Journal of clinical monitoring and computing · 2014
Electrocardiogram characteristics prior to in-hospital cardiac arrest.
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Attin Mina, Feld Gregory, Lemus Hector, Najarian Kayvan, Shandilya Sharad, Wang Lu, Sabouriazad Pouya, Lin Chii-Dean
Аннотация
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.
Предметные рубрики MeSH
AgedArrhythmias, CardiacBrugada SyndromeCardiac Conduction System DiseaseCardiopulmonary ResuscitationElectrocardiographyFemaleHeart ArrestHeart Conduction SystemHeart RateHumansIntensive Care UnitsMachine LearningMaleMiddle AgedMonitoring, PhysiologicPatient DischargeRetrospective StudiesRisk FactorsTelemetryTreatment OutcomeVentricular Fibrillation