FT-GAT: Graph neural network for predicting spontaneous breathing trial success in patients with mechanical ventilation.

Abstract

Background and ObjectivesIntensive care unit (ICU) physicians perform weaning procedures considering complex clinical situations and weaning protocols; however, liberating critical patients from mechanical ventilation (MV) remains challenging. Therefore, this study aims to aid physicians in deciding the early liberation of patients from MV by developing an artificial intelligence model that predicts the success of spontaneous breathing trials (SBT). MethodsWe retrospectively collected data of 652 critical patients (SBT success: 641, SBT failure: 400) who received MV at the Chungbuk National University Hospital (CBNUH) ICU from July 2020 to July 2022, including mixed and trauma ICUs. Patients underwent SBTs according to the CBNUH weaning protocol or physician's decision, and SBT success was defined as extubation performed by the physician on the SBT day. Additionally, our dataset comprised 11 numerical and 2 categorical features that can be obtained for any ICU patient, such as vital signs and MV setting values. To predict SBT success, we analyzed tabular data using a graph neural network-based approach. Specifically, the graph structure was designed considering feature correlation, and a novel deep learning model, called feature tokenizer graph attention network (FT-GAT), was developed for graph analysis. FT-GAT transforms the input features into high-dimensional embeddings and analyzes the graph via the attention mechanism. ResultsThe quantitative evaluation results indicated that FT-GAT outperformed conventional models and clinical indicators by achieving the following model performance (AUROC): FT-GAT (0.80), conventional models (0.69–0.79), and clinical indicators (0.65–0.66) ConclusionsThrough timely detection critical patients who can succeed in SBTs, FT-GAT can help prevent long-term use of MV and potentially lead to improvement in patient outcomes.