Machine learning-based assessments of perioperative features in determining long-term conduct block post-transcatheter closure of ventricular septal defect.

BACKGROUND: Currently, studies have shown that this procedure of transcatheter closure of ventricular septal defect can lead to a range of adverse complications, especially for the threatening heart conduct block. However, it had not been fully analysed that whether early alternations of electrocardiogram, including vectorcardiogram, could determine the long-term persistent heart conduct block. METHODS: A retrospective study was conducted to develop a Random Forest-based prediction model for the postoperative of the transcatheter interventional closure. The study encompassed data preprocessing, model training, and assessment of feature importance. RESULT: We assessed the predictive value of parameters derived from defect anatomical features, interventional closure data, and ECG+VCG changes measured preoperatively, on postoperative days 1 and 3, and at 1 month after closure. The area under receiver operating characteristic curve values of such evaluations on the test sets were 0.9974, 0.9592, 0.9977, 0.9985, 0.9957, and 0.9977, recalls were 98.35%, 83.39%, 99.57%, 98.04%, 97.80%, and 99.58%. In the prediction model, the importance of features was ranked. We found that the diameter and number of occluder used in surgery and the size of left atrium and ventricle were the primary factors for predicting arrhythmia after surgery. For electrocardiogram data, significant parameters include RV5 voltage and the R-T angle of the sagittal plane vectorcardiogram before the treatment, T-wave, R-wave, and QRS duration in early days after operation and RV1 voltage, R-wave axis during 1-month follow-up. CONCLUSION: Electrocardiogram information during the perioperative period of interventional closure could be used to effectively predict the occurrence and prognosis of postoperative arrhythmia.