ArticleJournal of thoracic disease2022
Prediction model based on machine learning for short- and long-term adverse events in left atrial appendage closure.
Article in Journal of thoracic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors at 1 institution in 1 country.
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Abstract
Background: At present, the prediction of adverse events (AE) had practical significance in clinic and the accuracy of AE prediction model after left atrial appendage closure (LAAC) needed to be improved. To identify a good prediction model based on machine learning for short- and long-term AE after LAAC. Methods: In this study, 869 patients were included from the Department of Cardiovascular Medicine of Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital during 2017 and 2021. Univariate and multivariate analyses were conducted for short-term AE after LAAC to determine possible risk factors related with AE. We compared 8 machine learning algorithms for prediction short-term AE, and XGBoost was found to have the best performance. In addition, Cox-regression was used for long-term AE to find out the risk factors and establish a prediction model. Results: In univariate and multivariate analysis, body mass index (BMI) [odds ratio (OR) =0.91], congestive heart failure, hypertension, age ≥75 years, diabetes, stroke Conclusions: In short- and long-term AE, CHADS
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