Prognostic value of a normal Artificial Intelligence applicative response in subjects with stable chest pain. From the ARTICA co-operative registry. (25th November 2020)

• Record Type: Journal Article
• Authors: Mazzanti, M; Shirka, E; Marini, M; Pottle, A; Goda, A; Pugliese, F
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: e-Health; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.3504
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: An innovative artificial intelligence (AI) Decision Support System (DSS) ESC guidelines based has already been used at point of care with efficacy for evaluating subjects with stable chest pain (SCP) and it has been proved to correctly identify absence of significant coronary artery disease (CAD) versus standard care approach (SD) without known prognostic implication yet. Purpose: The aim is to determine the prognostic value of "no testing/Follow up" AI DSS response in a referral population evaluated for SCP. Methods: From 2016 to 2019, an AI DSS ESC guidelines based applicative was used on 1.291 subjects with SCP to determine testing appropriateness compared with human specialist standard evaluation. 590 of them, 332 males, mean age 62±11 years deemed to be completely negative – by "no testing/follow up" response - were evaluated. The negative response was defined and confirmed by a normal Coronary Tomography Angiography scan executed in all these subjects. Mean follow-up was 3.46±1.76 years. Two groups based on pre-test likelihood of having CAD were analyzed – low and intermediate. No subjects with high pre-test likelihood were present. The primary endpoint was cumulative incidence of cardiovascular death, hospitalizations for acute coronary syndrome and coronary revascularizations. Results: The primary endpoints classification is displayed in the Table. The unadjusted hazard ratio for primary endpoint was 3.84 (95% CI 0.32–8.68, p=0.009) in patients with intermediate compared to those with low pre-test likelihood of CAD. Moreover, the "no testing and Follow up" response showed an incremental prognostic value over conventional risk factors (χ 2 =7, P=0.022) and over a combination of conventional factors and ST-T changes (χ 2 =9, P=0.014). Conclusions: In an outpatient population without known CAD evaluated for chest pain, after the administration of AI DSS, a "No tests/Follow up" confers an excellent prognosis regardless of the ESC SCORE Charts and rest ECG abnormalities. These preliminary data confirms the safety of ARTICA AI DSS use in subjects with stable chest pain. Funding Acknowledgement: Type of funding source: None