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Prediction of Sudden Cardiac Death Manifesting With VF/VT
Quick Takes
- A clinical algorithm for prediction of VF/VT (VFRisk) constructed with 13 clinical, ECG, and echocardiographic variables had very good discrimination in the training dataset as well as the internal validation dataset and external dataset.
- These data suggest that a targeted identification of high-risk candidates with potentially treatable SCA may enhance the effectiveness of sudden death prevention, especially among individuals with mid-range or preserved LVEF.
- Given limitations of the current study design, further evaluation in randomized clinical trials is indicated before potential clinical application.
Study Questions:
What is the utility of a novel clinical prediction algorithm for avertable sudden cardiac death?
Methods:
The investigators prospectively ascertained subjects with out-of-hospital sudden cardiac arrest (SCA) presenting with documented ventricular fibrillation (VF) or pulseless ventricular tachycardia (VT) (33% of total cases) from the Portland, Oregon, metro area with a population of approximately 1 million residents (n = 1,374, 2002–2019). Comparisons of lifetime clinical records were conducted with a control group (n = 1,600) with approximately 70% coronary disease prevalence. Prediction models were constructed from a training dataset using backwards stepwise logistic regression and applied to an internal validation dataset. Receiver operating characteristic curves (C statistic) were used to evaluate model discrimination. External validation was performed in a separate geographically distinct population (Ventura County, California, population approximately 850,000, 2015–2020).
Results:
A clinical algorithm (VFRisk) constructed with 13 clinical, electrocardiographic (ECG), and echocardiographic variables had very good discrimination in the training dataset (C statistic = 0.808; 95% confidence interval [CI], 0.774-0.842) and was successfully validated in internal (C statistic = 0.776; 95% CI, 0.725 - 0.827) and external (C statistic = 0.782; 95% CI, 0.718-0.846) datasets. The algorithm substantially outperformed the left ventricular ejection fraction (LVEF) ≤35% (C statistic = 0.638) and performed well across the LVEF spectrum.
Conclusions:
The authors concluded that an algorithm for prediction of SCA manifesting with VF/VT was successfully constructed using widely available clinical and noninvasive markers.
Perspective:
This study reports development of a clinical algorithm for prediction of VF/VT (VFRisk) from individuals who suffered out-of-hospital SCA with documented VF/pulseless VT and control subjects of whom the majority (67%) had significant coronary artery disease. VFRisk, constructed with 13 clinical, ECG, and echocardiographic variables had very good discrimination in the training dataset as well as the internal validation dataset and was also externally validated successfully, in a geographically distinct population. These data suggest that a targeted identification of high-risk candidates with potentially treatable SCA may enhance the effectiveness of sudden death prevention, especially among individuals with mid-range or preserved LVEF. Given limitations of the current study design, further evaluation in randomized clinical trials is indicated before potential clinical application.
Clinical Topics: Arrhythmias and Clinical EP, Heart Failure and Cardiomyopathies, Noninvasive Imaging, Prevention, Atherosclerotic Disease (CAD/PAD), Implantable Devices, SCD/Ventricular Arrhythmias, Atrial Fibrillation/Supraventricular Arrhythmias, Acute Heart Failure, Echocardiography/Ultrasound
Keywords: Arrhythmias, Cardiac, Coronary Artery Disease, Death, Sudden, Cardiac, Diagnostic Imaging, Echocardiography, Electrocardiography, Heart Arrest, Heart Failure, Myocardial Ischemia, Out-of-Hospital Cardiac Arrest, Risk Assessment, Secondary Prevention, Stroke Volume, Tachycardia, Ventricular, Ventricular Fibrillation, Ventricular Function, Left
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