Sudden Cardiac Death Risk Stratification — An Update

Liew, Reginald

doi:10.15420/ecr.2015.10.2.118

Cited by 5 publications

(2 citation statements)

References 73 publications

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“…Previous studies reported that several ECG features (e.g., heart rate variability, T-wave alternance, early repolarization, late potential, and atrial fibrillation) are associated with SCD and can provide independent values beyond LV dysfunction. 26,27 In fact, the Grad-CAM showed that our neural network model looked at the QRS wave and T wave, which seemed to focus on early repolarization, late potential, and T-wave alternance. Our ECG-based AI algorithm likely integrates specific features associated with SCD and can be accurately applied to a broader spectrum of patients with HF than classical predictive models.…”

Section: Discussionmentioning

confidence: 99%

Electrocardiography-Based Prediction of Sudden Cardiac Death in Heart Failure Patients: Application of Artificial Intelligence

Goto

Niimi

Katsumata

et al. 2022

Preprint

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BackgroundAlthough predicting sudden cardiac death (SCD) in patients with heart failure (HF) is critical, the current predictive model is suboptimal. Electrocardiography-based artificial intelligence (ECG-AI) algorithms may better stratify risk. We assessed whether the ECG-AI index established here could better predict SCD in HF and whether the ECG-AI index and conventional predictors of SCD can improve SCD stratification.MethodsIn a prospective observational study, four tertiary care hospitals in metropolitan Tokyo that enrolled 2,559 patients hospitalized with HF who were successfully discharged after acute decompensation. ECG data collected during the index hospitalization were extracted from the hospitals’ electronic medical record systems. The ECG-AI index is the output from an AI model that was trained to predict the risk of SCD based on ECG input. The association between ECG-AI index and SCD was evaluated with adjustment for left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and competing risk of non-SCD. The outcome measure was a composite of SCD and implantable cardioverter-defibrillator activation. The ECG-AI index was established using a derivation (hospital A) and validation cohort (hospital B), and its ability was evaluated in a test cohort (hospitals C and D).ResultsThe ECG-AI index plus classical predictive guidelines (i.e., LVEF ≤ 35%, NYHA class II–III) significantly improved the discriminative value of SCD (area under the receiver operating characteristic curve, 0.66 vs. 0.59; p=0.017; Delong’s test) with good calibration (p=0.11; Hosmer–Lemeshow test) and improved net reclassification (36%; 95% confidence interval, 9%–64%; p=0.009). The Fine-Gray model considering the competing risk of non-SCD demonstrated that the ECG-AI index was independently associated with SCD (adjusted sub-distributional hazard ratio, 1.25; 95% confidence interval, 1.04–1.49; p=0.015). An increased proportional risk of SCD vs. non-SCD with increasing ECG-AI index was also observed (low, 16.7%; intermediate, 18.5%; high, 28.7% risk; p for trend = 0.023). Similar findings were observed in patients aged ≤75 years with a non-ischemic etiology and an LVEF >35%.ConclusionsAmong patients with HF, ECG-based AI significantly improved the SCD risk stratification compared to the conventional indication for implantable cardioverter-defibrillators inclusive of LVEF and NYHA class.

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Section: Discussionmentioning

confidence: 99%

Electrocardiography-Based Prediction of Sudden Cardiac Death in Heart Failure Patients: Application of Artificial Intelligence

Goto

Niimi

Katsumata

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…CVD and sudden cardiac deaths rank among the leading causes of morbidity and mortality worldwide and are therefore acknowledged as major healthcare problems [ 7 , 8 ]. The identification of early stages and the knowledge about risk factors is of tremendous importance for the prevention and prophylaxis of this condition [ 9 , 10 ]. Early lifestyle modifications and pharmacological therapies can reduce the progression of CVD with its potentially deleterious consequences.…”

Section: Introductionmentioning

confidence: 99%

Atherosclerosis of the iliac arteries for the prediction of erectile dysfunction and epistaxis in men undergoing abdominal CT scan

Schmid,

Mergen,

Bärlocher

et al. 2023

BMC Urol

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Background To investigate the association between erectile dysfunction (ED) as well as epistaxis (ES) in relation to the extent of iliac atherosclerosis. Methods In this retrospective cross-sectional study, all consecutive male patients treated at our institution from 01/2016 to 12/2020 undergoing abdominal CT scan were evaluated. Patients (n = 1272) were invited by mail to participate in the study in returning two questionnaires for the evaluation of ED (IIEF-5) and ES. Patients who returned filled-in questionnaires within a 3-month deadline were included in the study. The extent of atherosclerosis in the common iliac artery (CIA) and the internal iliac artery (IIA) was assessed by calcium scoring on unenhanced CT. Stratification of results was performed according to reported IIEF-5 scores and consequential ED groups. Results In total, 437 patients (34.4% of contacted) met the inclusion criteria. Forty-two patients did not fulfill predefined age requirements (< 75 years) and 120 patients had to be excluded as calcium scoring on nonenhanced CT was not feasible. Finally, 275 patients were included in the analysis and stratified into groups of “no-mild” (n = 146) and “moderate-severe” (n = 129) ED. The calcium score (r=-0.28, p < 0.001) and the number of atherosclerotic lesions (r=-0.32, p < 0.001) in the CIA + IIA showed a significant negative correlation to the IIEF-5 score, respectively. Patients differed significantly in CIA + IIA calcium score (difference: 167.4, p < 0.001) and number of atherosclerotic lesions (difference: 5.00, p < 0.001) when belonging to the “no-mild” vs. “moderate-severe” ED group, respectively. A multivariable regression model, after adjusting for relevant baseline characteristics, showed that the number of atherosclerotic CIA + IIA lesions was an independent predictor of ED (OR = 1.05, p = 0.036), whereas CIA + IIA calcium score was not (OR = 1.00031, p = 0.20). No relevant correlation was found between ES episodes and IIEF-5 scores (r=-0.069, p = 0.25), CIA + IIA calcium score (r=-0.10, p = 0.87) or number of atherosclerotic CIA + IIA lesions (r=-0.032, p = 0.60), respectively. Conclusions The number of atherosclerotic lesions in the iliac arteries on nonenhanced abdominal CT scans is associated with the severity of ED. This may be used to identify subclinical cardiovascular disease and to quantify the risk for cardiovascular hazards in the future. Trial registration BASEC-Nr. 2020 − 01637.

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New Approaches to Stratification of Patients by the Level of Sudden Cardiac Death Risk Using the Data on Energies of Cardiac Micropotentials Obtained by Nanosensor-Based Hardware and Software Complex

Avdeeva

Максимов

Guo

et al. 2021

Progress in Material Science and Engineering

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Sudden Cardiac Death Risk Stratification — An Update

Cited by 5 publications

References 73 publications

Electrocardiography-Based Prediction of Sudden Cardiac Death in Heart Failure Patients: Application of Artificial Intelligence

Electrocardiography-Based Prediction of Sudden Cardiac Death in Heart Failure Patients: Application of Artificial Intelligence

Atherosclerosis of the iliac arteries for the prediction of erectile dysfunction and epistaxis in men undergoing abdominal CT scan

New Approaches to Stratification of Patients by the Level of Sudden Cardiac Death Risk Using the Data on Energies of Cardiac Micropotentials Obtained by Nanosensor-Based Hardware and Software Complex

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