Prediction of Sudden Cardiac Death Manifesting With Documented Ventricular Fibrillation or Pulseless Ventricular Tachycardia

Abstract

Objectives: This study aimed to develop a novel clinical prediction algorithm for avertable sudden cardiac death.

Background: Sudden cardiac death manifests as ventricular fibrillation (VF)/ ventricular tachycardia (VT) potentially treatable with defibrillation, or nonshockable rhythms (pulseless electrical activity/asystole) with low likelihood of survival. There are no available clinical risk scores for targeted prediction of VF/VT.

Methods: Subjects with out-of-hospital sudden cardiac arrest presenting with documented VF or pulseless VT (33% of total cases) were ascertained prospectively from the Portland, Oregon, metro area with population ≈1 million residents (n = 1,374, 2002-2019). Comparisons of lifetime clinical records were conducted with a control group (n = 1,600) with ≈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 ≈850,000, 2015-2020).

Results: A clinical algorithm (VFRisk) constructed with 13 clinical, electrocardiogram, and echocardiographic variables had very good discrimination in the training dataset (C statistic = 0.808; [95% 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: An algorithm for prediction of sudden cardiac arrest manifesting with VF/VT was successfully constructed using widely available clinical and noninvasive markers. These findings have potential to enhance primary prevention, especially in patients with mid-range or preserved LVEF.

Keywords: cardiac arrest; prevention; risk stratification; sudden cardiac death; ventricular fibrillation.

Figure 1.. Cases and Controls in the Training, Internal Validation, and External Validation Datasets for Prediction of SCA Presenting With VF/VT

The analysis was conducted among sudden cardiac arrest (SCA) cases with shockable rhythms (VF/VT). Cases with nonshockable presenting rhythm (PEA/asystole) or with missing rhythm information were excluded. The analysis dataset from the Oregon SUDS discovery population was divided into the training dataset (67%) for development of the prediction models, and the validation dataset (33%) to test the prediction models. Prediction models were externally validated in the geographically distinct Ventura PRESTO study. ECG = electrocardiogram; PEA = pulseless electrical activity; PRESTO = Prediction of Sudden Death in Multi-ethnic Communities; SUDS = Sudden Unexpected Death Study; VF = ventricular fibrillation; VT = ventricular tachycardia.

Figure 2.. ROC curves for the different VF/VT prediction models in the training dataset based on the type of variables included.

Prediction models shown include (model 0) left ventricular ejection fraction (EF) as the only predictor; (model 1) clinical (medical history) predictors only; (model 2) clinical plus ECG predictors; (model 3) clinical plus echocardiogram predictors; and (model 4) clinical, ECG, and echocardiogram predictors. N shown in legend is the number of subjects in the dataset for each model. Echo, echocardiogram; ROC, receiver operating curve; other abbreviations as in Figure 1.

Figure 3.. Distribution of VFRisk Score in Cases and Control

Histogram showing distribution of (A) VFRisk score among cases and (B) controls in the training, internal validation, and external validation datasets.

Figure 3.. Distribution of VFRisk Score in Cases and Control

Histogram showing distribution of (A) VFRisk score among cases and (B) controls in the training, internal validation, and external validation datasets.

Figure 4.. Performance of the VFRisk Score in Distinct Population Subgroups in the combined Training and Internal Validation Dataset

Odds ratios and 95% CIs by VFRisk quartile, by age, left ventricular (LV) ejection fraction (EF), and sex, in the training dataset.