| アイテムタイプ |
リポジトリ登録用アイテムタイプ（シンプル）(1) |
| 公開日 |
2025-12-16 |
| タイトル |
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|
タイトル |
Machine Learning-Based Prediction of Life-Threatening Complications During Hemodialysis in Hospitalized Patients With Poor General Conditions |
|
言語 |
en |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
Kato, Naotaka
| en |
Kato, Naotaka
Department of Clinical Engineering, Hirosaki University School of Medicine and Hospital
|
| ja |
弘前大学医学部附属病院 臨床工学部
|
Search repository
Goto, Takeshi
| en |
Goto, Takeshi
Department of Clinical Engineering, Hirosaki University School of Medicine and Hospital
|
| ja |
弘前大学医学部附属病院 臨床工学部
|
Search repository
Ohira, Tomoyuki
Kinoshita, Hirotaka
Kurokawa, Kugo
Naganuma, Kouhei
Ohminato, Chikako
Ogasawara, Junko
Hatakeyama, Shingo
Sasaki, Yoshihiro
Hirota, Kazuyoshi
Ohyama, Chikara
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Background
Patients undergoing hemodialysis (HD) face a significantly elevated risk of cardiovascular mortality, with sudden events during treatment posing a critical threat to survival. These risks are particularly pronounced in high-risk populations, such as patients recovering from cardiovascular surgery or those being treated for sepsis. Therefore, the development of effective preventive strategies is essential for improving patient outcomes. This study aimed to develop a machine learning model that uses pretreatment patient characteristics to predict sudden adverse events during HD and within 24 h after treatment in high-risk inpatients at acute care hospitals.
Methods
His retrospective study analyzed data from 739 patients who underwent HD at Hirosaki University Hospital between 2018 and 2021. Sudden events were defined as fatal arrhythmia, refractory intradialytic hypotension, or respiratory arrest. A logistic regression model was constructed using backward stepwise selection from 51 patient characteristics (demographic data, clinical parameters, laboratory data, and HD-related information).
Results
Among the 739 patients, 17 (2.3%) experienced sudden events. The model identified 23 pre-HD covariates and achieved an area under the receiver operating characteristic curve (AUC) of 0.889. Key covariates included emergency hospitalization (present in 71% of patients with sudden events), recent surgery (76%), shorter HD history, elevated pre-HD heart rate, lower serum albumin levels, and higher C-reactive protein concentrations.
Conclusions
Our model enables the early identification of high-risk inpatients receiving hemodialysis using pre-dialysis data, thereby supporting timely clinical interventions, optimized resource allocation, and improved patient safety. |
|
言語 |
en |
| 書誌情報 |
en : Artificial Organs
巻 Early View,
発行日 2025-09-20
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| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
0160-564X |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1525-1594 |
| DOI |
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関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.1111/aor.70008 |
| 権利情報 |
|
|
権利情報 |
© 2025 The Author(s). Artificial Organs published by International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC. |
|
言語 |
en |
| 権利情報 |
|
|
権利情報 |
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
|
言語 |
en |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 出版者 |
|
|
出版者 |
Wiley |
|
言語 |
en |
Artificial Organs, 2025; 01–11.pdf (592 KB)
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