Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit
IntroductionDelirium, 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 deliri...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1425562/full |
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| author | Hyungjun Kim Hyungjun Kim Min Kim Da Young Kim Dong Gi Seo Ji Man Hong Ji Man Hong Dukyong Yoon Dukyong Yoon |
| author_facet | Hyungjun Kim Hyungjun Kim Min Kim Da Young Kim Dong Gi Seo Ji Man Hong Ji Man Hong Dukyong Yoon Dukyong Yoon |
| author_sort | Hyungjun Kim |
| collection | DOAJ |
| description | IntroductionDelirium, 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.MethodsA 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.ResultsThe 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.ConclusionOur model can predict the real-time possibility of delirium in patients with ischemic stroke and will be helpful to monitor high-risk patients. |
| format | Article |
| id | doaj-art-24187e9f112a4f8f81c70acbe19762b6 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-24187e9f112a4f8f81c70acbe19762b62025-01-09T06:11:03ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-01-011810.3389/fnins.2024.14255621425562Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unitHyungjun Kim0Hyungjun Kim1Min Kim2Da Young Kim3Dong Gi Seo4Ji Man Hong5Ji Man Hong6Dukyong Yoon7Dukyong Yoon8Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of KoreaMDHi Corp, Suwon, Republic of KoreaDepartment of Neurology, Ajou University School of Medicine, Suwon, Republic of KoreaDepartment of Convergence Healthcare Medicine, Graduate School of Ajou University (ALCHeMIST), Suwon, Republic of KoreaDepartment of Biomedical Science, Ajou University Graduate School of Medicine, Suwon, Republic of KoreaDepartment of Neurology, Ajou University School of Medicine, Suwon, Republic of KoreaDepartment of Convergence Healthcare Medicine, Graduate School of Ajou University (ALCHeMIST), Suwon, Republic of KoreaDepartment of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of KoreaCenter for Digital Health, Yongin Severance Hospital, Yongin, Republic of KoreaIntroductionDelirium, 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.MethodsA 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.ResultsThe 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.ConclusionOur model can predict the real-time possibility of delirium in patients with ischemic stroke and will be helpful to monitor high-risk patients.https://www.frontiersin.org/articles/10.3389/fnins.2024.1425562/fulldeliriummachine learningvital signsearly diagnosisischemic stroke |
| spellingShingle | Hyungjun Kim Hyungjun Kim Min Kim Da Young Kim Dong Gi Seo Ji Man Hong Ji Man Hong Dukyong Yoon Dukyong Yoon Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit Frontiers in Neuroscience delirium machine learning vital signs early diagnosis ischemic stroke |
| title | Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit |
| title_full | Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit |
| title_fullStr | Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit |
| title_full_unstemmed | Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit |
| title_short | Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit |
| title_sort | prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit |
| topic | delirium machine learning vital signs early diagnosis ischemic stroke |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1425562/full |
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