一种新的机器学习方法来预测大肠切除术的教科书结果
Amir Ashraf Ganjouei1, Fernanda Romero-Hernandez1, Patricia C Conroy1
1Department of Surgery, University of California San Francisco, San Francisco, California.
Diseases of the colon and rectum
|October 10, 2023
概括
一个机器学习模型确定了预测colectomy患者理想手术结果的关键因素. 网络计算器现在可以帮助外科医生评估实现教科书结果的概率,帮助高风险患者咨询.
科学领域:
- 手术瘤学手术瘤学
- 机器学习在医学中的应用
- 医疗信息学 医疗信息学
背景情况:
- 现有的风险计算器专注于并发症,但预测理想的结果 (教科书结果) 对于像胆管切除术这样的低风险手术的临床决策至关重要.
- 一本教科书的结果是一个复合的措施,代表一个理想的外科手术结果,由没有死亡率,再入院,并发症,重新干预和短期住院来定义.
研究的目的:
- 确定关键的手术前因素,以预测经过胆管切除术的非转移性结肠癌患者的教科书结果.
- 开发基于机器学习的决策支持工具,用于估计实现教科书结果的可能性.
主要方法:
- 来自ACS NSQIP数据库的20498名成年患者接受非转移性结肠癌选择性切除术 (2014-2020年) 的回顾性分析.
- 四个机器学习模型 (逻辑回归,决策树,随机森林,极端梯度提升) 被训练并验证.
- 作为一个概念验证工具,开发了一个基于Web的计算器.
主要成果:
- 总体而言,66%的患者实现了教科书的结果.
- 机器人切除术 (77%) 的结局率高于腹腔镜 (68%) 和开放式切除术 (39%).
- 极端梯度增强模型表现出最好的性能 (AUC=0.72). 关键预测因素包括手术方法,年龄,血红素,口服抗生素肠道准备和性别.
结论:
- 教科书结局是评估切除术外科结果的有价值指标.
- 开发的基于Web的计算器,利用机器学习,可以帮助外科医生进行手术前评估和患者咨询,特别是对于高风险个体.
- 这项研究的回顾性质是一个局限性.
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