Researchers have developed a risk prediction tool that can identify individuals at high risk for developing coronary artery disease (CAD).
The tool is more accurate than existing models and could easily be integrated into electronic patient records or mobile applications, they say.
The American College of Cardiology/American Heart Association, European Society of Cardiology, and the UK National Clinical Guidance Centre for Acute and Chronic Diseases currently recommend using the Diamond and Forrester model, or the Duke Clinical score, to estimate the pretest probability of CAD in patients with chest pain.
However, the accuracy of these tools has been questioned.
The team, led by Myriam Hunink from Erasmus University Medical Centre in Rotterdam, The Netherlands, therefore aimed to develop prediction models that better estimate the pretest probability of CAD in low prevalence populations.
Using data from 18 hospitals in Europe and the US, the researchers included a total of 5,677 patients with stable chest pain without evidence for previous CAD in their study. Of these, 1,634 had obstructive CAD, defined as more than 50 % diameter stenosis in at least one vessel found on catheter based coronary angiography.
Their basic model predicted CAD according to age, gender, symptoms, and setting. A clinical model included diabetes, hypertension, dyslipidemia, and smoking, while an extended model also used the computed tomography-based coronary calcium score.
As reported in the BMJ, all potential predictors were significantly associated with the presence of disease. Age, gender, symptoms, and coronary calcium score were strong predictors for disease.
The results showed that the Duke clinical score significantly overestimated the probability of CAD, which, the researchers say, highlights the need for an updated predictive model.
Their predictive model predicted probabilities between 2 % for a 50-year-old woman with non-specific chest pain without any risk factors and 91 % for an 80-year-old man with typical chest pain and multiple risk factors.
The researchers conclude that implementation of these models could improve clinical outcomes, but further evaluation may still be needed.
By Nikki Withers