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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Translational Neuroscience
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1425562
This article is part of the Research Topic Innovative Applications of Machine Learning and Cutting-Edge Tools for Stroke Prediction and Treatment Strategies View all 4 articles

Prediction of Delirium Occurrence Using Machine Learning in Acute Stroke Patients in Intensive Care Unit

Provisionally accepted
Hyungjun Kim Hyungjun Kim 1,2Min Kim Min Kim 3Da Young Kim Da Young Kim 4Ji Man Hong Ji Man Hong 3,4*Dukyong Yoon Dukyong Yoon 5,6*
  • 1 Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
  • 2 MDHi Corp, Suwon, Republic of Korea
  • 3 Department of Neurology, Ajou University School of Medicine, Suwon, Gyeonggi, Republic of Korea
  • 4 Department of Convergence Healthcare Medicine, Graduate School of Ajou University (ALCHeMIST), Suwon, Republic of Korea
  • 5 Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Gyeonggi, Republic of Korea
  • 6 Center for Digital Health, Yongin Severance Hospital, Yongin, Republic of Korea

The final, formatted version of the article will be published soon.

    Delirium, frequently experienced by ischemic stroke patients, is one of the most common neuropsychiatric syndromes reported in the Intensive Care Unit (ICU). Stroke patients with delirium have a high mortality rate and lengthy hospitalization. For these reasons, early diagnosis of delirium in the ICU is critical for better patient prognosis. Therefore, we developed and validated prediction models to classify the real-time delirium status in patients admitted to the ICU or Stroke Unit (SU) with ischemic stroke. A total of 84 delirium patients and 336 non-delirium patients in the ICU of Ajou University Hospital were included. The 8 fixed features (Age, Sex, Alcohol Intake, National Institute of Health Stroke Scale(NIHSS), HbA1c, Prothrombin time, D-dimer, and Hemoglobin) identified at admission and 12 dynamic features(Mean or Variability indexes calculated from Body Temperature(BT), Heart Rate(HR), Respiratory Rate(RR), Oxygen saturation(SpO2), Systolic Blood Pressure(SBP), and Diastolic Blood Pressure(DBP)) based on vital signs were used for developing prediction models using the ensemble method. The Area Under the Receiver Operating Characteristic curve (AUROC) for delirium-state classification was 0.80. In simulation-based evaluation, AUROC was 0.71, and the predicted probability increased closer to the time of delirium occurrence. We observed that the patterns of dynamic features, including BT, SpO2, RR, and Heart Rate Variability(HRV) kept changing as the time points were getting closer to the delirium occurrence time. Therefore, the model that employed these patterns showed increasing prediction performance. Our model can predict the real-time possibility of delirium in patients with ischemic stroke and will be helpful to monitor high-risk patients.

    Keywords: Delirium, machine learning, Vital Signs, early diagnosis, Ischemic Stroke For primary outcome labeling, we classified two statuses-125 delirium and non-delirium status

    Received: 30 Apr 2024; Accepted: 11 Dec 2024.

    Copyright: © 2024 Kim, Kim, Kim, Hong and Yoon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Ji Man Hong, Department of Neurology, Ajou University School of Medicine, Suwon, Gyeonggi, Republic of Korea
    Dukyong Yoon, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Gyeonggi, Republic of Korea

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.