ObjectiveTo systematically review the early clinical prediction value of machine learning (ML) for cardiac arrest (CA).MethodsPubMed, EMbase, WanFang Data and CNKI databases were electronically searched to retrieve all ML studies on predicting CA from January 2015 to February 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. The value of each model was evaluated based on the area under receiver operating characteristic curve (AUC) and accuracy.ResultsA total of 38 studies were included. In terms of data sources, 13 studies were based on public database, and other studies retrospectively collected clinical data, in which 21 directly predicted CA, 3 predicted CA-related arrhythmias, and 9 predicted sudden cardiac death. A total of 51 models had been adopted, among which the most popular ML methods included artificial neural network (n=11), followed by random forest (n=9) and support vector machine (n=5). The most frequently used input feature was electrocardiogram parameters (n=20), followed by age (n=12) and heart rate variability (n=10). Six studies compared the ML models with other traditional statistical models and the results showed that the AUC value of ML was generally higher than that in traditional statistical models.ConclusionsThe available evidence suggests that ML can accurately predict the occurrence of CA, and the performance is significantly superior to traditional statistical model in certain cases.
The pathological state of "epileptic heart" induced by epilepsy has shattered the perception that epilepsy is an isolated neurological disorder, reflecting the synergistic damage between chronic epilepsy and cardiac bioelectrical activity as well as mechanical function. Autonomic nervous system dysfunction is the core mediating link, which regulates the expression of myocardial ion channels and induces risks such as arrhythmias. In the long term, it also drives structural remodeling and functional impairment including left atrial enlargement and myocardial fibrosis. This kind of damage is influenced by factors such as classification of epilepsy, disease duration, and antiepileptic drugs—for instance, patients with drug resistant epilepsy or those on long-term polypharmacy suffer more significant damage. This article systematically reviews recent research progress, summarizes consensus and controversies, provides a basis for the precision management of cardiovascular risks in epilepsy patients, and contributes to reducing the incidence of SUDEP and improving long-term quality of life.