Machine learning techniques for arrhythmic risk stratification: a review of the literature

[Digital precision medicine in rhythmology : Risk prediction of recurrences, sudden cardiac death, and outcome]

Digital precision medicine is gaining increasing importance in rhythmology, especially in the treatment of cardiac arrhythmias. This trend is driven by the advancing digitization in healthcare and the availability of large amounts of data from various sources such as electrocardiograms (ECGs), implants like pacemakers and implantable cardioverter-defibrillators (ICDs), as well as wearables like smartwatches and fitness trackers. Through the analysis of this data, physicians can develop more precise and individualized diagnoses and treatment strategies for patients with cardiac arrhythmias. For example, subtle changes in ECGs can be identified, indicating potentially dangerous arrhythmias. Genetic analyses and resulting large datasets also play an increasingly significant role, especially in hereditary ion channel disorders such as long QT syndrome (LQTS) and Brugada syndrome (BrS), as well as in lone atrial fibrillation (AF). Precision medicine enables the development of individualized treatment approaches tailored to the specific needs and risk factors of each patient. This can help improve screening strategies, reduce adverse events, and ultimately enhance the quality of life for patients. Technological advancements such as big data, artificial intelligence, machine learning, and predictive analytics play a crucial role in predicting the risk of arrhythmias and sudden cardiac death. These concepts enable more precise and personalized predictions and support physicians in the treatment and monitoring of their patients.

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

The Action Potential Clamp Technique as a Tool for Risk Stratification of Sinus Bradycardia Due to Loss-of-Function Mutations in HCN4: An In Silico Exploration Based on In Vitro and In Vivo Data

These days, in vitro functional analysis of gene variants is becoming increasingly important for risk stratification of cardiac ion channelopathies. So far, such risk stratification has been applied to SCN5A, KCNQ1, and KCNH2 gene variants associated with Brugada syndrome and long QT syndrome types 1 and 2, respectively, but risk stratification of HCN4 gene variants related to sick sinus syndrome has not yet been performed. HCN4 is the gene responsible for the hyperpolarization-activated 'funny' current If, which is an important modulator of the spontaneous diastolic depolarization underlying the sinus node pacemaker activity. In the present study, we carried out a risk classification assay on those loss-of-function mutations in HCN4 for which in vivo as well as in vitro data have been published. We used the in vitro data to compute the charge carried by If (Qf) during the diastolic depolarization phase of a prerecorded human sinus node action potential waveform and assessed the extent to which this Qf predicts (1) the beating rate of the comprehensive Fabbri-Severi model of a human sinus node cell with mutation-induced changes in If and (2) the heart rate observed in patients carrying the associated mutation in HCN4. The beating rate of the model cell showed a very strong correlation with Qf from the simulated action potential clamp experiments (R2 = 0.95 under vagal tone). The clinically observed minimum or resting heart rates showed a strong correlation with Qf (R2 = 0.73 and R2 = 0.71, respectively). While a translational perspective remains to be seen, we conclude that action potential clamp on transfected cells, without the need for further voltage clamp experiments and data analysis to determine individual biophysical parameters of If, is a promising tool for risk stratification of sinus bradycardia due to loss-of-function mutations in HCN4. In combination with an If blocker, this tool may also prove useful when applied to human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) obtained from mutation carriers and non-carriers.

Risk Factors in Japanese Drug Overdose Patients: Identifying Their Associations With Suicide Risk

Several suicide attempts presented at the emergency department are due to drug overdose associated with psychiatric disorders. We examined and identified the major risk factors among Japanese drug overdose patients and several close associations of suicide risk. We enrolled 101 patients who attempted suicide by drug overdose between January 2015 and April 2018, assessed their background using the SAD PERSONS scale, and performed association rule analysis to characterize the major risk factors and their associations. We identified three main nodes-depressive state, social support lacking, and no spouse-as considerable risk factors. Furthermore, we identified several close associations of suicide risk and their intensity; in cases with previous suicide attempts and ethanol abuse or substance use, a simultaneous social support lacking is likely. These findings align with previous studies that used conventional statistical analysis on suicide and suicide attempt risk and highlight its importance.

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.