Key result
XGBoost model predicts left atrial thrombus risk in non-valvular AF with an AUC of ~0.96.
Why the study?
The study aimed to develop a machine learning model combining clinical data and TTE features to predict thrombosis risk in non-valvular atrial fibrillation, in contrast to commonly used clinical models.
Does an XGBoost machine learning model utilizing clinical and multimodal echocardiographic parameters improve the prediction of left atrial thrombus or spontaneous echo contrast compared to traditional logistic regression in patients with non-valvular atrial fibrillation?
Observational (n=402)
Single-blind
No
Does an XGBoost machine learning model utilizing clinical and multimodal echocardiographic parameters improve the prediction of left atrial thrombus or spontaneous echo contrast compared to traditional logistic regression in patients with non-valvular atrial fibrillation?
An XGBoost machine learning model incorporating clinical and multimodal echocardiographic parameters, particularly peak atrial longitudinal strain, accurately predicts left atrial thrombus or spontaneous echo contrast in patients with non-valvular atrial fibrillation.
Answers are grounded in this paper and cite their sources.
May aid LAT/SEC risk stratification in non-valvular AF; hypothesis-generating and requires external validation before clinical adoption.
This study aimed to develop a machine learning (ML) model utilizing clinical data and transthoracic echocardiography (TTE) features to predict thrombosis risk in non-valvular atrial fibrillation (NVAF), in contrast to commonly used clinical models. Between January 2020 and December 2023, 402 NVAF patients undergoing catheter ablation or left atrial appendage closure at the First Affiliated Hospital of Guangxi Medical University were prospectively enrolled. Among them, 142 patients (35.3%) exhibited left atrial thrombus or spontaneous echo contrast (LAT/SEC), including 37 cases (9.2%) of LAT, while 260 patients showed no evidence of LAT/SEC. The patients were split into training and validation sets at a ratio of 7:3, with clinical data, biochemical markers, and echocardiographic parameters collected to build the model. Model performance was assessed using accuracy, precision, recall, F1 score, and area under the curve (AUC). The Shapley additive explanations (SHAP) analysis was employed to investigate the contributions of variables. The eXtreme Gradient Boosting (XGBoost) model slightly outperformed the traditional logistic regression model in predicting thrombosis risk in NVAF patients, demonstrating better predictive capability than other ML algorithms, with an AUC of 0.959 (95% CI, 0.925–0.993, P < 0.05). Additionally, XGBoost offered greater clinical net benefit within a threshold probability range of 0.1 to 1.0. SHAP analysis highlighted decreased peak atrial longitudinal strain (PALS), hemodynamic abnormalities, and left atrium spherical remodeling as key factors associated with higher thrombosis risk in NVAF. Utilizing ML techniques to combine clinical risk factors and multimodal echocardiographic parameters can enhance thrombosis risk prediction in NVAF patients, with the XGBoost model showing superior accuracy among various ML algorithms. However, its clinical reliability still needs to be validated in larger, multicenter, prospective cohorts. Not applicable.
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Zeng et al. (2026) conducted an observational in Non-valvular atrial fibrillation (n=402). eXtreme Gradient Boosting (XGBoost) machine learning model vs. Logistic regression model was evaluated on Prediction of left atrial thrombus or spontaneous echo contrast (LAT/SEC) (AUC 0.959, 95% CI 0.925-0.993, p=<0.05). The eXtreme Gradient Boosting (XGBoost) model utilizing clinical and echocardiographic parameters predicted thrombosis risk in non-valvular atrial fibrillation patients with an AUC of 0.959.
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