Xie Yi, Xie Ni, Guo Jiao
Department of Anesthesiology, Shaanxi Provincial People's Hospital, Xi'An, China.
Faculty of Business, The Hong Kong Polytechnic University, Hong Kong, Hong Kong.
Digit Health. 2025 Jul 30;11:20552076251361680. doi: 10.1177/20552076251361680. eCollection 2025 Jan-Dec.
With the intensifying global population aging, the demand for mechanical ventilation in geriatric patients is rising. Given their complex physiological traits and sparse intensive care unit (ICU) data, accurate intubation prediction is difficult. Premature intubation may raise the risk of hypoxic organ damage, whereas delayed intubation can lead to increased ventilator-associated mortality. Therefore, developing precise intubation prediction models is vital for elderly ICU patients.
This study retrospectively analyzed data from ICU patients aged over 65 years in the MIMIC-IV and eICU databases. The intubation prediction task was formulated using a sliding window with a strict temporal data split to avoid data leakage. We propose a dynamic mask attention graph neural network (DymaGNN) to capture the time-varying relationship of key physiological variables by constructing a dynamic heterogeneous graph structure and an adaptive edge-weighting mechanism. The mask attention layer is designed to identify the key timesteps in the irregular sampling data.
The experiments showed that DymaGNN achieved an area under the curve (AUC) value of 0.8363 and 0.8557 on the intubation prediction task on MIMIC-IV and eICU datasets, respectively, and maintained an AUC of 0.8115 under a 15% data missing rate. Visualization of the feature interaction graph revealed the relationship between important features such as respiratory rate and oxygen saturation. These interaction patterns matched much clinical knowledge, significantly improving doctors' trust in the model prediction.
Our proposed DymaGNN establishes a useful method for mechanical ventilation prediction in elderly ICU patients, achieving high predictive accuracy and remaining robust under a 10% data missing rate. Its interpretable feature interaction graphs provide transparent insights, aligning with established medical knowledge to build trustworthy tools for real-world ICU intubation decisions.
随着全球人口老龄化加剧，老年患者对机械通气的需求不断上升。鉴于其复杂的生理特征以及重症监护病房（ICU）数据稀少，准确的插管预测较为困难。过早插管可能会增加缺氧性器官损伤的风险，而延迟插管则可能导致呼吸机相关性死亡率上升。因此，开发精确的插管预测模型对老年ICU患者至关重要。
本研究回顾性分析了MIMIC-IV和eICU数据库中65岁以上ICU患者的数据。插管预测任务采用滑动窗口制定，并进行严格的时间数据分割以避免数据泄露。我们提出了一种动态掩码注意力图神经网络（DymaGNN），通过构建动态异构图结构和自适应边加权机制来捕捉关键生理变量的时变关系。掩码注意力层旨在识别不规则采样数据中的关键时间步长。
实验表明，DymaGNN在MIMIC-IV和eICU数据集上的插管预测任务中，曲线下面积（AUC）值分别达到0.8363和0.8557，并且在数据缺失率为15%的情况下，AUC仍保持在0.8115。特征交互图的可视化揭示了呼吸频率和血氧饱和度等重要特征之间的关系。这些交互模式与许多临床知识相匹配，显著提高了医生对模型预测的信任度。
我们提出的DymaGNN为老年ICU患者的机械通气预测建立了一种有用的方法，具有较高的预测准确性，并且在数据缺失率为10%的情况下仍保持稳健。其可解释的特征交互图提供了透明的见解，与既定医学知识相一致，可以为现实世界中的ICU插管决策构建值得信赖的工具。
