Machine Learning Model-Based Simple Clinical Information to Predict Decreased Left Atrial Appendage Flow Velocity

Abstract

Background: Transesophageal echocardiography (TEE) is the first technique of choice for evaluating the left atrial appendage flow velocity (LAAV) in clinical practice, which may cause some complications. Therefore, clinicians require a simple applicable method to screen patients with decreased LAAV. Therefore, we investigated the feasibility and accuracy of a machine learning (ML) model to predict LAAV. Method: The analysis included patients with atrial fibrillation who visited the general hospital of PLA and underwent transesophageal echocardiography (TEE) between January 2017 and December 2020. Three machine learning algorithms were used to predict LAAV. The area under the receiver operating characteristic curve (AUC) was measured to evaluate diagnostic accuracy. Results: Of the 1039 subjects, 125 patients (12%) were determined as having decreased LAAV (LAAV < 25 cm/s). Patients with decreased LAAV were fatter and showed a higher prevalence of persistent AF, heart failure, hypertension, diabetes and stroke, and the decreased LAAV group had a larger left atrium diameter and a higher serum level of NT-pro BNP than the control group (p < 0.05). Three machine-learning models (SVM model, RF model, and KNN model) were developed to predict LAAV. In the test data, the RF model performs best (R = 0.608, AUC = 0.89) among the three models. A fivefold cross-validation scheme further verified the predictive ability of the RF model. In the RF model, NT-proBNP was the factor with the strongest impact. Conclusions: A machine learning model (Random Forest model)-based simple clinical information showed good performance in predicting LAAV. The tool for the screening of decreased LAAV patients may be very helpful in the risk classification of patients with a high risk of LAA thrombosis.

Deep learning for transesophageal echocardiography view classification

“…Tasken et al, was able to automatically quantify the mitral annular plane systolic excursion (MAPSE), using a pipeline that included a view classification task prior to the quantification of MAPSE, highlighting the utility and necessity of TEE view classification for downstream machine learning tasks. Thalappillil et al 21 and Li et al 22 used quantitative measurements derived from TEE videos, rather than the TEE image data itself, as the input variables or output labels in their machine learning algorithms. Our group is the first to apply machine learning techniques to clinically-acquired intraoperative and intraprocedural TEE image data and the first to add structure to the data contained within these comprehensive clinical TEE exams.…”

Primer on Machine Learning in Electrophysiology

“…Random forest algorithms are a popular approach in cardiac EP and have been used in studies ranging from predicting left atrial appendage flow velocity to predicting 30-day mortality post ST-elevation myocardial infarction. [47][48][49][50][51][52][53][54][55] Random forest models were useful in these studies because a decision path to the correct prediction could be found in the majority of distinct trees. Random forest models have been shown to be particularly useful in providing interpretability due to each split being a defined threshold that offers insight.…”