Automating the Study of Population Variation of Electrocardiographic Features
The study by Ritchie et al.,1 in this issue employs electronic health record data and DNA biobanks to identify several genomic variants previously implicated 2, 3 in the variation of ECG parameters of cardiac conduction and diseases of cardiac conduction. So why is this study worthy of note?
Ever since Enthoven first named the QRS complex 4, investigators have sought to define what constitutes a normal complex and the diagnostic and prognostic significance of deviations from the norm. The growing understanding that there is no categorical set of normal values, prompted population studies of (typically white and male) subjects numbering in the 100's. 5 and eventually tens of thousands 6. These studies did generate a more robust set of reference values and did emphasize that the notion of normal vs. abnormal QRS was not appropriate and argued for "an index of the possibility of normals or abnormals occurring at various levels" and "variations in electrocardiograms ... considerably greater than the present standards would lead one to expect..." 5 Subsequent, larger population studies including clinical trial populations 7, 8 with broader age and gender distributions revealed that variation in QRS characteristics in healthy individuals was larger than suspected. In parallel, several studies analyzed the clinical correlates of ECG features, For example in 1967, Pipberger et al 9 conducted what might today be called a "phenome scan" 10, 11. For each of the identified ECG measures, they scanned multiple constitutional features (e.g. obesity) and ethnicity to assess bias and correlation. Among their findings were the significant differences in QRS measures in African Americans, even when correcting for differences in the other constitutional features.
- electronic health records
- medical informatics
- arrhythmia (heart rhythm disorders)
- Received February 26, 2013.
- Accepted February 26, 2013.