Research on left atrial appendage thrombogenic milieu prediction model in patients with nonvalvular atrial fibrillation based on machine learning algorithm Article Swipe
Ling Song
,
Niu Xiao-qi
,
Binbin Wang
,
Xiang Xu
,
Wan Chen
,
Feng Liu
,
Xuefeng Tang
,
Yan Wen
,
Liping Liu
,
S. Zhiyuan
,
Li huakang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7301811/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7301811/v1
Objective To establish a machine-learning model of left atrial appendage thrombogenic milieu (LAATM) in patients with nonvalvular atrial fibrillation (NVAF), and analyze the corresponding risk factors to guide clinical decision-making. Methods Patients with NVAF were selected and divided into LAATM group and non-LAATM group, according to the results of transesophageal echocardiography (TEE). The LAATM group included LAA thrombus formation, sludge and spontaneous echo contrast. The patient data was collected and preprocessed. The machine learning algorithms of random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) were used to establish a predictive model for LAATM in patients with NVAF. Shapley additive explanation (SHAP) was used to sort the feature importance of clinical factors. Results A total of 1217 patients were selected in this study, including 112 patients in LAATM group and 1105 patients in non-LAATM group. In terms of predictive performance, AUC value of RF model was 0.97, F1 score was 0.93, accuracy was 0.98, precision was 0.99, and recall was 0.89; AUC value of SVM model is 0.96, F1 score is 0.89, accuracy is 0.97, precision is 0.95, recall is 0.84; The AUC value of the XGBoost model is 0.96, F1 score is 0.88, accuracy is 0.97, precision is 0.98, and recall is 0.82. The prediction efficiency of RF model is the best. The prediction results of RF model were visualized by SHAP diagram, indicating that Homocysteine (HCY), NT-proBNP, C-reactive protein(CRP), glycosylated hemoglobin (HbA1c) and ABC stroke score were the top five risk factors affecting the formation of LAATM in patients with NVAF. Conclusion The RF model achieved the best predictive performance between the three prediction model. HCY, NT-proBNP, CRP, HbA1c and ABC stroke score were the top five risk factors affecting the formation of LAATM in patients with NVAF.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-7301811/v1
- https://www.researchsquare.com/article/rs-7301811/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414067281
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4414067281Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-7301811/v1Digital Object Identifier
- Title
-
Research on left atrial appendage thrombogenic milieu prediction model in patients with nonvalvular atrial fibrillation based on machine learning algorithmWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-08Full publication date if available
- Authors
-
Ling Song, Niu Xiao-qi, Binbin Wang, Xiang Xu, Wan Chen, Feng Liu, Xuefeng Tang, Yan Wen, Liping Liu, S. Zhiyuan, Li huakangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-7301811/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-7301811/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-7301811/latest.pdfDirect OA link when available
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_topic.display_name | Atrial Fibrillation Management and Outcomes |
| related_works | https://openalex.org/W4387497383, https://openalex.org/W3183948672, https://openalex.org/W3173606202, https://openalex.org/W3110381201, https://openalex.org/W2948807893, https://openalex.org/W2935909890, https://openalex.org/W2899084033, https://openalex.org/W2778153218, https://openalex.org/W2758277628, https://openalex.org/W1531601525 |
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| publication_date | 2025-09-08 |
| publication_year | 2025 |
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