低社会经济地位的晚期肺癌患者的预后和风险因素评估:模型开发和验证
Jiaxin Cui1,2, Zifen An1,3, Xiaozhou Zhou1,4
1Center for Nurturing Care Research, Wuhan University School of Nursing, Wuhan University, No. 115 Donghu Road, Wuhan, Hubei province, 430071, China.
BMC cancer
|September 10, 2024
概括
机器学习和考克斯回归模型有效预测低SES高级肺癌患者的死亡率. 关键因素包括绩效状况,生活质量和咳,指导风险分层和护理.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 肺癌不成比例地影响低社会经济地位 (SES) 患者,导致结果较差.
- 评估预后模型对于改善对这种脆弱人群的护理至关重要.
研究的目的:
- 评估Cox比例危险 (CoxPH) 回归和机器学习 (ML) 模型的预后性能.
- 为了比较传统和ML方法来预测低SES的晚期肺癌患者的死亡率.
主要方法:
- 对949名晚期肺癌患者的回顾性分析,低SES.
- 开发和比较CoxPH,决策树 (DT),随机森林 (RF),支向量机 (SVM) 和极端梯度增强 (XGBoost) 模型.
- 模型被训练并使用3:1比率进行测试.
主要成果:
- 考克斯PH nomogram 显示可靠的生存预测在60-120天.
- XGBoost表现出最佳的ML性能;RF,DT和SVM在不同时间点的精度不同.
- 卡诺夫斯基表现状态 (KPS),生活质量 (QOL) 和咳症状是模型中的关键预测因素.
结论:
- 考克斯PH和ML模型 (DT,RF,SVM,XGBoost) 有效预测低SES高级肺癌患者的短期死亡率.
- 监测KPS,QOL和咳有助于识别高风险个体.
- 模型选择应根据患者的需要和不同的预测准确性进行个性化选择.
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