  Electronic health record (EHR) data are becoming an increasingly common data
source for understanding clinical risk of acute events. While their
longitudinal nature presents opportunities to observe changing risk over time,
these analyses are complicated by the sparse and irregular measurements of many
of the clinical metrics making typical statistical methods unsuitable for these
data. In this paper, we present an analytic procedure to both sample from an
EHR and analyze the data to detect clinically meaningful markers of acute
myocardial infarction (MI). Using an EHR from a large national dialysis
organization we abstracted the records of 64,318 individuals and identified
5,314 people that had an MI during the study period. We describe a nested
case-control design to sample appropriate controls and an analytic approach
using regression splines. Fitting a mixed-model with truncated power splines we
perform a series of goodness-of-fit tests to determine whether any of 11
regularly collected laboratory markers are useful clinical predictors. We test
the clinical utility of each marker using an independent test set. The results
suggest that EHR data can be easily used to detect markers of clinically acute
events. Special software or analytic tools are not needed, even with irregular
EHR data.
