Background

Accurate and timely detection of atrial fibrillation (AF) episodes is important in primarily and secondary prevention of ischemic stroke and heart-related problems. In this work, heart rate regularity of ECG inter-beat intervals was investigated in episodes of AF and other rhythms using a wavelet leader based multifractal analysis. Our aim was to improve the detectability of AF episodes.

Methods

Inter-beat intervals from 25 ECG recordings available in the MIT-BIH atrial fibrillation database were analysed. Four types of annotated rhythms (atrial fibrillation, atrial flutter, AV junctional rhythm, and other rhythms) were available. A wavelet leader based multifractal analysis was applied to 5 min non-overlapping windows of each recording to estimate the multifractal spectrum in each window. The width of the multifractal spectrum was analysed for its discrimination power between rhythm episodes.

Results

In 10 of 25 recordings, the width of multifractal spectrum was significantly lower in episodes of AF than in other rhythms indicating increased regularity during AF. High classification accuracy (95%) of AF episodes was achieved using a combination of features derived from the multifractal analysis and statistical central moment features.

Conclusions

An increase in the regularity of inter-beat intervals was observed during AF episodes by means of multifractal analysis. Multifractal features may be used to improve AF detection accuracy.

Background

Ventricular tachycardia (V-tach) is the most common lethal arrhythmia, yet 90% of alarms are false and contribute to alarm fatigue. We hypothesize that some true V-tach also causes alarm fatigue because current criteria are too sensitive (i.e., ≥6 beats ≥100 beats/min [bpm]).

Purpose

This study was designed to determine (1) the proportion of clinically actionable true V-tach events; (2) whether true actionable versus nonactionable V-tach differs in terms of heart rate and/or duration (seconds); and (3) if actionable V-tach is associated with adverse outcomes.

Methods

This was a secondary analysis in 460 intensive care unit (ICU) patients. Electronic health records were examined to determine if a V-tach event was actionable or nonactionable. Actionable V-tach was defined if a clinical action(s) was taken within 15 minutes of its occurrence (i.e., new and/or change of medication, defibrillation, and/or laboratory test). Maximal heart rate and duration for each V-tach event were measured from bedside monitor electrocardiography. Adverse patient outcomes included a code blue event and/or death.

Results

In 460 ICU patients, 50 (11%) had 151 true V-tach events (range 1-20). Of the 50 patients, 40 (80%) had only nonactionable V-tach (97 events); 3 (6%) had both actionable and nonactionable V-tach (32 events); and 7 patients (14%) had only actionable V-tach (23 events). There were differences in duration comparing actionable versus nonactionable V-tach (mean 56.19 ± 116.87 seconds vs. 4.28 ± 4.09 seconds; P = 0.001) and maximal heart rate (188.81 ± 116.83 bpm vs. 150.79 ± 28.26 bpm; P = 0.001). Of the 50 patients, 3 (6%) had a code blue, 2 died, and all were in the actionable V-tach group.

Conclusions

In our sample, <1% experienced a code blue following true V-tach. Heart rate and duration for actionable V-tach were much faster and longer than that for nonactionable V-tach. Current default settings typically used for electrocardiographic monitoring (i.e., ≥6 beats ≥100 bpm) appear to be too conservative and can lead to crisis/red level nuisance alarms that contribute to alarm fatigue. A prospective study designed to test whether adjusting default settings to these higher levels is safe for patients is needed.

Background

False ventricular tachycardia (VT) alarms are common during in-hospital electrocardiographic (ECG) monitoring. Prior research shows that the majority of false VT can be attributed to algorithm deficiencies.

Purpose

The purpose of this study was: (1) to describe the creation of a VT database annotated by ECG experts and (2) to determine true vs. false VT using a new VT algorithm created by our group.

Methods

The VT algorithm was processed in 5320 consecutive ICU patients with 572,574 h of ECG and physiologic monitoring. A search algorithm identified potential VT, defined as: heart rate >100 beats/min, QRSs > 120 ms, and change in QRS morphology in >6 consecutive beats compared to the preceding native rhythm. Seven ECG channels, SpO₂ , and arterial blood pressure waveforms were processed and loaded into a web-based annotation software program. Five PhD-prepared nurse scientists performed the annotations.

Results

Of the 5320 ICU patients, 858 (16.13%) had 22,325 VTs. After three levels of iterative annotations, a total of 11,970 (53.62%) were adjudicated as true, 6485 (29.05%) as false, and 3870 (17.33%) were unresolved. The unresolved VTs were concentrated in 17 patients (1.98%). Of the 3870 unresolved VTs, 85.7% (n = 3281) were confounded by ventricular paced rhythm, 10.8% (n = 414) by underlying BBB, and 3.5% (n = 133) had a combination of both.

Conclusions

The database described here represents the single largest human-annotated database to date. The database includes consecutive ICU patients, with true, false, and challenging VTs (unresolved) and could serve as a gold standard database to develop and test new VT algorithms.

BACKGROUND: Continuous electrocardiographic (ECG) monitoring is used to identify ventricular tachycardia (VT), but false alarms occur frequently. OBJECTIVE: 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: 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: 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: 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.

In-hospital electrocardiographic (ECG) monitors are typically configured to alarm for premature ventricular complexes (PVCs) due to the potential association of PVCs with ventricular tachycardia (VT). However, no contemporary hospital-based studies have examined the association of PVCs with VT. Hence, the benefit of PVC monitoring in hospitalized patients is largely unknown. This secondary analysis used a large PVC alarm data set to determine whether PVCs identified during continuous ECG monitoring were associated with VT, in-hospital cardiac arrest (IHCA), and/or death in a cohort of adult intensive care unit patients. Six PVC types were examined (i.e., isolated, bigeminy, trigeminy, couplets, R-on-T, and run PVCs) and were compared between patients with and without VT, IHCA, and/or death. Of 445 patients, 48 (10.8%) had VT; 11 (2.5%) had IHCA; and 49 (11%) died. Isolated and run PVC counts were higher in the VT group (p = 0.03 both), but group differences were not seen for the other four PVC types. The regression models showed no significant associations between any of the six PVC types and VT or death, although confidence intervals were wide. Due to the small number of cases, we were unable to test for associations between PVCs and IHCA. Our findings suggest that we should question the clinical relevance of activating PVC alarms as a forewarning of VT, and more work should be done with larger sample sizes. A more precise characterization of clinically relevant PVCs that might be associated with VT is warranted.