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Predicting Thromboembolism in Hospitalized Patients with Ventricular Thrombus
Qing Yang1,2, Xin Quan1,3, Xinyue Lang1,4
1National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China.
Reviews in Cardiovascular Medicine
|July 30, 2024
Summary
This study developed a prediction model to identify hospitalized patients with ventricular thrombus at high risk of thromboembolism. The model identified key factors to forecast thromboembolism probability, aiding early detection and intervention.
Area of Science:
- Cardiology
- Medical Informatics
- Clinical Prediction Models
Background:
- Thromboembolism significantly increases mortality and morbidity in patients with ventricular thrombus.
- Early detection of thromboembolism is crucial for managing ventricular thrombus patients.
- Predicting thromboembolism risk can improve patient outcomes.
Purpose of the Study:
- To identify patient characteristics that predict thromboembolism in hospitalized ventricular thrombus patients.
- To develop and validate a predictive model for thromboembolism risk.
- To aid in the early detection and management of thromboembolism.
Main Methods:
- Retrospective cohort study including 338 hospitalized patients with ventricular thrombus.
- Lasso regression and multivariate logistic regression were used to select predictors.
- A nomogram risk prediction model was developed and validated.
Main Results:
- A seven-factor prediction model demonstrated good performance (AUC 0.930 training, 0.839 validation).
- Protuberant thrombus and diabetes mellitus history were associated with high thromboembolism risk.
- High left ventricular ejection fraction and no antiplatelet therapy were associated with low risk.
Conclusions:
- A Lasso-algorithm-based prediction model effectively forecasts thromboembolism risk in ventricular thrombus patients.
- The model aids in predicting the probability of thromboembolism.
- Further prospective studies are needed for model development and validation.
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