Elevated premature ventricular complex counts on 24-hour electrocardiogram predict incident atrial fibrillation and heart failure—A prospective population-based cohort study

BackgroundPremature ventricular complexes (PVCs) are known to predict heart failure (HF) and premature atrial contractions (PACs) are known to predict atrial fibrillation (AF) and stroke. PVCs and PACs share pathophysiological mechanisms; however, the combined effects of PVCs and PACs on HF, AF, and stroke risk have not been studied.ObjectivesTo study elevated PVC counts on 24-hour electrocardiogram monitoring (24hECG) in relation to incidence of AF, HF, and stroke, and whether this effect is altered by PAC frequency.MethodsThe prospective population-based Malmö Diet and Cancer study includes 24hECG registrations in 375 AF- and HF-free subjects (mean age 65 years, 55% women). During 17 years of follow-up there were 28 HF, 89 AF, and 28 stroke events. The hazard ratios (HR) of elevated PVC counts (defined as the top quartile, ≥77/24 hours) vs lower quartiles were assessed using multivariable adjusted Cox regression models.ResultsElevated PVC counts predicted incident AF (HR 1.9, 95% confidence interval [CI] 1.2–3.0) and HF (HR 3.1, 95% CI 1.4–7.0). Results were similar after adjustment for NT-proBNP and PACs. Multiform PVCs were associated with even higher risks (HR 2.8, 95% CI: 1.7–4.6 for AF; HR 5.0, 95% CI 2.2–11.7 for HF), as was the presence of both elevated PACs and PVCs (9% of the population, HR 4.1, 95% CI 2.4–6.8 for AF and HR 4.3, 95% CI 1.7–11.4 for HF). No significant association was found between elevated PVC counts and incident stroke.ConclusionElevated PVC counts predict incident AF and HF, particularly if PVCs are multiform or occur in combination with elevated PAC counts

Ventricular tachycardia risk prediction with an abbreviated duration mobile cardiac telemetry

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

Can 24 h of ambulatory ECG be used to triage patients to extended monitoring?

Abstract Background Access to long‐term ambulatory recording to detect atrial fibrillation (AF) is limited for economical and practical reasons. We aimed to determine whether 24 h ECG (24hECG) data can predict AF detection on extended cardiac monitoring. Methods We included all US patients from 2020, aged 17–100 years, who were monitored for 2–30 days using the PocketECG device (MEDICALgorithmics), without AF ≥30 s on the first day (n = 18,220, mean age 64.4 years, 42.4% male). The population was randomly split into equal training and testing datasets. A Lasso model was used to predict AF episodes ≥30 s occurring on days 2–30. Results The final model included maximum heart rate, number of premature atrial complexes (PACs), fastest rate during PAC couplets and triplets, fastest rate during premature ventricular couplets and number of ventricular tachycardia runs ≥4 beats, and had good discrimination (ROC statistic 0.7497, 95% CI 0.7336–0.7659) in the testing dataset. Inclusion of age and sex did not improve discrimination. A model based only on age and sex had substantially poorer discrimination, ROC statistic 0.6542 (95% CI 0.6364–0.6720). The prevalence of observed AF in the testing dataset increased by quintile of predicted risk: 0.4% in Q1, 2.7% in Q2, 6.2% in Q3, 11.4% in Q4, and 15.9% in Q5. In Q1, the negative predictive value for AF was 99.6%. Conclusion By using 24hECG data, long‐term monitoring for AF can safely be avoided in 20% of an unselected patient population whereas an overall risk of 9% in the remaining 80% of the population warrants repeated or extended monitoring