Classification and Regression Trees to Predict for Survival for Patients With Hepatocellular Carcinoma Treated With Atezolizumab and Bevacizumab.

IF 3.3 Q2 ONCOLOGY
Timothy J Brown, Phyllis A Gimotty, Ronac Mamtani, Thomas B Karasic, Yu-Xiao Yang
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Abstract

Purpose: Systemic therapy with atezolizumab and bevacizumab can extend life for patients with advanced hepatocellular carcinoma (HCC). However, there is substantial variability in response to therapy and overall survival. Although current prognostic models have been validated in HCC, they primarily consider covariates that may be reflective of the severity of the underlying liver disease of patients with HCC. We developed and internally validated a classification and regression tree (CART) to identify patient characteristics associated with risks of early mortality, at or before 6 months from treatment initiation.

Methods: This retrospective cohort study used the nationwide Flatiron Health electronic health record-derived deidentified database and included patients with a diagnosis of HCC after January 1, 2020, who received initial systemic therapy with atezolizumab and bevacizumab. CART was developed from available baseline clinical and demographic information to predict mortality within 6 months from treatment initiation. Model characteristics were compared to the albumin-bilirubin (ALBI) model and was further validated against a contemporary validation cohort of patients after a data update.

Results: A total of 293 patients were analyzed. The CART identified seven cohorts of patients from baseline demographic and laboratory characteristics. The model had an area under the receiver operating curve (AUROC) of 0.739 (95% CI, 0.683 to 0.794) for predicting 6-month mortality. This model was internally valid and performed more favorably than the ALBI model, which had an AUROC of 0.608 (95% CI, 0.557 to 0.660). The model applied to the contemporary validation cohort (n = 111) had an AUROC of 0.666 (95% CI, 0.506 to 0.826).

Conclusion: Using CART, we identified unique cohorts of patients with HCC treated with atezolizumab and bevacizumab with distinct risks of early mortality. This approach outperformed the ALBI model and used clinical and laboratory characteristics that are readily available to oncologists caring for these patients.

用分类树和回归树预测接受阿特珠单抗和贝伐珠单抗治疗的肝细胞癌患者的生存率
目的:使用阿特珠单抗和贝伐单抗进行全身治疗可延长晚期肝细胞癌(HCC)患者的生存期。然而,患者对治疗的反应和总生存期存在很大差异。虽然目前的预后模型已在 HCC 中得到验证,但它们主要考虑的是可能反映 HCC 患者基础肝病严重程度的协变量。我们开发并在内部验证了一种分类和回归树(CART),以确定与治疗开始后 6 个月或 6 个月之前早期死亡风险相关的患者特征:这项回顾性队列研究使用了全国性的 Flatiron Health 电子健康记录衍生去标识数据库,纳入了 2020 年 1 月 1 日之后诊断为 HCC 并接受了阿特珠单抗和贝伐珠单抗初始系统治疗的患者。根据现有的基线临床和人口统计学信息开发了 CART,用于预测治疗开始后 6 个月内的死亡率。模型特征与白蛋白-胆红素(ALBI)模型进行了比较,并在数据更新后与当代验证患者队列进行了进一步验证:结果:共分析了 293 名患者。结果:共分析了 293 名患者,CART 从基线人口学和实验室特征中识别出七个患者队列。该模型预测 6 个月死亡率的接收者操作曲线下面积 (AUROC) 为 0.739(95% CI,0.683 至 0.794)。该模型具有内部有效性,其表现优于ALBI模型,后者的AUROC为0.608(95% CI,0.557至0.660)。应用于当代验证队列(n = 111)的模型的AUROC为0.666(95% CI,0.506至0.826):利用 CART,我们确定了接受阿特珠单抗和贝伐珠单抗治疗的 HCC 患者中具有不同早期死亡风险的独特队列。这种方法的效果优于 ALBI 模型,而且使用的临床和实验室特征对于治疗这些患者的肿瘤学家来说是唾手可得的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
4.80%
发文量
190
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