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

Abstract

Abstract Background The left atrioventricular coupling index (LACI) is a strong and independent predictor of heart failure (HF) in individuals without clinical cardiovascular disease. Its prognostic value is not established in patients with cardiovascular disease. Purpose To determine in patients undergoing stress CMR whether fully automated artificial intelligence-based LACI can provide incremental prognostic value to predict HF. Methods Between 2016 and 2018, we conducted a longitudinal study including all consecutive patients with abnormal (inducible ischemia or late gadolinium enhancement (LGE)) vasodilator stress CMR. Control subjects with normal stress 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. Cox regression was used to evaluate the association of LACI with the primary outcome after adjustment for traditional risk factors. Results In 2,134 patients [65±12 years, 77% men, 1:1 matched patients (1,067 with normal and 1,067 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], 1.18 [95%CI, 1.13-1.24] per 0.1% increment), patients with abnormal (adjusted HR, 1.22 [95%CI, 1.14-1.30] per 0.1% increment), and normal CMR (adjusted HR, 1.12 [95%CI, 1.05-1.20] 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: 0.16; NRI=0.388; IDI=0.153, all p<0.001; LR-test p<0.001). Conclusion 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.LACI measurement method using CMRKaplan-Meier curves by LACI value