title: Algorithm for quantitative 3 dimensional analysis of ECG signals improves myocardial diagnosis over cardiologists in diabetic patients.
abstract: Acute myocardial infarction (AMI) diagnosis in type II diabetes (DM2) patients is difficult and ECG findings are often non-diagnostic or inconclusive. We developed computer algorithms to process standard 12-lead ECG input data for quantitative 3-dimensional (3D) analysis (my3KGTM), and hypothesized that use of the my3KGTM's array of over 100 3D-based AMI diagnostic markers may improve diagnostic accuracy for AMI in DM2 patients.
  
  
  METHODS
  We identified 155 consecutive DM2 patients age >25 yrs with chest discomfort or shortness of breath who were evaluated at an urban emergency department (130 patients (pts)) or the cardiac catheterization laboratory (25 pts) for possible AMI. The first digital 12-lead ECG for each patient, obtained within 30 min of presentation, was evaluated by (1) 2 blinded expert cardiologists, and (2) my3KGTM. In each case, the ECG was classified as either likely AMI or likely non-AMI. "Gold standard" was the final clinical diagnosis. Statistical analysis was McNemar's test with continuity correction.
  
  
  RESULTS
  The 155 DM2 patients were 50% male, mean age 56.8 ± 12.0 yrs; 44 pts had a final clinical diagnosis of AMI (17 ST Elevation Myocardial Infarctions (STEMI), 27 Non-ST Elevation Myocardial Infarctions (NSTEMI)) and 111 had no AMI.
  
  
  CONCLUSIONS
  Relative to standard 12L ECG read by cardiologists, quantitative 3D ECG analysis showed significant and substantial gains in sensitivity for AMI diagnosis in DM2 patients, without loss in specificity. Sensitivity gains were particularly high in patients exhibiting NSTEMI, the most common form of AMI in DM2.
authors.affiliation: NewCardio, Inc in Santa Clara, CA, USA. bwenzel@newcardio.com
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Wenzel
authors.forename: Samuel
authors.initials: S
authors.lastname: George
authors.forename: Dhanunjaya
authors.initials: D
authors.lastname: Lakkireddy
authors.forename: Subba
authors.initials: S
authors.lastname: Vanga
authors.forename: Sudha
authors.initials: S
authors.lastname: Bommana
authors.forename: Ihor
authors.initials: I
authors.lastname: Gussak
authors.forename: Goran
authors.initials: G
authors.lastname: Simic
authors.forename: Bosko
authors.initials: B
authors.lastname: Bojovic
authors.forename: Ljupco
authors.initials: L
authors.lastname: Hadzievski
authors.forename: Dorin
authors.initials: D
authors.lastname: Panescu
mesh_terms: D000465:Algorithms; D018780:Body Surface Potential Mapping / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000150:complications* / Q000175:diagnosis*; D015588:Observer Variation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity