Background

Acute kidney injury (AKI) is a common complication after liver transplantation (LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach.

Methods

A total of 493 patients with donation after cardiac death LT (DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes (KDIGO). The clinical data of patients with AKI (AKI group) and without AKI (non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve (AUC).

Results

The incidence of AKI was 35.7% (176/493) during the follow-up period. Compared with the non-AKI group, the AKI group showed a remarkably lower survival rate (P < 0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval (CI): 0.794–0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models (P < 0.001).

Conclusions

The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.

中文翻译：

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机器学习模型在预测心脏死亡肝移植后捐献后急性肾损伤中的应用

背景

急性肾损伤（AKI）是肝移植（LT）后常见的并发症，是预后不良的指标。建立更准确的 AKI 术前预测模型有助于改善 LT 的预后。机器学习算法提供了一种潜在有效的方法。

方法

共有 493 名心源性死亡 LT (DCDLT) 患者入组。AKI 是根据肾脏疾病临床实践指南定义的：改善全球结局 (KDIGO)。比较有 AKI（AKI 组）和无 AKI（非 AKI 组）患者的临床资料。以逻辑回归分析作为常规模型，使用以下算法开发了四种预测机器学习模型：随机森林、支持向量机、经典决策树和条件推理树。然后使用接受者操作特征曲线 (AUC) 下的面积评估这些模型的预测能力。

结果

随访期间AKI发生率为35.7%（176/493）。与非 AKI 组相比，AKI 组的生存率显着降低（P < 0.001）。随机森林模型的预测精度最高，为 0.79，AUC 为 0.850 [95% 置信区间 (CI)：0.794–0.905]，显着高于其他机器学习算法和逻辑回归模型的 AUC ( P  < 0.001 ）。

结论

在我们的研究中，基于机器学习算法预测 DCDLT 后发生的 AKI 的随机森林模型表现出比其他模型更强的预测能力。这表明机器学习方法可以为 DCDLT 后的 AKI 预测提供可行的工具。