Prediction of breath-holding spells based on electrocardiographic parameters using machine-learning model.

Autor: Khalilian MR; Department of Pediatrics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Tofighi S; Department of Cardiology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran., Attar EZ; Department of Pediatrics, Mofid Children Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Nikkhah A; Mofid Children Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Hajipour M; Hepatology and Nutrition Research Center, Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Ghazavi M; Department of Pediatrics, School of Medicine, Kashan University of Medical Sciences and Health Services, Kashan, Iran., Samimi S; Department of Cardiology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Jazyk: angličtina
Zdroj: Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc [Ann Noninvasive Electrocardiol] 2024 Jan; Vol. 29 (1), pp. e13093. Date of Electronic Publication: 2023 Nov 07.
DOI: 10.1111/anec.13093
Abstrakt: Background: Breath-holding spells (BHS) are common in infancy and early childhood and may appear like seizures. Factors such as autonomic dysfunction and iron deficiency anemia are thought to contribute to the incidence of BHS. In this study, electrocardiographic (ECG) parameters of patients with BHS were compared to those of healthy, normal children. Logistic regression and machine-learning (ML) models were then created to predict these spells based on ECG characteristics.
Methods: In this case-control study, 52 BHS children have included as the case and 150 healthy children as the control group. ECG was taken from all children along with clinical examinations. Multivariate logistic regression model was used to predict BHS occurrence based on ECG parameters. ML model was trained and validated using the Gradient-Boosting algorithm, in the R programming language.
Results: In BHS and control groups, the average age was 11.90 ± 6.63 and 11.33 ± 6.17 months, respectively (p = .58). Mean heart rate, PR interval, and QRS interval on ECGs did not differ significantly between the two groups. BHS patients had significantly higher QTc, QTd, TpTe, and TpTe/QT (all p-values < .001). Evaluation of the ML model for prediction of BHS, fitting on the testing data showed AUC, specificity, and sensitivity of 0.94, 0.90, and 0.94 respectively.
Conclusion: There are repolarization changes in patients with BHS, as the QTc, QTd, TpTe, and TpTe/QT ratio were significantly higher in these patients, which might be noticeable for future arrhythmia occurrence. In this regard, we developed a successful ML model to predict the possibility of BHS in suspected subjects.
(© 2023 The Authors. Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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