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Development of a machine learning-based model to predict major adverse events after surgery for type A aortic dissection complicated by malnutrition.
Xie, Lin-Feng; Lin, Xin-Fan; Xie, Yu-Ling; Wu, Qing-Song; Qiu, Zhi-Huang; Lan, Quan; Chen, Liang-Wan.
Afiliación
  • Xie LF; Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
  • Lin XF; Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China.
  • Xie YL; Fujian Provincial Center for Cardiovascular Medicine, Fuzhou, Fujian, China.
  • Wu QS; Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
  • Qiu ZH; Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China.
  • Lan Q; Fujian Provincial Center for Cardiovascular Medicine, Fuzhou, Fujian, China.
  • Chen LW; Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
Front Nutr ; 11: 1428532, 2024.
Article en En | MEDLINE | ID: mdl-39027660
ABSTRACT

Objective:

This study aims to develop a predictive model for the risk of major adverse events (MAEs) in type A aortic dissection (AAAD) patients with malnutrition after surgery, utilizing machine learning (ML) algorithms.

Methods:

We retrospectively collected clinical data from AAAD patients with malnutrition who underwent surgical treatment at our center. Through least absolute shrinkage and selection operator (LASSO) regression analysis, we screened for preoperative and intraoperative characteristic variables. Based on the random forest (RF) algorithm, we constructed a ML predictive model, and further evaluated and interpreted this model.

Results:

Through LASSO regression analysis and univariate analysis, we ultimately selected seven feature variables for modeling. After comparing six different ML models, we confirmed that the RF model demonstrated the best predictive performance in this dataset. Subsequently, we constructed a model using the RF algorithm to predict the risk of postoperative MAEs in AAAD patients with malnutrition. The test set results indicated that this model has excellent predictive efficacy and clinical applicability. Finally, we employed the Shapley additive explanations (SHAP) method to further interpret the predictions of this model.

Conclusion:

We have successfully constructed a risk prediction model for postoperative MAEs in AAAD patients with malnutrition using the RF algorithm, and we have interpreted the model through the SHAP method. This model aids clinicians in early identification of high-risk patients for MAEs, thereby potentially mitigating adverse clinical outcomes associated with malnutrition.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Nutr Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Nutr Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza