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April 12, 2026BMC Cardiovascular DisordersOpen Access

Development of a nomogram with machine learning-assisted feature selection for predicting left atrial appendage thrombosis and severe spontaneous echo contrast in patients with non-paroxysmal atrial fibrillation

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Why the study?

Does a machine learning-derived nomogram improve the prediction of LAA thrombosis or severe SEC compared to the CHA2DS2-VASc score in patients with non-paroxysmal NVAF?

Population

327 patients with non-paroxysmal non-valvular atrial fibrillation (NVAF)

Comparison

Nomogram constructed using machine… vs CHA2DS2-VASc score

Design

Cohort

Authors

HWHao WangJFJunyu FanBZBingyuan Zhou

Discussion

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Overview

A novel machine learning-derived nomogram significantly improves the prediction of LAA thrombosis and severe SEC compared to the standard CHA2DS2-VASc score in patients with non-paroxysmal NVAF.

Key Points

  • The aim is to develop a nomogram using machine learning for predicting LAA thrombosis and severe SEC in non-paroxysmal NVAF patients.
  • Retrospective enrollment of 327 patients with non-paroxysmal NVAF
  • Collection of 34 clinical and echocardiographic variables
  • Application of three machine learning approaches (SVM-RFE, Boruta, LASSO) for feature selection
  • Evaluation of model performance using ROC curves, calibration curves, DCA, and CIC
  • Reclassification analysis performed using NRI and IDI
  • LAA thrombosis or severe SEC detected in 12.2% of patients
  • AUC values for machine learning algorithms: 0.88 (SVM-RFE), 0.89 (LASSO), 0.89 (Boruta)
  • Final nomogram AUC of 0.88, significantly higher than CHA2DS2-VASc score (AUC = 0.68)
  • High net benefits shown in DCA and CIC analysis across various threshold probabilities
  • NRI of 0.957 and IDI of 0.254 compared to CHA2DS2-VASc score

Structured PICO

Does a machine learning-derived nomogram improve the prediction of LAA thrombosis or severe SEC compared to the CHA2DS2-VASc score in patients with non-paroxysmal NVAF?

P
Population
327 patients with non-paroxysmal non-valvular atrial fibrillation (NVAF)
I
Intervention
Nomogram constructed using machine learning-assisted feature selection (SVM-RFE, Boruta, and LASSO)
C
Comparator
CHA2DS2-VASc score
O
Outcome
Detection of left atrial appendage (LAA) thrombosis or severe spontaneous echo contrast (SEC)surrogate

A novel machine learning-derived nomogram significantly improves the prediction of LAA thrombosis and severe SEC compared to the standard CHA2DS2-VASc score in patients with non-paroxysmal NVAF.

Limitations

  • Exploratory study
  • Clinical reliability requires further validation in larger, independent, prospective cohorts

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69db375f4fe01fead37c54echttps://doi.org/10.1186/s12872-026-05805-w
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