在膝关节骨关节炎的皮质类固醇决策中用于患者选择的机器学习:可行性模型
Omar Musbahi1, Kyriacos Pouris2, Savvas Hadjixenophontos2
1Department of Surgery and Cancer - Faculty of Medicine, Imperial College London, London W12 0BZ, United Kingdom. om112@ic.ac.uk.
World journal of methodology
|September 3, 2025
概括
开发了一种机器学习模型,预测皮质类固醇注射可缓解膝关节骨关节炎的疼痛. 该模型显示可行性,但需要进一步开发用于临床使用.
科学领域:
- 整形医学
- 生物医学工程
- 数据科学
背景情况:
- 膝关节骨关节炎的疼痛管理至关重要,内关节皮质类固醇注射是一种常见但具有可变的有效治疗方法.
- 确定可能受益于这些注射的患者对于优化治疗结果至关重要.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测膝关节骨关节炎患者对关节内皮质固醇注射的反应.
- 加强临床决策在选择适当的候选人进行这种干预.
主要方法:
- 来自两个前性队列研究 (骨关节炎倡议和多中心骨关节炎研究) 的综合数据.
- 使用线性判别分析构建预测症状疼痛改善的ML算法.
- 使用西安大略大学和麦克马斯特大学的OA疼痛量表来评估结果,通过既定标准定义显著的变化.
主要成果:
- 包括330名患者 (平均年龄为63. 4岁).
- 只有25.5%的患者在注射后得到明显的疼痛缓解.
- ML模型的准确度为67.8%,F1得分为30.8%和AUC为0.60.
结论:
- 开发的ML模型显示有助于临床医生选择患者进行皮质类固醇注射.
- 在临床实施之前需要进一步的研究和模型改进.
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