A new predictive tool in the form of a risk algorithm--dubbed QStroke--can help to identify and treat patients at risk of having a stroke, according to research recently published in BMJ.
The algorithm uses a statistical model and established risk factors, based on variables that patients are likely to know, according to researchers from the University of Nottingham. They tested a large group--3.5 million patients between the ages of 25 and 84.
The results found that the QStroke algorithm explained 57 percent of the variation in women and 55 percent in men without a prior stroke.
"QStroke also provides an accurate measure of absolute stroke risk in the general population of patients free of stroke or transient ischaemic attack, as shown by its performance in a separate validation cohort," the researchers said.
According to the researchers, the algorithm can be easily updated to account for changes in populations and improvements in data quality, as well as evolving guidelines.
"The algorithms can also be implemented in primary care since the data are already present in the clinical computer systems," they said. "QStroke will work both in populations with atrial fibrillation and those without atrial fibrillation--though the immediate clinical use might be for risk stratification among patients with atrial fibrillation, QStroke can still inform other patients of their specific risk of stroke or transient ischaemic attack as part of their general cardiovascular risk assessment.
Lead author Julia Hippisley-Cox, from the University of Nottingham's Division of Primary Care, said that while further research is needed to evaluate clinical outcomes and cost effectiveness, she's confident that the study has "good validity."
"[The study] was conducted in general practice, where most patients are assessed, treated and followed up, and where there are good levels of accuracy and completeness in recording diagnoses and prescribed drugs," Hippisley-Cox said.