Usefulness of Trends in Continuous Electrocardiographic Telemetry Monitoring to Predict In-Hospital Cardiac Arrest

Do, Duc; Kuo, Alan; Lee, Edward S.; Mortara, David; Elashoff, David; Hu, Xiao; Boyle, Noel G.

doi:10.1016/j.amjcard.2019.06.032

Cited by 15 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, development of automated algorithms to detect development of the RVS pattern can help not only guide management of a patient suffering PEA/Asystole arrest in real-time, but potentially predict impending cardiac arrests and allow for earlier targeted interventions. 6,22 Furthermore, this study suggests that different clinical causes and pathophysiologic mechanisms can lead to the same clinical manifestation of PEA/Asystole, and it is possible that they can be distinguished through their preceding ECG manifestations.…”

Section: Discussionmentioning

confidence: 74%

Electrocardiographic right ventricular strain precedes hypoxic pulseless electrical activity cardiac arrests: Looking beyond pulmonary embolism

H.¹,

Yang²,

Kuo³

et al. 2020

Resuscitation

Self Cite

View full text Add to dashboard Cite

Aim: The role of the right ventricle (RV) in pulseless electrical activity (PEA) is poorly defined outside of pulmonary embolism. We aimed to (1) describe the continuous electrocardiographic (ECG) manifestations of RV strain (RVS) preceding PEA/Asystole in-hospital cardiac arrest (IHCA), and (2) determine the prevalence and clinical causes of RVS in PEA/Asystole IHCA.Methods: In this retrospective cross-sectional study, we evaluated 140 patients with continuous ECG data preceding PEA/Asystole IHCA. We iteratively defined the RVS continuous ECG pattern using the development cohort (93 patients). Clinical cause determination was blinded from ECG analysis in the validation cohort (47 patients).Results: The overall cohort had mean age 62.1 AE 17.1 years, 70% return of spontaneous circulation and 30% survival to discharge. RVS continuous ECG pattern was defined as progressive RV depolarization delay in lead V1 with at least one supporting finding of RV ischaemia or right axis deviation.Using this criterion, 66/140 (47%) cases showed preceding RVS. In patients with RVS, no pulmonary embolism was found in 9/13 (69%) autopsies and 8/10 (80%) CT chest angiograms. The positive and negative predictive value of RVS pattern for diagnosing a respiratory cause of PEA/Asystole in the validation cohort was 81% [95% CI 64À98%] and 58% [95% CI 36À81%], respectively. Conclusion:RVS continuous ECG pattern preceded 47% of PEA/Asystole IHCA and is predictive of a respiratory cause of cardiac arrest, not just pulmonary embolism. These suggest that rapid elevations in pulmonary pressures and resultant RV failure may cause PEA in respiratory failure.

show abstract

Section: Discussionmentioning

confidence: 74%

Electrocardiographic right ventricular strain precedes hypoxic pulseless electrical activity cardiac arrests: Looking beyond pulmonary embolism

H.¹,

Yang²,

Kuo³

et al. 2020

Resuscitation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ultimately, a total of 46 studies were included in this review. 15 , 16 , 17 , 18 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 Out of these 46 studies, 36 used one or more ad-hoc dataset(s) and were pooled in separate meta-analysis. 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 55 , 57 , …”

Section: Resultsmentioning

confidence: 99%

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

Kolk¹,

Deb²,

Ruipérez-Campillo³

et al. 2023

eBioMedicine

View full text Add to dashboard Cite

“…Early incorporation of this concept has been shown in studies in which patterns and changes in continuous telemetry monitoring are analyzed to predict in hospital cardiac arrest. One study showed that a predictive model could be used to predict in hospital cardiac arrest and that trending telemetry changes was feasible (Do et al, 2019 ). Similar monitoring of telemetry changes has shown that machine learning and AI techniques can be applied to telemetry waveforms to also predict sepsis (Bravi et al, 2012 ).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

Rafie

Jentzer

Noseworthy

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.

show abstract

Usefulness of Trends in Continuous Electrocardiographic Telemetry Monitoring to Predict In-Hospital Cardiac Arrest

Cited by 15 publications

References 28 publications

Electrocardiographic right ventricular strain precedes hypoxic pulseless electrical activity cardiac arrests: Looking beyond pulmonary embolism

Electrocardiographic right ventricular strain precedes hypoxic pulseless electrical activity cardiac arrests: Looking beyond pulmonary embolism

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

Contact Info

Product

Resources

About