| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-05-25 |
| タイトル |
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タイトル |
Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning |
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言語 |
en |
| 言語 |
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言語 |
eng |
| キーワード |
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言語 |
en |
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キーワード |
coronary artery calcification |
| キーワード |
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言語 |
en |
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キーワード |
coronary artery disease |
| キーワード |
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言語 |
en |
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キーワード |
coronary computed tomography |
| キーワード |
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言語 |
en |
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キーワード |
machine learning analysis |
| キーワード |
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言語 |
en |
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キーワード |
major adverse cardiac events |
| キーワード |
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言語 |
en |
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キーワード |
validation study |
| 資源タイプ |
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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 |
| 著者 |
Wada, Shinichi
Sakuraba, Makino
中井, 陸運
WEKO
35309
e-Rad_Researcher
50595147
| ja |
中井, 陸運
宮崎大学
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| ja-Kana |
ナカイ, ミチカズ
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| en |
Nakai, Michikazu
University of Miyazaki
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Search repository
Suzuki, Takayuki
Miyamoto, Yoshihiro
Noguchi, Teruo
Iwanaga, Yoshitaka
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parameters for predicting major adverse cardiac events (MACEs).
Methods The Nationwide Gender-specific Atherosclerosis Determinants Estimation and Ischemic Cardiovascular Disease Prospective Cohort study (NADESICO) of 1,187 patients with suspected coronary artery disease 50-74 years old was used to build a MACE prediction model. The ML random forest (RF) model was compared with a logistic regression analysis. The performance of the ML model was evaluated using the area under the curve (AUC) with the 95% confidence interval (CI).
Results Among 1,178 patients from the NADESICO dataset, MACEs occurred in 103 (8.7%) patients during a median follow-up of 4.4 years. The AUC of the RF model for MACE prediction was 0.781 (95% CI: 0.670-0.870), which was significantly higher than that of the conventional logistic regression model [AUC, 0.750 (95% CI: 0.651-0.839)]. The important features in the RF model were coronary artery stenosis (CAS) at any site, CAS in the left anterior descending branch, HbA1c level, CAS in the right coronary artery, and sex. In the external validation cohort, the model accuracy of ensemble ML-RF models that were trained on and tuned using the NADESICO dataset was not similar [AUC: 0.635 (95% CI: 0.599-0.672)].
Conclusion The ML-RF model improved the long-term prediction of MACEs compared to the logistic regression model. However, the selected variables in the internal dataset were not highly predictive of the external dataset. Further investigations are required to validate the usefulness of this model. |
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言語 |
en |
| bibliographic_information |
en : Internal Medicine
巻 64,
号 7,
p. 1001-1008,
発行日 2025-01-01
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| 出版者 |
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出版者 |
Japanese Society of Internal Medicine |
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言語 |
en |
| 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 |
09182918 |
| 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 |
13497235 |
| item_10001_relation_14 |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.2169/internalmedicine.3566-24 |
| 権利 |
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権利情報 |
© 2025 by The Japanese Society of Internal Medicine |
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言語 |
en |
| 出版タイプ |
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出版タイプ |
VoR |
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