Community-based cardiovascular risk assessment using the cardisioTM AI test: a prospective cohort study.

Abstract

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.