Key result
A 24-hour mobile cardiac telemetry model predicts subsequent VT episodes with ~0.76 AUC.
Why the study?
Ventricular tachycardia occurs intermittently and unpredictably with potentially lethal consequences, motivating the derivation of a risk prediction model for VT episodes using an abbreviated 24-hour recording.
Does a risk prediction model based on the first 24 hours of mobile cardiac telemetry predict ventricular tachycardia episodes ≥10 beats in patients undergoing extended monitoring?
Observational (n=19,781)
Does a risk prediction model based on the first 24 hours of mobile cardiac telemetry predict ventricular tachycardia episodes ≥10 beats in patients undergoing extended monitoring?
A risk prediction model using the first 24 hours of mobile cardiac telemetry can accurately identify patients at high risk for near-term ventricular tachycardia, potentially guiding triage for extended monitoring.
May support early VT risk stratification on telemetry; hypothesis-generating pending prospective validation.
Background: Ventricular tachycardia (VT) occurs intermittently, unpredictably, and has potentially lethal consequences. Objective: Our aim was to derive a risk prediction model for VT episodes ≥10 beats detected on 30-day mobile cardiac telemetry based on the first 24 hours of the recording. Methods: We included patients who were monitored for 2 to 30 days in the United States using full-disclosure mobile cardiac telemetry, without any VT episode ≥10 beats on the first full recording day. An elastic net prediction model was derived for the outcome of VT ≥10 beats on monitoring days 2 to 30. Potential predictors included age, sex, and electrocardiographic data from the first 24 hours: heart rate; premature atrial and ventricular complexes occurring as singlets, couplets, triplets, and runs; and the fastest rate for each event. The population was randomly split into training (70%) and testing (30%) samples. Results: In a population of 19,781 patients (mean age 65.3 ± 17.1 years, 43.5% men), with a median recording time of 18.6 ± 9.6 days, 1510 patients had at least 1 VT ≥10 beats. The prediction model had good discrimination in the testing sample (area under the receiver-operating characteristic curve 0.7584, 95% confidence interval 0.7340-0.7829). A model excluding age and sex had an equally good discrimination (area under the receiver-operating characteristic curve 0.7579, 95% confidence interval 0.7332-0.7825). In the top quintile of the score, more than 1 in 5 patients had a VT ≥10 beats, while the bottom quintile had a 98.2% negative predictive value. Conclusion: Our model can predict risk of VT ≥10 beats in the near term using variables derived from 24-hour electrocardiography, and could be used to triage patients to extended monitoring.
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Lundeberg et al. (2023) conducted an observational in Ventricular tachycardia (n=19,781). Risk prediction model based on 24-hour electrocardiography was evaluated on VT episodes ≥10 beats on monitoring days 2 to 30 (AUC 0.7584, 95% CI 0.7340-0.7829). A risk prediction model derived from the first 24 hours of mobile cardiac telemetry accurately predicted VT episodes ≥10 beats on subsequent days (AUC 0.7584; 95% CI 0.7340-0.7829).
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