Heart rate variability and microvolt T wave alternans changes during ajmaline test may predict prognosis in Brugada syndrome

Călburean, Paul-Adrian; Pannone, Luigi; Sorgente, Antonio; Gauthey, Anaïs; Monaco, Cinzia; Strazdas, Antanas; Almorad, Alexandre; Bisignani, Antonio; Bala, Gezim; Ramak, Robbert; Overeinder, Ingrid; Ströker, Erwin; Pappaert, Gudrun; Dooren, Sonia Van; Ravel, Thomy de; Meir, Mark La; Brugada, Pedro; Sieira, Juan; Chierchia, Gian‐Battista; Asmundis, Carlo de

doi:10.1007/s10286-023-00922-4

Cited by 5 publications

(4 citation statements)

References 54 publications

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“…When applied to physiological signals, wavelet analysis can reflect both time and frequency features, in contrast with Fourier transform, which can only reflect frequency features. 26 , 27 , 28 Feasibility of ECG rhythm analysis using wavelet transform was reported in a previous study. 29 Moreover, clinical application of ECG morphology analysis using wavelet transform was reported in previous studies by detecting left ventricular dysfunction.…”

Section: Discussionmentioning

confidence: 93%

Predicting and Recognizing Drug‐Induced Type I Brugada Pattern Using ECG‐Based Deep Learning

Călburean,

Pannone,

Monaco

et al. 2024

JAHA

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Background Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug‐induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and evaluate its performance in recognizing and predicting BrS diagnosis. Methods and Results Consecutive patients who underwent ajmaline testing for BrS following a standardized protocol were included. ECG tracings from baseline and during ajmaline were transformed using wavelet analysis and a deep convolutional neural network was separately trained to (1) recognize and (2) predict BrS type I pattern. The resultant networks are referred to as BrS‐Net. A total of 1188 patients were included, of which 361 (30.3%) patients developed BrS type I pattern during ajmaline infusion. When trained and evaluated on ECG tracings during ajmaline, BrS‐Net recognized a BrS type I pattern with an AUC‐ROC of 0.945 (0.921–0.969) and an AUC‐PR of 0.892 (0.815–0.939). When trained and evaluated on ECG tracings at baseline, BrS‐Net predicted a BrS type I pattern during ajmaline with an AUC‐ROC of 0.805 (0.845–0.736) and an AUC‐PR of 0.605 (0.460–0.664). Conclusions BrS‐Net, a deep convolutional neural network, can identify BrS type I pattern with high performance. BrS‐Net can predict from baseline ECG the development of a BrS type I pattern after ajmaline with good performance in an unselected population.

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Section: Discussionmentioning

confidence: 93%

Predicting and Recognizing Drug‐Induced Type I Brugada Pattern Using ECG‐Based Deep Learning

Călburean,

Pannone,

Monaco

et al. 2024

JAHA

Self Cite

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“…The performance analysis of the ML models showed that an accurate prognosis estimate was given from routine biomarkers. The performance in survival prediction was reflected not only by the AUC-ROC, but also by the AUC-PR, a better metric for imbalanced datasets (e.g., deceased patients were fewer than alive patients) [ 17 , 18 , 19 ].…”

Section: Discussionmentioning

confidence: 99%

Inflammatory Status Assessment by Machine Learning Techniques to Predict Outcomes in Patients with Symptomatic Aortic Stenosis Treated by Transcatheter Aortic Valve Replacement

Stan,

Călburean,

Drinkal

et al. 2023

Diagnostics

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(1) Background: Although transcatheter aortic valve replacement (TAVR) significantly improves long-term outcomes of symptomatic severe aortic stenosis (AS) patients, long-term mortality rates are still high. The aim of our study was to identify potential inflammatory biomarkers with predictive capacity for post-TAVR adverse events from a wide panel of routine biomarkers by employing ML techniques. (2) Methods: All patients diagnosed with symptomatic severe AS and treated by TAVR since January 2016 in a tertiary center were included in the present study. Three separate analyses were performed: (a) using only inflammatory biomarkers, (b) using inflammatory biomarkers, age, creatinine, and left ventricular ejection fraction (LVEF), and (c) using all collected parameters. (3) Results: A total of 338 patients were included in the study, of which 56 (16.5%) patients died during follow-up. Inflammatory biomarkers assessed using ML techniques have predictive value for adverse events post-TAVR with an AUC-ROC of 0.743 and an AUC-PR of 0.329; most important variables were CRP, WBC count and Neu/Lym ratio. When adding age, creatinine and LVEF to inflammatory panel, the ML performance increased to an AUC-ROC of 0.860 and an AUC-PR of 0.574; even though LVEF was the most important predictor, inflammatory parameters retained their value. When using the entire dataset (inflammatory parameters and complete patient characteristics), the ML performance was the highest with an AUC-ROC of 0.916 and an AUC-PR of 0.676; in this setting, the CRP and Neu/Lym ratio were also among the most important predictors of events. (4) Conclusions: ML models identified the CRP, Neu/Lym ratio, WBC count and fibrinogen as important variables for adverse events post-TAVR.

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“…mdpi.com/article/10.3390/diagnostics14070719/s1, Section S1: Study Inclusion/Exclusion Criteria; Section S2: CADRADS Definition; Section S3: Additional Metrics for the Model Performance; Section S4: Model Subgroup Performance; Section S5: Features. References [34,35] are cited in the Supplementary Materials. Funding: This research, including clinical trials, was funded by Analytics for Life Inc.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease

Burton,

Fathieh,

Nemati

et al. 2024

Diagnostics

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The current standard of care for coronary artery disease (CAD) requires an intake of radioactive or contrast enhancement dyes, radiation exposure, and stress and may take days to weeks for referral to gold-standard cardiac catheterization. The CAD diagnostic pathway would greatly benefit from a test to assess for CAD that enables the physician to rule it out at the point of care, thereby enabling the exploration of other diagnoses more rapidly. We sought to develop a test using machine learning to assess for CAD with a rule-out profile, using an easy-to-acquire signal (without stress/radiation) at the point of care. Given the historic disparate outcomes between sexes and urban/rural geographies in cardiology, we targeted equal performance across sexes in a geographically accessible test. Noninvasive photoplethysmogram and orthogonal voltage gradient signals were simultaneously acquired in a representative clinical population of subjects before invasive catheterization for those with CAD (gold-standard for the confirmation of CAD) and coronary computed tomographic angiography for those without CAD (excellent negative predictive value). Features were measured from the signal and used in machine learning to predict CAD status. The machine-learned algorithm achieved a sensitivity of 90% and specificity of 59%. The rule-out profile was maintained across both sexes, as well as all other relevant subgroups. A test to assess for CAD using machine learning on a noninvasive signal has been successfully developed, showing high performance and rule-out ability. Confirmation of the performance on a large clinical, blinded, enrollment-gated dataset is required before implementation of the test in clinical practice.

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Heart rate variability and microvolt T wave alternans changes during ajmaline test may predict prognosis in Brugada syndrome

Cited by 5 publications

References 54 publications

Predicting and Recognizing Drug‐Induced Type I Brugada Pattern Using ECG‐Based Deep Learning

Predicting and Recognizing Drug‐Induced Type I Brugada Pattern Using ECG‐Based Deep Learning

Inflammatory Status Assessment by Machine Learning Techniques to Predict Outcomes in Patients with Symptomatic Aortic Stenosis Treated by Transcatheter Aortic Valve Replacement

Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease

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