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Article summary:
1. This study investigated the feasibility and accuracy of a machine learning (ML) model to predict left atrial appendage flow velocity (LAAV).
2. Three ML algorithms were used to predict LAAV, with the Random Forest model performing best with an R value of 0.608 and an AUC of 0.89.
3. NT-proBNP was found to be the factor with the strongest impact on predicting LAAV, suggesting that it may be a useful tool for screening patients with decreased LAAV who are at high risk of thrombosis.
Article analysis:
The article is generally reliable and trustworthy in its reporting of the research findings, as it provides detailed information about the methods used in the study, including patient selection criteria, data collection methods, and machine learning models employed. The authors also provide clear explanations of their results and discuss potential implications for clinical practice.
However, there are some potential biases that should be noted in this article. First, since this is a retrospective study based on data collected from hospital records, there may be some missing or incomplete data which could affect the accuracy of the results. Second, since only patients from one hospital were included in this study, it is possible that there may be selection bias due to differences between hospitals in terms of patient demographics or other factors which could influence the results. Third, although NT-proBNP was found to be a strong predictor of LAAV in this study, further research is needed to confirm its utility as a screening tool for decreased LAAV risk. Finally, although three machine learning models were used in this study, only one (the Random Forest model) was found to have good predictive ability; thus further research is needed to determine if other models can achieve similar performance levels.