Zheng Shan, Tong Yuxin, Chen Jiayi, Yang Linlin, Tan Yamin
Department of Hematology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
Hangzhou Institute of Medicine (HlM), Chinese Academy of Sciences, Hangzhou, China.
Front Psychiatry. 2025 Feb 21;16:1506550. doi: 10.3389/fpsyt.2025.1506550. eCollection 2025.
A marked increase in suicide rate has been detected among individuals diagnosed with leukemia. Our research aimed to develop a predictive model intended for assessing the suicide risk in leukemia patients. This novel tool aims to optimize the process of pinpointing individuals at high risk within clinical environments, thereby guaranteeing the timely provision of targeted intervention strategies.
Between 2000 and 2020, our study involved a cohort of 194584 leukemia patients, extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly stratified into distinct training and validation cohorts. We utilized the Cox proportional hazards model to screen for influential variables and construct a predictive nomogram within the training set. The concordance index (C-index) and receiver operating characteristic (ROC) curves were employed to evaluate model's discrimination, and calibration curves was used to assess the calibration ability. Furthermore, the validation set was utilized to conduct an internal validation process to ensure the robustness of nomogram.
Age, gender, race, residence, marital status, and histologic type were selected to construct the nomogram for predicting suicide risk of leukemia patients. In the training and validation sets, the C-indexes were 0.798 and 0.776, respectively. The calibration plots demonstrated a significant agreement between the predicted and actual outcomes. Ultimately, leukemia patients were divided into two groups, and Kaplan-Meier curves showed significant differences in the high- and low-risk groups, as confirmed in the validation set.
We have successfully developed an intuitive and robust predictive model for assessing the suicide risk among leukemia patients. This model holds the potential to contribute to the reduction of preventable deaths.
在被诊断患有白血病的个体中，自杀率已出现显著上升。我们的研究旨在开发一种预测模型，用于评估白血病患者的自杀风险。这一新型工具旨在优化在临床环境中精准识别高危个体的过程，从而确保及时提供有针对性的干预策略。
2000年至2020年间，我们的研究纳入了从监测、流行病学和最终结果（SEER）数据库中提取的194584名白血病患者队列。这些患者被随机分层为不同的训练和验证队列。我们使用Cox比例风险模型筛选有影响的变量，并在训练集中构建预测列线图。采用一致性指数（C指数）和受试者工作特征（ROC）曲线评估模型的区分度，并用校准曲线评估校准能力。此外，利用验证集进行内部验证过程，以确保列线图的稳健性。
选择年龄、性别、种族、居住地、婚姻状况和组织学类型来构建预测白血病患者自杀风险的列线图。在训练集和验证集中，C指数分别为0.798和0.776。校准图显示预测结果与实际结果之间具有显著一致性。最终，将白血病患者分为两组，Kaplan-Meier曲线显示高危组和低危组存在显著差异，这在验证集中得到了证实。
我们成功开发了一种直观且稳健的预测模型，用于评估白血病患者的自杀风险。该模型有可能有助于减少可预防的死亡。
