Ventricular tachycardia and in-hospital mortality in the intensive care unit.

Alarm fatigue Algorithm development Continuous electrocardiographic monitoring In-hospital mortality Intensive care unit 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:
Nov 2023
Historique:
medline: 30 11 2023
pubmed: 30 11 2023
entrez: 30 11 2023
Statut: epublish

Résumé

Continuous electrocardiographic (ECG) monitoring is used to identify ventricular tachycardia (VT), but false alarms occur frequently. The purpose of this study was to assess the rate of 30-day in-hospital mortality associated with VT alerts generated from bedside ECG monitors to those from a new algorithm among intensive care unit (ICU) patients. We conducted a retrospective cohort study in consecutive adult ICU patients at an urban academic medical center and compared current bedside monitor VT alerts, VT alerts from a new-unannotated algorithm, and true-annotated VT. We used survival analysis to explore the association between VT alerts and mortality. We included 5679 ICU admissions (mean age 58 ± 17 years; 48% women), 503 (8.9%) experienced 30-day in-hospital mortality. A total of 30.1% had at least 1 current bedside monitor VT alert, 14.3% had a new-unannotated algorithm VT alert, and 11.6% had true-annotated VT. Bedside monitor VT alert was not associated with increased rate of 30-day mortality (adjusted hazard ratio [aHR] 1.06; 95% confidence interval [CI] 0.88-1.27), but there was an association for VT alerts from our new-unannotated algorithm (aHR 1.38; 95% CI 1.12-1.69) and true-annotated VT(aHR 1.39; 95% CI 1.12-1.73). Unannotated and annotated-true VT were associated with increased rate of 30-day in-hospital mortality, whereas current bedside monitor VT was not. Our new algorithm may accurately identify high-risk VT; however, prospective validation is needed.

Sections du résumé

Background UNASSIGNED
Continuous electrocardiographic (ECG) monitoring is used to identify ventricular tachycardia (VT), but false alarms occur frequently.
Objective UNASSIGNED
The purpose of this study was to assess the rate of 30-day in-hospital mortality associated with VT alerts generated from bedside ECG monitors to those from a new algorithm among intensive care unit (ICU) patients.
Methods UNASSIGNED
We conducted a retrospective cohort study in consecutive adult ICU patients at an urban academic medical center and compared current bedside monitor VT alerts, VT alerts from a new-unannotated algorithm, and true-annotated VT. We used survival analysis to explore the association between VT alerts and mortality.
Results UNASSIGNED
We included 5679 ICU admissions (mean age 58 ± 17 years; 48% women), 503 (8.9%) experienced 30-day in-hospital mortality. A total of 30.1% had at least 1 current bedside monitor VT alert, 14.3% had a new-unannotated algorithm VT alert, and 11.6% had true-annotated VT. Bedside monitor VT alert was not associated with increased rate of 30-day mortality (adjusted hazard ratio [aHR] 1.06; 95% confidence interval [CI] 0.88-1.27), but there was an association for VT alerts from our new-unannotated algorithm (aHR 1.38; 95% CI 1.12-1.69) and true-annotated VT(aHR 1.39; 95% CI 1.12-1.73).
Conclusion UNASSIGNED
Unannotated and annotated-true VT were associated with increased rate of 30-day in-hospital mortality, whereas current bedside monitor VT was not. Our new algorithm may accurately identify high-risk VT; however, prospective validation is needed.

Identifiants

pubmed: 38034889
doi: 10.1016/j.hroo.2023.09.008
pii: S2666-5018(23)00227-1
pmc: PMC10685163
doi:

Types de publication

Journal Article

Langues

eng

Pagination

715-722

Informations de copyright

© 2023 Heart Rhythm Society. Published by Elsevier Inc.

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Auteurs

Priya A Prasad (PA)

Department of Medicine, Division of Hospital Medicine, School of Medicine, University of California, San Francisco, San Francisco, California.
Center for Physiologic Research, University of California San Francisco School of Nursing, San Francisco, California.

Jonas L Isaksen (JL)

Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.

Yumiko Abe-Jones (Y)

Department of Medicine, Division of Hospital Medicine, School of Medicine, University of California, San Francisco, San Francisco, California.

Jessica K Zègre-Hemsey (JK)

School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

Claire E Sommargren (CE)

Department of Physiological Nursing, University of California School of Nursing, San Francisco, California.

Salah S Al-Zaiti (SS)

Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania.

Mary G Carey (MG)

School of Nursing, University of Rochester, Rochester, New York.

Fabio Badilini (F)

Center for Physiologic Research, University of California San Francisco School of Nursing, San Francisco, California.
Department of Physiological Nursing, University of California School of Nursing, San Francisco, California.
Department of Medicine, Division of Cardiology, School of Medicine, University of California, San Francisco, San Francisco, California.

David Mortara (D)

Center for Physiologic Research, University of California San Francisco School of Nursing, San Francisco, California.
Department of Physiological Nursing, University of California School of Nursing, San Francisco, California.
Department of Medicine, Division of Cardiology, School of Medicine, University of California, San Francisco, San Francisco, California.

Jørgen K Kanters (JK)

Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.

Michele M Pelter (MM)

Center for Physiologic Research, University of California San Francisco School of Nursing, San Francisco, California.
Department of Physiological Nursing, University of California School of Nursing, San Francisco, California.

Classifications MeSH