Abstract Introduction Speckle-tracking echocardiography is an increasingly important tool for assessing left ventricular non-compaction phenotype (LVNC). This study aims to analyse left ventricular early diastolic strain rate (eSR) in LVNC and explore its association with CV outcome during long-term follow-up. Methods Fifty-nine patients meeting prespecified criteria were included from our prospective LVNC cohort. eSR was determined using TomTec ImageArena (Figure A). A combined endpoint (cardiovascular events) was defined including atrial flutter/fibrillation, sustained ventricular arrhythmias, aborted cardiac arrest, and cardiovascular mortality. Results Baseline characteristics were comparable between patients reaching the endpoint (16 [27%]) and those without (43 [73%]) except for a higher prevalence of ischemic heart disease in the former (event group: 13%; no-event group: 2%; P=0.019). In the event group, there was a non-significant tendency for a lower LVEF (38 [26–56]% vs 51 [43–56]%; P=0.091) and LVGLS (-12.3 [-16.3 to -9.7]% vs -15.2 [-17.7 to -12.8]%; P=0.111). In contrast, eSR was significantly lower in the event group (0.45 [0.30–0.65]s-1 vs 0.70 [0.39–0.93]s-1; P=0.018; Figure B). Patients with an eSR ≤ 0.70 s-1 (ROC AUC 70%; P=0.005) exhibited a lower event-free probability in Kaplan-Meier survival analysis (P=0.001; Figure C). Inclusion of eSR improved the fitness (ꭓ2) of a multivariable logistic regression model for clinical characteristics (age, gender, and NC:C ratio; Figure D), while LVEF and LVGLS did not (Table). This improvement in ꭓ2 by eSR was significant (ANOVA P=0.033), while that by LVEF or LVGLS was not (P=0.302 and 0.152, respectively). Conclusions eSR was lower in LVNC patients with events, differentiated those with events from those without, and was associated with a higher risk of events during long-term follow-up. Outcome association of eSR was independent of age, gender, and NC:C ratio and improved their event prediction. eSR has potential value for functional characterization and outcome prediction in LVNC.