Bedford2,1
1Centre for Peri-operative Medicine, University College London, UK.
Anaesthesia
|February 18, 2024
概括
预测手术后的主要术后并发症至关重要. 结合医院特定数据和患者因素的新型复杂模型显著优于现有工具,改善了患者护理和资源管理.
科学领域:
- 手术结果研究研究.
- 医疗信息学 医疗信息学
- 医学中的预测分析.
背景情况:
- 大型手术经常导致并发症,增加医疗费用,患者残疾和死亡率.
- 目前用于预测手术并发症的工具往往不准确且难以使用,需要改进手术前风险评估.
研究的目的:
- 开发和验证一个准确的术前模型来预测主要的术后并发症.
- 将新型模型的性能与现有的预测工具进行比较.
- 确定导致当前预测模型不准确的因素.
主要方法:
- 来自英国外科手术期间质量改善计划数据集 (24,983例) 的患者记录分析.
- 使用75%的培训和25%的测试分割开发预测模型,优先考虑手术前预测因子与LASSO (最小绝对收缩和选择操作员).
- 主要并发症的定义是Clavien-Dindo等级≥2,模型选择基于Akaike的信息标准.
主要成果:
- 25.6% (6389名患者) 经历了重大术后并发症.
- 表现最好的模型是复杂的,包括个别的医院并发症率和11个患者共变量,保留疼痛,运动减少和吸烟等可修改因素.
- 新型模型与通用和特定预测模型和得分相比,表现明显优越.
结论:
- 开发了一种新的,高度准确的术前模型,用于预测主要的术后并发症.
- 医院一级的变化是现有预测工具不准确性的重要,以前被忽视的因素.
- 最佳模型的复杂性凸显了对自动化临床风险预测系统的需求,以提供及时,信息化的风险估计.
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