to 6.1 million people in the United States have atrial fibrillation and
that the condition contributes to approximately 130,000 deaths each
year, suggested the U.S. Centers for Disease Control and Prevention.
Dr. Dawood Darbar, chief of cardiology at the University of
Illinois Hospital & Health Sciences System, worked with a team of
physicians and researchers to answer one important question: Will a
widely accepted atrial fibrillation risk prediction algorithm work when
applied to the electronic medical record?
‘Old models for predicting the risk of atrial fibrillation are not compatible with electronic medical records (EMR). To be effective, the risk prediction models need to work when applied to EMR.’
Early identification of patients at risk for atrial fibrillation is
essential, Darbar said, to reduce the common and severe complications of
stroke, heart failure and death from atrial fibrillation, the most
common type of irregular heartbeat.
Risk prediction, he said, should leverage the technology routinely used in clinical practice.
"The electronic medical record is increasingly pervasive in clinical
practice, and it is a powerful tool in patient care," said Darbar, who
is also professor of medicine and pharmacology in the UIC College of
Medicine. "To be effective, risk prediction models need to work when
applied to EMRs, where they are most likely to be used by physicians to
identify subjects at risk for developing a disease."
In a study published in the journal JAMA Cardiology
and colleagues found that risk prediction models for atrial fibrillation
developed by investigators on the Cohorts for Heart and Aging Research
in Genomic Epidemiology (CHARGE) trial, did not accurately predict
incidence of the condition when it was applied to the EMRs of a large
group of patients.
The study looked retrospectively at the medical records of patients
who as of December 2005 did not have atrial fibrillation but returned
for follow-up care at least three times within the next two years.
Researchers used the EMR to follow their health for five years (through
December 2010) to see how many developed atrial fibrillation.
They found that the CHARGE-AF risk model, when applied to the EMR,
underpredicted the incidence of atrial fibrillation among low-risk
subjects - and overpredicted the incidence among high-risk subjects.
"This study illustrates the challenges of applying a predictive
model developed in prospective cohort studies to a real-world EMR
setting," Darbar said. "Ultimately, as health care advances, so will the
role of the EMR. It follows that risk models should be derived from and
validated in EMR cohorts, with an ultimate goal of developing
individualized preventive strategies."
The study suggests the model's failure to accurately predict atrial
fibrillation may be due to the different baseline characteristics of the
prospective and EMR cohorts. Darbar and his co-authors also acknowledge
some limitations of the study, including inconsistent EMR data-entry
procedures, and an "indication bias," wherein individuals who developed
atrial fibrillation likely had more clinical encounters than those who