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走向无阿片类药物门诊手术:使用机器学习预测术后阿片类药物使用的前性研究.

Savannah Renshaw1, Divyaam Satija1, Abdullah Aly2

  • 1Center for Abdominal Core Health, Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH.

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概括
此摘要是机器生成的。

一个机器学习模型预测患者在手术后需要阿片类药物,帮助个性化疼痛管理. 这种阿片类药物节约策略减少了与术后阿片类药物使用相关的风险.

关键词:
外科手术 手术手术这是一个循环的门诊机构.止痛药是一种止痛药.机器学习是机器学习.在阿片类药物阿片类药物.

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科学领域:

  • 麻醉学 麻醉学
  • 疼痛管理 疼痛管理
  • 医疗保健中的机器学习

背景情况:

  • 手术后使用阿片类药物存在依赖和转移的风险.
  • 制定有效的阿片类药物节约策略对于患者安全至关重要.
  • 确定患有阿片类药物使用高风险的患者对于量身定制的疼痛管理至关重要.

研究的目的:

  • 开发和评估在门诊外科手术中使用阿片类药物节约治疗方案.
  • 创建一个机器学习 (ML) 模型来预测术后阿片类药物使用.
  • 确定与术后阿片类药物消费相关的关键因素.

主要方法:

  • 实施了向无阿片类药物出行外科手术 (TOFAS) 计划,交替使用布洛芬和乙氨基,并使用有限的氧化救援剂量.
  • 开发了一种ML模型,以预测在接受门诊手术的成年患者中术后使用阿片类药物.
  • 验证了ML模型使用接收器运行特征曲线 (AUC) 下的面积,使用80/20列车测试分割和10个随机种子.

主要成果:

  • 在223名注册患者中,42%的患者填写了他们的阿片类药物处方,使用的中位数为4剂量.
  • ML模型实现了0.674的平均测试AUC,灵敏度为0.70和特异性为0.68.
  • 阿片类药物使用的关键预测因素包括活跃的癌症,年龄,麻醉类型,种族/种族,COPD病史,手术后并发症,手术前使用乙氨基和疼痛强度.

结论:

  • 开发的ML模型可靠地识别了患有术后阿片类药物使用高风险的患者.
  • 这种预测能力支持在门诊环境中个性化,节省阿片类药物的疼痛管理策略.
  • 该模型促进了量身定制的疼痛管理计划,可能减少阿片类药物依赖和转移.