Predicting cardiovascular disease using indicators of oral infections

cardiovascular disease and markers for oral infections

A study aiming to develop and test a machine learning model to predict cardiovascular disease using indicators of oral infections was presented at the 52nd Annual Meeting & Exhibition of the AADOCR, held in conjunction with the 47th Annual Meeting of the CADR.

The AADOCR/CADR Annual Meeting & Exhibition took place at the Oregon Convention Center in Portland in March 2023.

The study by a team at the University of Pittsburgh, analysed the relationship between self-reported cardiovascular disease (heart surgery, heart valve, heart murmur, irregular heartbeat, and congenital heart disease) and markers for oral infections in 5188 subjects from the University of Pittsburgh School of Dental Medicine’s Dental Registry and DNA Repository project. 

Periodontal screening and recording data (PSR) available from 740 subjects and the decayed, missing, or filled teeth and surfaces (DMFT and DMFS) from 5010 subjects were used in the analyses.

The results pointed to a significant association between both DMFT and DMFS and cardiovascular disease that are independent of sex and tobacco use. The results of the analysis of covariance between DMFS and cardiovascular disease also remained significant after controlling for participants’ age. 

The machine learning model predicted whether a subject had cardiovascular disease based on their DMFS score with an accuracy of 84.3 per cent in the registry.

The study confirmed the association between dental caries and cardiovascular disease and highlighted the potential for machine learning methods to improve cardiovascular disease prediction using indicators of oral infections. 

Previous articleSpecial needs dentist Dr Trudy Lin is a true go-getter
Next articleGlobal patient safety and wellbeing research initiative invites grant applications


Please enter your comment!
Please enter your name here