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

Geun-Hyeong Kim; Jae-Woo Kim; Ka Hyun Kim; Hyeran Kang; Jae Young Moon; Yoon Mi Shin; Seung Park

doi:10.1016/j.cmpb.2023.107673

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

Comput Methods Programs Biomed. 2023 Oct:240:107673. doi: 10.1016/j.cmpb.2023.107673. Epub 2023 Jun 14.

Authors

Geun-Hyeong Kim¹, Jae-Woo Kim¹, Ka Hyun Kim¹, Hyeran Kang², Jae Young Moon³, Yoon Mi Shin⁴, Seung Park⁵

Affiliations

¹ Medical AI Research Team, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea.
² Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea.
³ Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungnam National University Sejong Hospital, Chungnam National University College of Medicine, 35015, Rep. of Korea.
⁴ Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea. Electronic address: anees94@hanmail.net.
⁵ Department of Biomedical Engineering, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, 28644, Rep. of Korea. Electronic address: spark.cbnuh@gmail.com.

PMID: 37336152
DOI: 10.1016/j.cmpb.2023.107673

Abstract

Background and objectives: Intensive 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).

Methods: We 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.

Results: The 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) CONCLUSIONS: Through 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.

Keywords: Artificial intelligence; Graph neural network; Mechanical ventilation; Spontaneous breathing trial; Weaning protocols.

MeSH terms

Artificial Intelligence*
Humans
Neural Networks, Computer
Respiration, Artificial* / methods
Retrospective Studies
Ventilator Weaning / methods

Substances

GAT