title: A new electrocardiographic algorithm to locate the occlusion in left anterior descending coronary artery.
abstract: BACKGROUND
  Early prediction of proximal left anterior descending coronary artery (LAD) occlusion is essential from a clinical point of view
  
  
  OBJECTIVE
  To develop an electrocardiogram (ECG) algorithm based on ST-segment deviations to predict the location of occlusion of LAD as a culprit artery.
  
  
  METHODS
  ECG and angiographic findings were correlated in 100 patients with an ST-segment elevation myocardial infarction (MI) in precordial leads V(1), V(2), and V(4) through V(6).
  
  
  RESULTS
  ST-depression > or = 2.5 mm in leads III + ventricular fibrillation (VF) presents sensitivity (SE) of 77% and specificity (SP) of 84% for LAD occlusion proximal to the first diagonal artery (D1). ST-segment in III + VF isoelectric or elevated, presents SE of 44% and SP of 100% for LAD occlusion distal to D1. Subsequent analysis of the equation summation operator of ST-deviation in VR + V(1) - V(6) < 0, allows us to predict occlusion distal to first septal artery (S1) with 100% SP. On the other hand, any ST-depression in III + VF > 0.5 mm + summation operator of ST-deviation in VR + V(1) - V(6) > or = 0 identifies a high-risk group (lower ejection fraction, worse Killip findings, higher peak of CPK and CK-MB, and major adverse cardiac events [MACE]: death, reinfarction, recurrent angina, persistent left ventricular failure, or sustained ventricular arrhythmia during hospitalization).
  
  
  CONCLUSIONS
  This sequential ECG algorithm based on ST-segment deviations in different leads allowed us to predict the location of occlusion in LAD with good accuracy. Cases with proximal LAD occlusion present the most markers of poor prognosis. We recommend the use of the algorithm in everyday clinical practice.
authors.affiliation: Son Dureta University Hospital and University Institute for Investigation on Health Sciences, Coronary and Critical Care Unit, Barcelona. mfiol@hotmail.com
authors.forename: Miguel
authors.initials: M
authors.lastname: Fiol
authors.forename: Andrés
authors.initials: A
authors.lastname: Carrillo
authors.forename: Iwona
authors.initials: I
authors.lastname: Cygankiewicz
authors.forename: Julio
authors.initials: J
authors.lastname: Velasco
authors.forename: Maria
authors.initials: M
authors.lastname: Riera
authors.forename: Antoni
authors.initials: A
authors.lastname: Bayés-Genis
authors.forename: Alfredo
authors.initials: A
authors.lastname: Gómez
authors.forename: Vicente
authors.initials: V
authors.lastname: Peral
authors.forename: Armando
authors.initials: A
authors.lastname: Bethencourt
authors.forename: Diego
authors.initials: D
authors.lastname: Goldwasser
authors.forename: Fredis
authors.initials: F
authors.lastname: Molina
authors.forename: Antoni
authors.initials: A
authors.lastname: Bayés de Luna
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography; D054059:Coronary Occlusion / Q000150:complications / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000401:mortality; D042241:Early Diagnosis; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000209:etiology / Q000401:mortality; D011237:Predictive Value of Tests; D011379:Prognosis; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014693:Ventricular Fibrillation / Q000209:etiology