title: An Electrocardiogram-Based Risk Equation for Incident Cardiovascular Disease From the National Health and Nutrition Examination Survey.
abstract: Electrocardiography (ECG) may detect subclinical cardiovascular disease (CVD) in asymptomatic individuals, but its role in assessing adverse events beyond traditional risk factors is not clear. Interval and vector data that are commonly available on modern ECGs may offer independent prognostic information that improves risk classification.
  
  
  
  To derive and validate a CVD risk equation based on ECG metrics and to determine its incremental benefit in addition to the Framingham risk score (FRS).
  
  
  
  This study included 3640 randomly selected community-based adults aged 40 to 74 years without known CVD from the First National Health and Nutrition Examination Survey (NHANES I) cohort (1971-1975) and 6329 from the NHANES III cohort (1988-1994). Participants were sampled from across the United States. A risk score to assess incident nonfatal and fatal CVD events was derived based on computer-generated ECG data, including frontal P, R, and T axes; heart rate; and PR, QRS, and QT intervals from NHANES I. The most prognostic variables, along with age and sex, were incorporated into the NHANES ECG risk equation. The equation was evaluated in the NHANES III cohort for an independent validation. Follow-up in the NHANES III cohort was completed on December 31, 2006. Data for this study were analyzed from August 11, 2015, to May 20, 2016.
  
  
  
  The primary end point was CVD death. Secondary outcomes included 10-year ischemic heart disease and all-cause death.
  
  
  
  The final study sample included 9969 participants (4714 men [47.3%]; 5255 women [52.7%]; mean [SD] age, 55.3 [10.1] years) from both cohorts. Frontal T axis, heart rate, and heart rate-corrected QT interval were the most significant ECG factors in the NHANES I cohort. In the validation cohort (NHANES III), the equation provided for prognostic information for fatal CVD with a hazard ratio (HR) of 3.23 (95% CI, 2.82-3.72); the C statistic was 0.79 (95% CI, 0.76-0.81). When added to the FRS in Cox proportional hazards regression models, the categorical (1%, 5%, and 10% cutoffs) net reclassification improvement was 24%. When the FRS and ECG scores were combined in a single model, the C statistic improved by 0.04 (95% CI, 0.02-0.06) to 0.80 (95% CI, 0.77-0.82). Similar improvements were noted when the ECG score was added to the pooled cohort equation. When the equation for prognostic information about ischemic heart disease and all-cause death was evaluated, the results were similar.
  
  
  
  An ECG risk score based on age, sex, heart rate, frontal T axis, and QT interval assesses the risk for CVD and compares favorably with the FRS alone in an independent cohort of asymptomatic individuals. Although the ECG risk equation is low cost, further research is needed to ascertain whether this additional step in risk stratification may improve prevention efforts and reduce CVD events.
authors.affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia2Department of Medicine, Emory University, Atlanta, Georgia3Division of Cardiology, Department of Medicine, Atlanta Veterans Affairs Medical Center, Atlanta, Georgia.
authors.forename: Amit J
authors.initials: AJ
authors.lastname: Shah
authors.affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia2Department of Medicine, Emory University, Atlanta, Georgia.
authors.forename: Viola
authors.initials: V
authors.lastname: Vaccarino
authors.affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia4Department of Clinical Genetics/EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands.
authors.forename: A Cecile J W
authors.initials: AC
authors.lastname: Janssens
authors.affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
authors.forename: W Dana
authors.initials: WD
authors.lastname: Flanders
authors.affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
authors.forename: Suman
authors.initials: S
authors.lastname: Kundu
authors.affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia2Department of Medicine, Emory University, Atlanta, Georgia5Department of Biostatistics, Florida International University, Miami, Florida.
authors.forename: Emir
authors.initials: E
authors.lastname: Veledar
authors.affiliation: Department of Medicine, Emory University, Atlanta, Georgia3Division of Cardiology, Department of Medicine, Atlanta Veterans Affairs Medical Center, Atlanta, Georgia.
authors.forename: Peter W F
authors.initials: PW
authors.lastname: Wilson
authors.affiliation: Department of Epidemiology, Wake Forest School of Medicine, Winston-Salem, North Carolina7Department of Medicine, Section of Cardiology, Wake Forest School of Medicine, Winston-Salem, North Carolina.
authors.forename: Elsayed Z
authors.initials: EZ
authors.lastname: Soliman
mesh_terms: D000328:Adult; D000368:Aged; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009749:Nutrition Surveys; D018570:Risk Assessment; D012307:Risk Factors