ArticleJournal of clinical monitoring and computing2015
Electrocardiogram characteristics prior to in-hospital cardiac arrest.
Article in Journal of clinical monitoring and computing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review.Annals of biomedical engineering · 2024Pooled it
- ECG characteristics as indicators of the aetiology of pulseless electrical activity: a systematic review.British paramedic journal · 2025Article
- Wearable devices for out-of-hospital cardiac arrest: A population survey on the willingness to adhere.Journal of the American College of Emergency Physicians open · 2024Article
- Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review.Journal of clinical medicine · 2023Review
- Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU.NPJ digital medicine · 2023Article
- Article
- Generalist practitioners' self-rating and competence in electrocardiogram interpretation in South Africa.African journal of primary health care & family medicine · 2020Article
- Multicenter Analysis of Dosing Protocols for Sotalol Initiation.Journal of cardiovascular pharmacology and therapeutics · 2020Observational
- ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges.Sensors (Basel, Switzerland) · 2020Review
- Usefulness of Trends in Continuous Electrocardiographic Telemetry Monitoring to Predict In-Hospital Cardiac Arrest.The American journal of cardiology · 2019Article
- A review of big data applications of physiological signal data.Biophysical reviews · 2019Review
- Developing new predictive alarms based on ECG metrics for bradyasystolic cardiac arrest.Physiological measurement · 2015Article
- Big Data Analytics in Healthcare.BioMed research international · 2015Review
- Prognostic value of transient conduction disturbance in out-of-hospital cardiac arrest.Acute medicine & surgeryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Survival after in-hospital cardiac arrest (I-HCA) remains < 30 %. There is very limited literature exploring the electrocardiogram changes prior to I-HCA. The purpose of the study was to determine demographics and electrocardiographic predictors prior to I-HCA. A retrospective study was conducted among 39 cardiovascular subjects who had cardiopulmonary resuscitation from I-HCA with initial rhythms of pulseless electrical activity (PEA) and asystole. Demographics including medical history, ejection fraction, laboratory values, and medications were examined. Electrocardiogram (ECG) parameters from telemetry were studied to identify changes in heart rate, QRS duration and morphology, and time of occurrence and location of ST segment changes prior to I-HCA. Increased age was significantly associated with failure to survive to discharge (p < 0.05). Significant change was observed in heart rate including a downtrend of heart rate within 15 min prior to I-HCA (p < 0.05). There was a significant difference in heart rate and QRS duration during the last hour prior to I-HCA compared to the previous hours (p < 0.05). Inferior ECG leads showed the most significant changes in QRS morphology and ST segments prior to I-HCA (p < 0.05). Subjects with an initial rhythm of asystole demonstrated significantly greater ECG changes including QRS morphology and ST segment changes compared to the subjects with initial rhythms of PEA (p < 0.05). Diagnostic ECG trends can be identified prior to I-HCA due to PEA and asystole and can be further utilized for training a predictive machine learning model for I-HCA.
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Registered trials
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