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开发和验证癌症患者虚弱的诊断名录

Siyu Luo¹, Feifei Chong¹, Zhenyu Huo¹

¹Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China; Key Laboratory of Intelligent Clinical Nutrition and Transformation for Chongqing Municipal Health Commission, Chongqing, China.

Nutrition (Burbank, Los Angeles County, Calif.)

|August 7, 2024

概括

一种新型的诺姆图准确诊断癌症患者的脆弱性,识别了10个危险因素. 这种工具有助于临床医生选择最佳治疗方法,以改善患者的生存率和治疗结果.

科学领域:

在瘤学瘤学.
老年病的医生老年病的医生
生物统计学生物统计学

背景情况:

脆弱性显著降低了癌症患者的整体存活率.
准确的脆弱性诊断对于个性化癌症治疗计划至关重要.
目前的诊断方法需要改进,以获得临床效用.

研究的目的:

开发和验证用于诊断癌症患者脆弱性的名录.
为了确定与这一群体的脆弱性相关的关键风险因素.
通过准确的脆弱性评估,改进治疗选择和患者预后.

主要方法:

预计将有10649名癌症患者纳入INSCOC项目 (中国,2013-2022年).
随机划分为培训 (70%) 和验证 (30%) 队列.
应用多变量逻辑回归,考克斯回归和LASSO用于名ogram开发.
使用一致性指数和校准曲线评估诊断效用.

主要成果:

确定了10个脆弱的危险因素:年龄,AJCC阶段,肝癌,血红蛋白,放射治疗,手术,手握强度,小腿周长,PG-SGA得分和QOL (QLQ-C30).
诺米图表显示出高的诊断准确性,C指数为0.847 (训练) 和0.853 (验证).
该模型在两个队列中显示了对1年,3年和5年的死亡率的良好预测性能,并具有适当的校准曲线和DCA.

结论:

开发的诺米图谱是诊断癌症患者脆弱性的准确工具.
这个名图的临床应用可以促进更合适的治疗选择.
预计使用名图表改善的虚弱诊断将导致更好的患者预后.

关键词:

癌症患者癌症患者动态名字图 (Dynamic Nomogram) 是一个动态名字图.脆弱性脆弱性脆弱性拉索回归法 (Lasso Regression) 是一种回归法.死亡率死亡率死亡率现实世界的队列研究.

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