Researchers Use EMRs to Predict Risk of Stroke

Oct. 2, 2017
A team of researchers in California is using electronic medical records (EMRs) to predict the likelihood of a person experiencing atrial fibrillation—an irregular heartbeat that could result in a second stroke—after certain types of strokes.

A team of researchers in California is using electronic medical records (EMRs) to predict the likelihood of a person experiencing atrial fibrillation—an irregular heartbeat that could result in a second stroke—after certain types of strokes.

According to an article from the Stanford Medicine news center, one important risk factor for the dangerous second stroke is an irregular heart beat called atrial fibrillation. If doctors could identify the stroke patients who are most likely to experience atrial fibrillation, they could start treatments that would help prevent a second stroke.

As such, a team led by researchers at the Stanford University School of Medicine and Santa Clara Valley Medical Center is leveraging EMR data to predict the likelihood of a person experiencing atrial fibrillation after either of two kinds of strokes: a cryptogenic stroke or a transient ischemic attack. A paper describing their findings was published online June 28 in Cardiology.

The core idea behind the research is that stroke patients are typically monitored for atrial fibrillation while they’re in the hospital, but that constant monitoring is reduced significantly when they get discharged. But if doctors monitor stroke patients for even 30 days after they go home, atrial fibrillation can be picked up if it’s happening. This is why the American Heart Association recommends 30 days of heart rhythm monitoring to detect atrial fibrillation within six months of an initial stroke, the article pointed out.

But the researchers also noted that not every patient needs that monitoring; thus, the focus turned to finding a way to tell the patients who were at high risk for atrial fibrillation and should be monitored from the ones who were at low risk and didn’t need to be monitored.

The research team did a retrospective cohort study using data from thousands of stroke patients from Stanford’s Translational Research Integrated Database Environment. Of the 9,589 stroke patients in the database, 482 of them, or 5 percent, went on to be diagnosed with atrial fibrillation.

The team had already previously developed a text-processing pipeline for analyzing clinical data and clinical-diagnosis coding. Using that pipeline, the team extracted information from clinical notes, flagging, for example, phrases such as “ruled out stroke” and classifying data according to whether it referred to the patient or came from a family history section. The result was a list of biomedical facts about each patient—including age, body mass index and so on.

Then, by ranking the clinical attributes of patients whose medical records indicated they went on to be diagnosed with atrial fibrillation, the team was able to assemble a set of seven risk factors that, when combined, predicted which stroke patients were the most likely to develop the condition and should be monitored after hospitalization. The risk factors were: age, obesity, congestive heart failure, hypertension, coronary artery disease, peripheral vascular disease and disease of the heart valves. These are the basis of a scoring system that assigns patients to one of three risk groups.

The scoring system we developed is simple to use and the results could help physicians tailor treatment to individual patients,” said Stanford graduate student Albee Ling, one of the paper’s lead authors.

This scoring system can help physicians decide which patients to monitor. Once it’s known that patients have a high risk of atrial fibrillation, they can wear a heart monitor at home to see if they actually are experiencing bouts of atrial fibrillation and then, if they are, treated with the appropriate drugs to try to prevent a second stroke, the researchers noted.

“Our system needs to be further validated in studies using other independent data sources,” said Ling. She said she expects that clinicians and researchers will further validate and improve the scoring system and that, hopefully, it will one day be adopted in everyday practice. “On the other hand, there will surely be more clinical studies conducted using electronic health records, not just at Stanford but in other medical institutions, as well,” she said.

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