Ruptured brain arteriovenous malformations (bAVMs) in a child are associated with substantial morbidity and mortality. Prior studies investigating predictors of hemorrhagic presentation of a bAVM during childhood are limited. Machine learning (ML), which has high predictive accuracy when applied to large data sets, can be a useful adjunct for predicting hemorrhagic presentation. The goal of this study was to use ML in conjunction with a traditional regression approach to identify predictors of hemorrhagic presentation in pediatric patients based on a retrospective cohort study design.

Using data obtained from 186 pediatric patients over a 19-year study period, the authors implemented three ML algorithms (random forest models, gradient boosted decision trees, and AdaBoost) to identify features that were most important for predicting hemorrhagic presentation. Additionally, logistic regression analysis was used to ascertain significant predictors of hemorrhagic presentation as a comparison.

All three ML models were consistent in identifying bAVM size and patient age at presentation as the two most important factors for predicting hemorrhagic presentation. Age at presentation was not identified as a significant predictor of hemorrhagic presentation in multivariable logistic regression. Gradient boosted decision trees/AdaBoost and random forest models identified bAVM location and a concurrent arterial aneurysm as the third most important factors, respectively. Finally, logistic regression identified a left-sided bAVM, small bAVM size, and the presence of a concurrent arterial aneurysm as significant risk factors for hemorrhagic presentation.

By using an ML approach, the authors found predictors of hemorrhagic presentation that were not identified using a conventional regression approach.

儿童脑动静脉畸形 (bAVM) 破裂与高发病率和死亡率相关。先前调查儿童期 bAVM 出血性表现的预测因素的研究有限。机器学习 (ML) 在应用于大型数据集时具有很高的预测准确性，可以成为预测出血表现的有用辅助手段。本研究的目的是基于回顾性队列研究设计，将 ML 与传统回归方法结合使用，以确定儿科患者出血表现的预测因子。

使用在 19 年研究期间从 186 名儿科患者获得的数据，作者实施了三种 ML 算法（随机森林模型、梯度提升决策树和 AdaBoost）来识别对预测出血表现最重要的特征。此外，逻辑回归分析用于确定出血表现的重要预测因素作为比较。

所有三个 ML 模型在将 bAVM 大小和患者就诊时的年龄确定为预测出血性表现的两个最重要因素方面是一致的。在多变量逻辑回归中，就诊时的年龄未被确定为出血性就诊的重要预测因子。梯度增强决策树/AdaBoost 和随机森林模型分别将 bAVM 位置和并发动脉瘤确定为第三个最重要的因素。最后，逻辑回归确定左侧 bAVM、小 bAVM 大小和并发动脉瘤的存在是出血的重要危险因素。

通过使用 ML 方法，作者发现了使用传统回归方法无法识别的出血表现的预测因子。