Prediction of arrhythmia after intervention in children with atrial septal defect based on random forest

Sun, Hongxiao; Liu, Yuhai; Song, Bo; Cui, Xiaowen; Luo, Gang; Pan, Silin

doi:10.1186/s12887-021-02744-7

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…These favorable changes remained stable during a mid-term follow-up. This improvement of the electrical intracardiac "milieu" could significantly reduce the arrhythmic risk of these patients and potentially prevent the arrhythmic burden recorded early after device implantation [11,12,26,[31][32][33][34][35][36][37][38]. This positive electrical remodeling and the decreased arrhythmic complications could also be supported by the concurrent atrial and ventricular geometry changes.…”

Section: Discussionmentioning

confidence: 99%

Mid-Term Electrical Remodeling after Percutaneous Atrial Septal Defect Closure with GCO Device in a Pediatric Population

Fumanelli,

Garibaldi,

Castaldi

et al. 2023

JCM

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Background and aim: The GORE® CARDIOFORM (GCO) septal occluder is an atrial septal defect/patent foramen ovale closure device with theoretical advantages over other commercialized devices thanks to its softness and anatomical compliance. Our aim was to evaluate the short- and medium-term electrocardiographic changes after percutaneous ASD closure with GCO in a pediatric population. Methods: We enrolled 39 patients with isolated ASD submitted to trans-catheter closure from January 2020 to June 2021. ECG was performed before, at 24 h and 6 months after the procedure. P wave dispersion, QTc and QTc dispersion were calculated. ECG Holter was recorded at 6 months after implantation. Results: Patients’ age and body surface area (BSA) were 8.2 ± 4.2 years and 1.0 ± 0.3 m2 respectively. At the baseline, mean P wave dispersion was 40 ± 15 msec and decreased at 24 h (p < 0.002), without any further change at 6 months. At 24 h, PR conduction and QTc dispersion significantly improved (p = 0.018 and p < 0.02 respectively), while the absolute QTc value considerably improved after 6 months. During mid-term follow-up, QTc dispersion remained stable without a significant change in PR conduction. The baseline cardiac frequency was 88.6 ± 12.6 bpm, followed by a slight reduction at 24 h, with a further amelioration at 6 months after the procedure (87.3 ± 14.2, p = 0.9 and 81.0 ± 12.7, p = 0.009, respectively). After device deployment, two patients developed transient, self-limited junctional rhythm. One of them needed a short course of Flecainide for atrial ectopic tachycardia. No tachy/brady-arrhythmias were recorded at the 6-month follow-up. ASD closure resulted in a marked decrease in right heart volumes and diameters at 6 months after percutaneous closure. Conclusions: Percutaneous ASD closure with the GCO device results in significant, sudden improvement of intra-atrial, atrio-ventricular and intraventricular electrical homogeneity. This benefit persists unaltered over a medium-term follow-up. These electrical changes are associated with a documented positive right heart volumetric remodeling at mid-term follow-up.

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Section: Discussionmentioning

confidence: 99%

Mid-Term Electrical Remodeling after Percutaneous Atrial Septal Defect Closure with GCO Device in a Pediatric Population

Fumanelli,

Garibaldi,

Castaldi

et al. 2023

JCM

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“…They were used to classify the outcome variables. The subjects in the case and control groups were randomly divided into the training set and the test set according to the percentage of 7:3, and the two datasets were independent [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

Effective Macrosomia Prediction Using Random Forest Algorithm

Wang

et al. 2022

IJERPH

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(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.

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“…Furthermore, ML techniques have also studied in the prediction of postoperative arrhythmias following atrial septal defect closure. In this setting, a prediction model based on synthetic minority oversampling technique algorithm and the random forest was found to predict arrhythmias with excellent accuracy in a pediatric population [ 52 ]. This is further supported by Guo et al, which used a combination of ML techniques including support vector machine (SVM), random forest, naïve Bayes and adaptive boost to predict postoperative blood coagulation function for children with congenital heart disease [ 53 ].…”

Section: Specific Patient Populationsmentioning

confidence: 99%

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

Chung¹,

Bazoukis

Lee³

et al. 2022

Int J Arrhythm

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Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

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Prediction of arrhythmia after intervention in children with atrial septal defect based on random forest

Cited by 10 publications

References 26 publications

Mid-Term Electrical Remodeling after Percutaneous Atrial Septal Defect Closure with GCO Device in a Pediatric Population

Mid-Term Electrical Remodeling after Percutaneous Atrial Septal Defect Closure with GCO Device in a Pediatric Population

Effective Macrosomia Prediction Using Random Forest Algorithm

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

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