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淋巴瘤患者中自杀的情况

Jie Zhou¹, Mengjie Tian², Xiangchen Zhang¹

¹Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China.

Journal of affective disorders

|May 31, 2024

概括

这项研究开发了一种名谱,以识别高自杀风险的淋巴瘤患者. 该模型有助于个性化干预,以减少这一脆弱人群中自杀风险.

科学领域:

在瘤学瘤学.
精神病学是一个精神病学.
生物统计学生物统计学

背景情况:

淋巴瘤患者的自杀率较高.
需要有效的干预措施来减轻这一人口群体中自杀风险.

研究的目的:

为了构建一个预测性诺姆图,用于识别高风险淋巴瘤患者的自杀.
帮助开发针对性干预措施,以预防淋巴瘤患者的自杀.

主要方法:

利用监测,流行病学和最终结果 (SEER) 数据库 (2000-2020年) 对235,806名淋巴瘤患者.
雇佣最低绝对缩小和选择运算符 (LASSO) 和考克斯回归以确定自杀预测因子.
使用接收机操作特征 (ROC) 曲线,校准曲线和决策曲线分析 (DCA) 验证了名图.

主要成果:

确定了关键预测因素:诊断时的年龄,性别,种族,婚姻状况,阶段,手术,放射治疗和年收入.
诺米图表显示出良好的预测准确性,其c指数为0.773 (训练) 和0.777 (验证).
校准曲线和DCA证实了名图的临床实用性和可靠性.

结论:

开发了一种可靠的诺姆图,以个性化和量化淋巴瘤患者的自杀风险.
该工具可以支持针对个性化的自杀预防策略的临床决策.
进一步的研究可能会探索追溯分析中未测量的混因素的影响.

关键词:

淋巴瘤是一种淋巴瘤.机器学习是机器学习.这个名字叫做Nomogram.自杀自杀的自杀是自杀的自杀.

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