Holter Recordings at Initial Assessment for Long QT Syndrome: Relationship to Genotype Status and Cardiac Events

Holter Electrocardiographic Approach to Predicting Outcomes of Pediatric Patients With Long QT Syndrome

BACKGROUND

This study was performed to clarify the clinical findings of pediatric patients diagnosed with long QT syndrome (LQTS) through electrocardiographic screening programs and to predict their outcome using Holter electrocardiographic approaches.

METHODS AND RESULTS

This retrospective study included pediatric patients with a Schwartz score of ≥3.5 who visited the National Hospital Organization Kagoshima Medical Center between April 2005 and March 2019. Resting 12-lead and Holter electrocardiograms were recorded at every visit. The maximum resting QTc and maximum Holter QTc values among all recordings were used for statistical analyses. To test the prognostic value of QTc for the appearance of cardiac events after the first hospital visit, receiver operating characteristic curves were used to calculate the area under the curve (AUC). Among 207 patients, 181 (87%) were diagnosed through screening programs. The prevalence of cardiac events after the first hospital visit was 4% (8/207). Among QTc at diagnosis, maximum resting QTc, and maximum Holter QTc, only maximum Holter QTc value was a predictor (P=0.02) of cardiac events after the hospital visit in multivariate regression analysis. The AUC of the maximum Holter QTc was significantly superior to that of maximum resting QTc.

CONCLUSIONS

The maximum Holter QTc value can be used to predict the appearance of symptoms in pediatric patients with LQTS.

Artificial intelligence cloud platform improves arrhythmia detection from insertable cardiac monitors to 25 cardiac rhythm patterns through multi-label classification

BACKGROUND

Electrocardiogram (ECG) is the gold standard for the diagnosis of cardiac arrhythmias and other heart diseases. Insertable cardiac monitors (ICMs) have been developed to continuously monitor cardiac activity over long periods of time and to detect 4 cardiac patterns (atrial tachyarrhythmias, ventricular tachycardia, bradycardia, and pause). However, interpretation of ECG or ICM subcutaneous ECG (sECG) is time-consuming for clinicians. Artificial intelligence (AI) classifies ECG and sECG with high accuracy in short times.

OBJECTIVE

To demonstrate whether an AI algorithm can expand ICM arrhythmia recognition from 4 to many cardiac patterns.

METHODS

We performed an exploratory retrospective study with sECG raw data coming from 20 patients wearing a Confirm Rx™ (Abbott, Sylmar, USA) ICM. The sECG data were recorded in standard conditions and then analyzed by AI (Willem™, IDOVEN, Madrid, Spain) and cardiologists, in parallel.

RESULTS

In nineteen patients, ICMs recorded 2261 sECGs in an average follow-up of 23 months. Within these 2261 sECG episodes, AI identified 7882 events and classified them according to 25 different cardiac rhythm patterns with a pondered global accuracy of 88%. Global positive predictive value, sensitivity, and F1-score were 86.77%, 83.89%, and 85.52% respectively. AI was especially sensitive for bradycardias, pauses, rS complexes, premature atrial contractions, and inverted T waves, reducing the median time spent to classify each sECG compared to cardiologists.

CONCLUSION

AI can process sECG raw data coming from ICMs without previous training, extending the performance of these devices and saving cardiologists' time in reviewing cardiac rhythm patterns detection.