背景:肺癌,一个重大的全球健康问题,不成比例地影响低社会经济地位(SES)患者,他们面临着欠佳的护理和降低生存率。本研究旨在评估传统Cox比例风险(CoxPH)回归和机器学习模型的预后表现,特别是决策树(DT),随机森林(RF),支持向量机(SVM)和极端梯度提升(XGBoost),在低SES的晚期肺癌患者中。
方法:回顾性研究。
方法:武汉市某三甲医院临终关怀病房949例低SES的晚期肺癌患者,中国,从2012年1月至2021年12月,我们以3∶1的比例随机分为训练组和试验组.CoxPH回归方法和四种机器学习算法(DT,射频,SVM,和XGBoost)用于构建预后风险预测模型。
结果:基于CoxPH回归的列线图显示了60、90和120天生存率的可靠预测准确性。在机器学习模型中,XGBoost表现出最好的性能,而射频在60天的准确度最低,DT在90天,和SVM在120天。所有模型的关键预测因素包括Karnofsky绩效状态(KPS)得分,生活质量(QOL)评分,和咳嗽症状。
结论:CoxPH,DT,射频,SVM,XGBoost模型可有效预测低SES的晚期肺癌患者60-120天的死亡风险。监控KPS,QOL,咳嗽症状对于识别可能需要加强护理的高危患者至关重要。由于预测精度不同,临床医生应选择适合个人患者需求和偏好的模型。
■本研究报告严格遵守加强流行病学观察研究报告(STROBE)指南。
■没有患者或公众捐款。
BACKGROUND: Lung cancer, a major global health concern, disproportionately impacts low socioeconomic status (SES) patients, who face suboptimal care and reduced survival. This study aimed to evaluate the prognostic performance of traditional Cox proportional hazards (CoxPH) regression and machine learning models, specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in patients with advanced lung cancer with low SES.
METHODS: A retrospective study.
METHODS: The 949 patients with advanced lung cancer with low SES who entered the hospice ward of a tertiary hospital in Wuhan, China, from January 2012 to December 2021 were randomized into training and testing groups in a 3:1 ratio. CoxPH regression methods and four machine learning algorithms (DT, RF, SVM, and XGBoost) were used to construct prognostic risk prediction models.
RESULTS: The CoxPH regression-based nomogram demonstrated reliable predictive accuracy for survival at 60, 90, and 120 days. Among the machine learning models, XGBoost showed the best performance, whereas RF had the lowest accuracy at 60 days, DT at 90 days, and SVM at 120 days. Key predictors across all models included Karnofsky Performance Status (KPS) score, quality of life (QOL) score, and cough symptoms.
CONCLUSIONS: CoxPH, DT, RF, SVM, and XGBoost models are effective in predicting mortality risk over 60-120 days in patients with advanced lung cancer with low SES. Monitoring KPS, QOL, and cough symptoms is crucial for identifying high-risk patients who may require intensified care. Clinicians should select models tailored to individual patient needs and preferences due to varying prediction accuracies.
UNASSIGNED: This study was reported in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline.
UNASSIGNED: No patient or public contribution.