Background and Aims

Currently, patients with hepatocellular carcinoma (HCC) in the United States are assigned a uniform score relative to the median MELD at transplant (MMaT-3) after a minimum 6-month waiting period. Here, we develop a risk stratification model for patients with HCC, using the available and objective variables at time of listing.

Methods

We identified adult liver transplant candidates with approved HCC exception in the OPTN database from 2015-2022. Cox regression analysis, as well as machine learning models (random survival forest and neural network), were used to develop models predicting waitlist dropout. Predicted waitlist dropout for patients with HCC was scaled to patients without exception using MELD 3.0.

Results

18,273 patients with HCC were listed for liver transplant with a median MELD 3.0 of 11 (IQR 8-15) and AFP 6 (IQR 4-17). Since all models performed similarly, a parsimonious Cox-based model comprised of MELD 3.0, AFP, and tumor burden, Multi-HCC (Model for Urgency for Liver Transplantation in HCC), was selected, with a c-statistic of 0.71 (95% CI 0.69-0.74) for 6-month dropout in the validation set, outperforming previous models, including HALT-HCC, deMELD, and MELD-Eq.

Conclusion

An urgency-based priority system for patients with HCC, similar to MELD for patients with chronic liver disease, is achievable with a parsimonious model incorporating AFP, MELD 3.0, and tumor size. This approach can be applied to the liver allocation system to prioritize patients with HCC and can inform decision-making regarding urgency weights for exception cases in the upcoming continuous distribution system.

中文翻译：

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多 HCC：一种优先考虑肝移植等待名单上的肝细胞癌患者的实用模型

 背景和目标

目前，美国肝细胞癌 （HCC） 患者在至少 6 个月的等待期后被分配相对于移植时中位 MELD （MMaT-3） 的统一评分。在这里，我们使用上市时的可用和客观变量为 HCC 患者开发了一个风险分层模型。

 方法

我们在 2015-2022 年的 OPTN 数据库中确定了 HCC 例外的成人肝移植候选者。Cox 回归分析以及机器学习模型 （随机生存森林和神经网络） 用于开发预测候补名单退出的模型。使用 MELD 3.0 将 HCC 患者的预测候补名单退出率缩放到无一例外的患者。

 结果

18,273 例 HCC 患者被列为肝移植患者，中位 MELD 为 3.0，为 11 （IQR 8-15），AFP 6 （IQR 4-17）。由于所有模型的表现相似，因此选择了由 MELD 3.0、AFP 和肿瘤负荷组成的基于 Cox 的简洁模型，即 Multi-HCC（HCC 肝移植紧急度模型），验证集中 6 个月退出的 c 统计量为 0.71 （95% CI 0.69-0.74），优于以前的模型，包括 HALT-HCC、deMELD 和 MELD-Eq。

 结论

HCC 患者基于紧急性的优先系统，类似于慢性肝病患者的 MELD，可以通过结合 AFP、MELD 3.0 和肿瘤大小的精简模型来实现。这种方法可以应用于肝脏分配系统，以优先考虑 HCC 患者，并可以为即将到来的连续分配系统中异常情况的紧急权重的决策提供信息。