title: A Novel Electrocardiogram Algorithm Utilizing ST-Segment Instability for Detection of Cardiopulmonary Arrest in Single Ventricle Physiology: A Retrospective Study.
abstract: We evaluated ST-segment monitoring to detect clinical decompensation in infants with single ventricle anatomy. We proposed a signal processing algorithm for ST-segment instability and hypothesized that instability is associated with cardiopulmonary arrests.
  
  
  
  Retrospective, observational study.
  
  
  
  Tertiary children's hospital 21-bed cardiovascular ICU and 36-bed step-down unit.
  
  
  
  Twenty single ventricle infants who received stage 1 palliation surgery between January 2013 and January 2014. Twenty rapid response events resulting in cardiopulmonary arrests (arrest group) were recorded in 13 subjects, and nine subjects had no interstage cardiopulmonary arrest (control group).
  
  
  
  None.
  
  
  
  Arrest data were collected over the 4-hour time window prior to cardiopulmonary arrest. Control data were collected from subjects with no interstage arrest using the 4-hour time window prior to cardiovascular ICU discharge. A paired subgroup analysis was performed comparing subject 4-hour windows prior to arrest (prearrest group) with 4-hour windows prior to discharge (postarrest group). Raw values of ST segments were compared between groups. A 3D ST-segment vector was created using three quasi-orthogonal leads (II, aVL, and V5). Magnitude and instability of this continuous vector were compared between groups. There was no significant difference in mean unprocessed ST-segment values in the arrest and control groups. Utilizing signal processing, there was an increase in the ST-vector magnitude (p = 0.02) and instability (p = 0.008) in the arrest group. In the paired subgroup analysis, there was an increase in the ST-vector magnitude (p = 0.05) and instability (p = 0.05) in the prearrest state compared with the postarrest state prior to discharge.
  
  
  
  In single ventricle patients, increased ST instability and magnitude were associated with rapid response events that required intervention for cardiopulmonary arrest, whereas conventional ST-segment monitoring did not differentiate an arrest from control state.
authors.affiliation: 1Department of Pediatric Anesthesiology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX.2Department of Pediatric Cardiology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX.
authors.forename: Eric L
authors.initials: EL
authors.lastname: Vu
authors.forename: Craig G
authors.initials: CG
authors.lastname: Rusin
authors.forename: Dan J
authors.initials: DJ
authors.lastname: Penny
authors.forename: Kathy K
authors.initials: KK
authors.lastname: Kibler
authors.forename: Ronald Blaine
authors.initials: RB
authors.lastname: Easley
authors.forename: Brendan
authors.initials: B
authors.lastname: Smith
authors.forename: Dean
authors.initials: D
authors.lastname: Andropoulos
authors.forename: Ken
authors.initials: K
authors.lastname: Brady
mesh_terms: D000465:Algorithms; D003661:Decision Support Techniques; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006323:Heart Arrest / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D006352:Heart Ventricles / Q000002:abnormalities*; D006801:Humans; D018636:Hypoplastic Left Heart Syndrome / Q000150:complications* / Q000503:physiopathology / Q000601:surgery; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D019990:Perioperative Care / Q000379:methods; D012189:Retrospective Studies