title: Heart rate turbulence can predict cardiac mortality following myocardial infarction in patients with diabetes mellitus.
abstract: BACKGROUND
  Previous studies have described the clinical utility of heart rate turbulence (HRT) as an autonomic predictor in risk-stratifying patients after myocardial infarction (MI). Some reports showed that diabetes mellitus (DM) affects the prognostic value of autonomic markers. We assessed the utility of HRT as a risk marker in post-MI patients with DM and without DM.
  
  
  METHODS
  We prospectively enrolled 231 consecutive DM patients and 300 non-DM patients after acute MI. HRT was measured using an algorithm based on 24-hour Holter electrocardiograms (ECGs), assessing 2 parameters: turbulence onset (TO) and turbulence slope (TS). HRT was considered positive when both TO ≥0% and TS ≤2.5 ms/R-R interval were met. The endpoint was defined as cardiac mortality.
  
  
  RESULTS
  Of patients with DM, 9 patients (4%) were not utilized for HRT assessment because of frequent ventricular contractions or presence of atrial fibrillation. Forty-two of 222 patients (19%) were HRT positive. During follow-up of 876 ± 424 days, 26 patients (22%) reached the endpoint. Several factors including left ventricular ejection fraction (LVEF), renal dysfunction, documentation of nonsustained ventricular tachycardia (VT), and a HRT-positive outcome had significant association with the endpoint. Multivariate analysis determined that renal dysfunction and a positive HRT outcome had significant value with a hazard ratio (HR) of 4.7 (95%CI, 1.9-11.5; P = 0.0008) and 3.5 (95%CI, 1.4-8.8; P = 0.007), respectively. In non-DM patients, only a positive HRT outcome had significant value.
  
  
  CONCLUSIONS
  This study reveals that HRT detected by 24-hour Holter ECG can predict cardiac mortality in post-MI patients whether DM is present or not.
authors.affiliation: Second Department of Internal Medicine, Kyorin University School of Medicine, Tokyo.
authors.forename: Yosuke
authors.initials: Y
authors.lastname: Miwa
authors.forename: Mutsumi
authors.initials: M
authors.lastname: Miyakoshi
authors.forename: Kyoko
authors.initials: K
authors.lastname: Hoshida
authors.forename: Ryoji
authors.initials: R
authors.lastname: Yanagisawa
authors.forename: Atsuko
authors.initials: A
authors.lastname: Abe
authors.forename: Takehiro
authors.initials: T
authors.lastname: Tsukada
authors.forename: Haruhisa
authors.initials: H
authors.lastname: Ishiguro
authors.forename: Hisaaki
authors.initials: H
authors.lastname: Mera
authors.forename: Satoru
authors.initials: S
authors.lastname: Yusu
authors.forename: Hideaki
authors.initials: H
authors.lastname: Yoshino
authors.forename: Takanori
authors.initials: T
authors.lastname: Ikeda
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016009:Chi-Square Distribution; D048909:Diabetes Complications / Q000175:diagnosis* / Q000401:mortality* / Q000503:physiopathology; D015716:Electrocardiography, Ambulatory; D005260:Female; D006339:Heart Rate; D006801:Humans; D007564:Japan; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000401:mortality* / Q000503:physiopathology; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors