Community-based cardiovascular risk assessment using the cardisio^TM AI test: a prospective cohort study

Background Cardiovascular disease (CVD) accounts for significant morbidity and mortality disproportionately affecting hard-to-reach individuals. New technology that enables community testing rather than attending hospital may address health inequalities and facilitate new care pathways.

Aim We explored whether the Cardisio^TM test, which interprets three-dimensional vectorcardiography activity using a cloud-based AI algorithm, can identify asymptomatic cardiovascular disease.

Design & setting Prospective cohort study in three settings: general practice, pharmacy and a community health centre. Recruitment targeted asymptomatic adults aged≥18 years, with a QRISK3 score≥10% or CVD risk factors.

Method A 10 minute test using five electrodes (four chest, one back). The Cardisio^TM results are classified into red, amber, or green based on the Cardisio^TM test’s perfusion (P), structure (S), and arrhythmia (A) parameters. Pre- and post-test questionnaires provided feedback on their experience. Results reviewed by an independent consultant cardiologist (CI) and dealt with according to the Study Participants results and medical profile.

Results 628 tests were performed, 51% male (n=320), 49% (n=308) female, with a mean age of 54 years (18 -75 years). In the opinion of the CI there was a strong association between one or more Cardisio^TM red test results and referral to cardiology clinic being indicated (p=<0.001). The Test was understood as easy to perform, with a 87.5% recommendation rate among participants (n=492 of the 560).

Conclusion This simple near-patient test afforded high-risk hard-to-reach individuals access to a test more effective at identifying underlining cardiovascular disease than a traditional 12-lead ECG.