在接受息性放射治疗的患者中,通过例行血液检查预测30天的死亡率:逻辑回归和梯度增强模型的比较
Tae Hoon Lee1, Sang Hoon Seo1, Hyunju Shin2
1Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
概括
这项研究开发了一种渐变增强模型 (GBM-B),使用血液测试特征来预测接受息性放射治疗 (RT) 的患者的30天死亡率 (30D_M). 该模型有效地将患者分为不同的预后组,为死亡率预测提供了一个客观的工具.
科学领域:
- 在瘤学瘤学.
- 辐射疗法 辐射疗法
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 息性放射治疗 (RT) 旨在改善癌症患者的生活质量.
- 准确预测30天死亡率 (30D_M) 对于优化息治疗至关重要.
- 预测30D_M在这个群体中的现有模型需要进一步改进.
研究的目的:
- 在接受息性RT的患者中估计30天死亡率 (30D_M).
- 使用临床和实验室数据开发和比较30D_M的预测模型.
- 确定最有效的模型,将患者分为预后组分层.
主要方法:
- 对3756名接受息性RT (2018-2020) 的患者进行了回顾性审查.
- 开发了四种预测模型:后勤回归 (LRM-A,LRM-B) 和梯度增强 (GBM-A,GBM-B).
- 模型使用了所有19个提取的特征或7个血液测试特征的子集.
主要成果:
- 在培训,内部和外部验证队列中,30D_M率分别为10.6%,11.2%和17.5%.
- 使用血液测试特征 (GBM-B) 的渐变增强模型显示出强大的预测性能 (AUC 0.830-0.863).
- GBM-B成功地将患者分为四个不同的预后组,具有明显不同的30D_M率.
结论:
- GBM-B模型提供了一种强大而客观的方法,用于预测息性RT患者的30天死亡率.
- 该模型可以帮助临床医生进行风险分层和个性化息护理计划.
- 使用血液测试特征为这一队列的死亡率预测提供了一种实际方法.
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