Background: A priority of bedside electrocardiographic (ECG) monitoring is identification of ventricular tachycardia (VT), a lethal arrhythmia associated with morbidity and mortality. However, up to 87% of VT alarms could be false. In addition, little is known about the rate of mortality associated with VT. We assessed the rate of 30-day in-hospital mortality associated with VT generated from bedside ECG monitors and VT identified via a new algorithm among intensive care unit (ICU) patients.
Methods: We conducted a retrospective cohort study of adult patients admitted to all ICUs of an urban tertiary care academic medical center, September 2013-April 2015. We compared VT alerts from bedside ECG monitors, VT alerts identified by a new algorithm, and true VT manually-annotated by expert clinicians. Patient-level data were extracted from the electronic health record. We used Kaplan-Meier and Cox proportional hazards methods, adjusting for patient-level clustering to associate VT alerts from the bedside monitor, our unannotated new algorithm, and annotated true VT to study outcomes.
Results: We included 5,679 ICU admissions (mean age 58, +/- 17 years, 48% females), 503 died in-hospital within 30 days. A total of 1,711 patients (30.1%) had at least one VT alert from the current bedside monitor, 850 (15.0%) had an unannotated VT alert from the new algorithm, and 586 (10.3%) had true VT. The 30-day in-hospital survival was significantly lower for those who had at least one VT based on the current bedside monitor, our new VT algorithm, and true VT (Figure 1a-c). In multivariable analysis adjusting for age, sex, and underlying comorbidities known to be associated with VT, presence of VT alerts from bedside monitors was not associated with increased rate of 30-day mortality (HR 1.06,95%CI 0.88-1.27) but having at least one VT from our new algorithm (unannotated) and a true VT was associated with an increased rate of mortality (HR 1.41,95%CI 1.15-1.72; HR 1.32,95%CI 1.06-1.65)(Table).
Conclusions: Both unannotated and annotated true VT identified via our new algorithm were associated with increased rate of 30-day in-hospital mortality, while VT identified from the current bedside monitor was not. Our new algorithm may accurately identifying patients at greatest risk of mortality associated with VT; however, prospective validation is needed.