在初级膝关节关节整形手术中,使用机器学习方法预测输血的因素
Daniel R Cavazos1, Zain Sayeed, Tannor Court
1From the Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI (Dr. Cavazos, Dr. Sayeed, Dr. Chen, Dr. Court, Dr. Little, and Dr. Darwiche), and the Department of Biomedical Engineering, Wayne State University, Detroit, MI.
The Journal of the American Academy of Orthopaedic Surgeons
|September 21, 2023
概括
机器学习可以准确地预测膝关节全关节整形术 (TKA) 后的输血需求. 这种方法识别了关键的风险因素,改善了TKA手术的患者护理和手术前规划.
科学领域:
- 整形外科手术 整形外科手术
- 数据科学数据科学数据科学
- 麻醉学 麻醉学
背景情况:
- 急性失血性贫血是全膝关节整形术 (TKA) 后的重大术后并发症.
- 不同基因的输血经常需要管理这种贫血.
- 预测输血风险对于优化患者治疗结果至关重要.
研究的目的:
- 开发和验证机器学习模型,用于预测初级TKA后的输血需求.
- 确定导致输血必要性的人口和手术因素.
- 评估机器学习在改善TKA手术前规划方面的潜力.
主要方法:
- 分析了2,093名接受初级TKA治疗的患者队列.
- 来自医院质量改进数据库的数据被用来提取患者的人口统计和手术变量.
- 多层感知神经网络 (MPNN) 机器学习算法用于预测,并与传统的统计分析一起使用.
主要成果:
- 输血的关键预测因素包括手术前的血红蛋白和肌素水平,手术持续时间,同时进行的双边手术,使用松酸,ASA得分,白蛋白水平,乙醇使用,抗凝状态,年龄和TKA类型.
- 接受输血的患者在医院住院时间明显长.
- 该MPNN模型表现出高预测性能,AUC为0.894.
结论:
- MPNN模型有效地对影响TKA患者输血风险的因素进行排名.
- 与TKA输血预测的传统统计方法相比,机器学习提供了更好的预测能力.
- 这种MPNN在TKA中的新应用有可能提高手术前规划和患者管理.
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