在患有直肠癌的患者中转移口腔闭塞后预测肠功能
Zhang Wang1,2, Shengli Shao1, Lu Liu1
1Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
一个新的机器学习工具可以预测直肠癌患者在口腔关闭后的主要前下切除综合征 (LARS). 这有助于控制肠道功能障碍,改善患者的生活质量.
科学领域:
- 结肠直肠手术 结肠直肠手术
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
背景情况:
- 腹腔镜前切除和转移直肠癌的口腔可能导致关闭后的肠道功能障碍.
- 这种功能障碍显著损害了患者的生活质量.
- 预测和管理这种并发症对于患者护理至关重要.
研究的目的:
- 开发一种机器学习工具,用于预测直肠癌患者口腔闭塞后的肠功能.
- 确定影响重大前下切除综合征 (LARS) 发展的关键因素.
主要方法:
- 使用396名直肠癌患者的临床病理和随访数据开发了一种机器学习模型.
- 患者被分为训练 (70%) 和测试 (30%) 组.
- 模型的性能通过AUC,AUPRC,灵敏度和特异性进行评估;临床效用通过决策曲线分析 (DCA) 进行评估.
主要成果:
- 该研究确定了口腔间隔,新辅助疗法和BMI作为主要LARS的关键预测因素.
- 机器学习模型在训练组中实现了0.78的AUC,在测试组中达到0.74.
- 该模型在测试组中表现出良好的预测性能,灵敏度为0.67和特异性为0.66,DCA证实了临床适用性.
结论:
- 一个经过验证的机器学习模型可以预测直肠癌患者在转向静脉关闭后的主要LARS.
- 这种工具可以帮助临床医生在患者咨询和决策方面做出关于静脉栓逆转的决定.
- 改善LARS的预测可以提高术后管理和患者的生活质量.
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