Ventricular tachycardia risk prediction with an abbreviated duration mobile cardiac telemetry.

Ambulatory ECG Cardiac arrythmia Epidemiology Mobile cardiac telemetry Prediction Ventricular tachycardia

Journal

Heart rhythm O2
ISSN: 2666-5018
Titre abrégé: Heart Rhythm O2
Pays: United States
ID NLM: 101768511

Informations de publication

Date de publication:
Aug 2023
Historique:
medline: 30 8 2023
pubmed: 30 8 2023
entrez: 30 8 2023
Statut: epublish

Résumé

Ventricular tachycardia (VT) occurs intermittently, unpredictably, and has potentially lethal consequences. 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. 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. 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. 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.

Sections du résumé

Background UNASSIGNED
Ventricular tachycardia (VT) occurs intermittently, unpredictably, and has potentially lethal consequences.
Objective UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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.

Identifiants

pubmed: 37645265
doi: 10.1016/j.hroo.2023.06.009
pii: S2666-5018(23)00138-1
pmc: PMC10461200
doi:

Types de publication

Journal Article

Langues

eng

Pagination

500-505

Informations de copyright

© 2023 Heart Rhythm Society. Published by Elsevier Inc.

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Auteurs

Johan Economou Lundeberg (J)

Department of Clinical Physiology, Skåne University Hospital, Lund, Sweden.
Department of Clinical Sciences, Lund University, Malmö, Sweden.

Alexandra Måneheim (A)

Department of Clinical Physiology, Skåne University Hospital, Lund, Sweden.
Department of Clinical Sciences, Lund University, Malmö, Sweden.

Anders Persson (A)

Department of Clinical Physiology, Skåne University Hospital, Lund, Sweden.
Department of Clinical Sciences, Lund University, Malmö, Sweden.

Marek Dziubinski (M)

University of Washington Medical Center, Seattle, Washington.

Arun Sridhar (A)

University of Washington Medical Center, Seattle, Washington.

Jeffrey S Healey (JS)

Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada.

Magdalena Slusarczyk (M)

MEDICALgorithmics, Warsaw, Poland.

Gunnar Engström (G)

Department of Clinical Sciences, Lund University, Malmö, Sweden.

Linda S Johnson (LS)

Department of Clinical Sciences, Lund University, Malmö, Sweden.
Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada.

Classifications MeSH