title: Electrocardiogram characteristics prior to in-hospital cardiac arrest.
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.
authors.affiliation: School of Nursing, San Diego State University, 5500 Campanile Drive, San Diego, CA, 92182, USA, mattin@mail.sdsu.edu.
authors.forename: Mina
authors.initials: M
authors.lastname: Attin
authors.forename: Gregory
authors.initials: G
authors.lastname: Feld
authors.forename: Hector
authors.initials: H
authors.lastname: Lemus
authors.forename: Kayvan
authors.initials: K
authors.lastname: Najarian
authors.forename: Sharad
authors.initials: S
authors.lastname: Shandilya
authors.forename: Lu
authors.initials: L
authors.lastname: Wang
authors.forename: Pouya
authors.initials: P
authors.lastname: Sabouriazad
authors.forename: Chii-Dean
authors.initials: CD
authors.lastname: Lin
mesh_terms: D000368:Aged; D001145:Arrhythmias, Cardiac; D053840:Brugada Syndrome; D000075224:Cardiac Conduction System Disease; D016887:Cardiopulmonary Resuscitation; D004562:Electrocardiography; D005260:Female; D006323:Heart Arrest / Q000503:physiopathology*; D006329:Heart Conduction System / Q000002:abnormalities; D006339:Heart Rate; D006801:Humans; D007362:Intensive Care Units; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic; D010351:Patient Discharge; D012189:Retrospective Studies; D012307:Risk Factors; D013686:Telemetry; D016896:Treatment Outcome; D014693:Ventricular Fibrillation / Q000175:diagnosis