A model has been developed which predicts the number of years of life without cardiovascular disease (CVD) people with type 2 diabetes may benefit from should they take certain medications.
This prediction algorithm has been developed by the University Medical Center Utrecht, the Netherlands, and could help to facilitate personalised treatment regimens.
“With easy-to-measure patient characteristics, we can estimate 10-year CVD risk and CVD-free life expectancy for patients with type 2 diabetes,” said Gijs Berkelmans, MD, at the European Association for the Study of Diabetes (EASD) 2017 Annual Meeting.
Now, the effects of aspiri, blood pressure-lowering and lipid-lowering medication, all preventative therapies against CVD, can be analysed for individual patients to assess their risk of CVD.
“Our developed algorithm is able to estimate median life expectancy free of CVD and the treatment effects of lifelong preventive treatment in years/months gained without CVD … for patients between 30 and 95,” added Berkelmans.
Because people with type 2 diabetes have a higher risk of CVD, the researchers developed the algorithm to see what extent patients gained from preventative therapies.
A total of 389,366 people with type 2 diabetes were involved, with model inputs including gender, HbA1c, smoking status and blood pressure. The CVD events logged were stroke, myocardial infarction (heart attack) and stroke.
The model used data from 75 per cent of the cohort, with treatment decision-support validated in the remaining patients.
Berkelmans explained that one patient, a 55-year-old man with high blood pressure and cholesterol who was taking aspirin and statins, was estimated to have a 10-year risk of CVD of 27.1%. Following intensified treatment the patient had a 9.9% reduction in his 10-year CVD risk.
The manuscript for the decision-support tool and algorithm is set to be published later this year. Berkelmans is optimistic that further research could see the tool developed to include other preventative type 2 diabetes drugs such as SGLT2 inhibitors and GLP-1 agonists.

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