在腹腔镜胆囊切除术患者中对外科期阿片类药物需求的预测模型的开发:一种机器学习方法
Yongmei Huang1, Guohua Li2,3, Silvia S Martins2
1Department of Obstetrics and Gynecology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY.
Surgery open science
|February 3, 2026
概括
使用电子医疗记录和机器学习,开发对外科期间阿片类药物需求的预测模型是可行的. 这种方法可能有助于在腹腔镜胆囊切除术后个性化疼痛管理.
科学领域:
- 医疗信息学 医疗信息学
- 疼痛管理 疼痛管理
- 医疗保健中的机器学习
背景情况:
- 阿片类药物处方的做法正在不断发展,特别是在外科手术期间.
- 针对阿片类药物处方的计算机化临床决策支持系统有限.
- 准确预测外科手术期间的阿片类药物需求对于有效的疼痛管理和防止滥用至关重要.
研究的目的:
- 开发和验证进行腹腔镜胆囊切除术 (LC) 的患者的外科期阿片类药物需求的预测模型.
- 创建一个风险评分工具来指导阿片类药物处方.
- 为此目的探索机器学习模型和电子病历 (EMR) 的实用性.
主要方法:
- 对1136名因良性疾病接受LC的患者进行了回顾性队列研究 (2015年10月 - 2018年12月).
- 来自EMR的数据包括人口统计学,临床和外科手术因素.
- 预测模型 (随机森林,LASSO) 使用培训/测试分割开发和验证,评估AUC和Brier分数等绩效指标.
主要成果:
- 最小绝对收缩和选择操作员 (LASSO) 模型,经过对主题专业知识的修订,显示出比随机森林更好的校准.
- 最终的模型实现了0.64的AUC和0.20.20的布莱尔得分.
- 确定的主要预测因素包括性别,手术前药物,紧急手术,麻醉类型和手术指示. 为视觉预测而创建了一个名ogram.
结论:
- 使用EMR和机器学习预测外科手术期间的阿片类药物需求是可行的.
- 这种方法显示了支持个性化疼痛管理策略的潜力.
- 进一步完善模型性能是必要的,以提高临床实用性.
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