Use of hundreds of electrocardiographic biomarkers for prediction of mortality in postmenopausal women: the Women's Health Initiative

Abstract

BACKGROUND- Simultaneous contribution of hundreds of electrocardiographic (ECG) biomarkers to prediction of long-term mortality in postmenopausal women with clinically normal resting ECGs is unknown. METHODS AND RESULTS- We analyzed ECGs and all-cause mortality in 33 144 women enrolled in the Women's Health Initiative trials who were without baseline cardiovascular disease or cancer and had normal ECGs by Minnesota and Novacode criteria. Four hundred and seventy-seven ECG biomarkers, encompassing global and individual ECG findings, were measured with computer algorithms. During a median follow-up of 8.1 years (range for survivors, 0.5 to 11.2 years), 1229 women died. For analyses, the cohort was randomly split into derivation (n=22 096; deaths, 819) and validation (n=11 048; deaths, 410) subsets. ECG biomarkers and demographic and clinical characteristics were simultaneously analyzed using both traditional Cox regression and random survival forest, a novel algorithmic machine-learning approach. Regression modeling failed to converge. Random survival forest variable selection yielded 20 variables that were independently predictive of long-term mortality, 14 of which were ECG biomarkers related to autonomic tone, atrial conduction, and ventricular depolarization and repolarization. CONCLUSIONS- We identified 14 ECG biomarkers from among hundreds that were associated with long-term prognosis using a novel random forest variable selection methodology. These biomarkers were related to autonomic tone, atrial conduction, ventricular depolarization, and ventricular repolarization. Quantitative ECG biomarkers have prognostic importance and may be markers of subclinical disease in apparently healthy postmenopausal women.

Figure 1

Approach to constructing a Random Survival Forest. (A.) One thousand bootstrap samples of women were derived from full cohort, and (B.) each sample was then used to construct a unique and independent decision tree.

Figure 2

Example of one decision tree from forest. Depth of a branch (node) is indicated by numbers 0–10. Highlighted are maximal subtrees (i.e., largest subtree whose lowest branch is split using variable of interest) for the variables income (blue), and age (yellow). Income has one maximal subtree at minimal depth 0. Age has two maximal subtrees at minimal depths 3 and 6.

Figure 3

Minimal depth (variable importance) for (A.) all variables averaged out from all trees in forest (1,000 trees), and (B.) zoomed in on top 20 variables. Dashed blue line is threshold for filtering variables: variables to left of line are predictive. On y-axis is ranking of variables where age is most predictive, then waist-to-hip ratio, and so forth.

Figure 4

Measures of (A.) discrimination and (B.) calibration using validation cohort for nested models with variables ordered by increasing minimal depth for top 20 variables. First model included top variable (age), second model included top two variables (age and waist-to-hip ratio), third model included top three variables (age, waist-to-hip ratio, and smoking), and so forth.

Figure 5

Adjusted-predicted survival (%) at 5, 8, and 10 years for (A.) ventricular rate, (B.) P-wave duration (lead V2), (C.) T-wave amplitude (lead I), and (D.) QT duration (median of all leads).