AI-based fully automated left atrioventricular coupling index as a prognostic marker in patients undergoing stress-CMR

Abstract

The left atrioventricular coupling index (LACI) is a strong and independent predictor of heart failure (HF) in individuals without clinical cardiovascular disease. To determine in patients undergoing stress CMR whether fully automated artificial intelligence-based LACI can provide incremental prognostic value to predict HF. Between 2016 and 2018, we conducted a study including all consecutive patients with abnormal vasodilator stress CMR [inducible ischemia or late gadolinium enhancement (LGE)]. Control subjects with normal CMR were selected using propensity score-matching. LACI was defined as the ratio of LA to LV end-diastolic volumes. The primary outcome included hospitalization for acute HF or cardiovascular death using Cox regression. In 2662 patients [65 ± 12 years, 68% men, 1:1 matched patients (1331 with normal and 1331 with abnormal CMR)], LACI was positively associated with the primary outcome [median follow-up 5.2 (4.8–5.5) years] before and after adjustment for risk factors in the overall propensity-matched population [adjusted hazard ratio (HR), 5.94 (95%CI, 3.74–9.45) per 0.1% increment], patients with abnormal [adjusted HR, 6.38 (95%CI, 3.77–10.8) per 0.1% increment], and normal CMR [adjusted HR, 6.15 (95%CI, 2.97–12.7) per 0.1% increment; all P < 0.001]. After adjustment, a higher LACI of ≥ 25% showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-statistic improvement: 0.15; NRI = 0.705; IDI = 0.398, all P < 0.001; LR-test P < 0.001). LACI is independently associated with hospitalization for HF and cardiovascular death in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors including inducible ischemia and LGE (Fig. 1).