预测转移性结直肠癌患者应用福罗替尼后总生存期的提名图构建与多中心验证:一项多中心回顾性研究。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-10-08 eCollection Date: 2024-01-01 DOI:10.1177/17562848241284229
Xiao-Xuan Wang, Yu-Wen Zhou, Bo Wang, Peng Cao, De-Yun Luo, Chun-Hong Li, Kai Wang, Meng Qiu
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引用次数: 0

摘要

背景福喹替尼是转移性结直肠癌(mCRC)患者的三线及后续靶向治疗药物。确定弗鲁喹替尼治疗后的生存预测因素对于优化该药的临床应用至关重要:我们旨在确定影响接受fruquintinib治疗的mCRC患者预后的因素,并利用这些洞察力建立一个提名图模型,用于估算这一患者群体的生存率:多中心回顾性观察研究:我们收集了2019年1月至2023年10月的患者数据,其中一家医疗机构的数据作为训练队列,其他三家医院的数据作为多中心验证队列。根据 Cox 回归模型计算总生存期的提名图,使用单变量 Cox 回归分析筛选变量选择,并根据临床经验增加变量。通过一致性指数(C-index)、校准曲线、决策曲线分析(DCA)和效用(将患者分为低风险组和高风险组)来衡量模型的性能:最终收集了 240 名患者的数据,其中 144 名患者被纳入训练队列,96 名患者被纳入多中心验证队列。纳入提名图的预测因素包括 CA199、体重指数、T 分期、肿瘤的原发部位以及其他转移和病理分化。在训练集和多中心验证中,提名图的 C 指数分别为 0.714 和 0.729。模型已完全校准,其预测结果与观察数据非常吻合。DCA 曲线表明预测模型具有良好的临床效益。最后,通过使用提名图进行风险分类,也验证了模型的可靠性:我们根据六个变量构建了使用福仑替尼治疗 mCRC 的提名图,该提名图可用于协助个性化用药。
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A nomogram construction and multicenter validation for predicting overall survival after fruquintinib application in patients with metastatic colorectal cancer: a multicenter retrospective study.

Background: Fruquintinib is a third-line and subsequent targeted therapy for patients with metastatic colorectal cancer (mCRC). Identifying survival predictors after fruquintinib is crucial for optimizing the clinical use of this medication.

Objectives: We aimed to identify factors influencing the prognosis of patients with mCRC treated with fruquintinib and to leverage these insights to develop a nomogram model for estimating survival rates in this patient population.

Design: Multicenter retrospective observational study.

Methods: We collected patient data from January 2019 to October 2023, with one healthcare institution's data serving as the training cohort and the other three hospitals' data serving as the multicenter validation cohort. The nomogram for overall survival was calculated from Cox regression models, and variable selection was screened using the univariate Cox regression analysis with additional variables based on clinical experience. Model performance was measured by the concordance index (C-index), calibration curves, decision curve analyses (DCA), and utility (patient stratification into low-risk vs high-risk groups).

Results: Data were ultimately collected on 240 patients, with 144 patients included in the training cohort and 96 included in the multicenter validation cohort. Predictors included in the nomogram were CA199, body mass index, T stage, the primary site of the tumor, and other metastatic and pathological differentiation. The C-index of the nomogram in the training set and multicenter validation was 0.714 and 0.729, respectively. The models were fully calibrated and their predictions aligned closely with the observed data. DCA curves indicated the promising clinical benefits of the predictive model. Finally, the reliability of the model was also verified through the risk classification using the nomogram.

Conclusions: We constructed a nomogram for mCRC treated with fruquintinib based on six variables that may be used to assist in personalizing the use of the drug.

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