Abstract 4137796: Novel Deep Learning Aligned Strain Techniques in Repaired Tetralogy of Fallot

Introduction: Multicenter studies show that demographic and CMR-based volumetrics and strain predict death, ventricular tachycardia and fibrillation (DVTF) in repaired tetralogy of Fallot (rTOF). We developed a novel deep learning method to calculate radial (RS) and circumferential strain (CS) from end-diastole (ED) to other key frames: mid systole (MS), end-systole (ES), peak flow (PF) in diastole, and mid-diastole (MD) (called ED2K), as well as from each key frame to the next (called K2K strain), for each left ventricle (LV) segment. Hypothesis: We hypothesized that: H1) ED-ES strain; H2) septal strain; and H3) diastolic strain would be additionally predictive of DVTF as compared to a traditional model. Approach: 704 patients combined from the German Competence Network and INDICATOR cohorts had ED2K and K2K strain values calculated using CMR short axis stack. We first created a 4-variable “traditional” logistic regression model including age at CMR, RVEF%, LVEF% and RVESVi. We then separately added ED2K and K2K parameters to assess increased ability to discriminate DVTF, measured by the c-statistic. Results: In univariate analyses, H1: ED to ES RS and CS; H2: multiple systolic septal and non-septal variables; and H3: overall diastolic RS and CS were significantly associated with DVTF, with c-statistics between 0.65 and 0.73 (n=56, Table 1). When added to the traditional model, H1: ED to ES CS; and H2: septal and non-septal CS slightly improved model performance (Table 2). Model calibration was adequate for all models. Conclusion: Our key-frame specific strain recapitulates the CS prediction of adverse events in rTOF, and slightly improves prediction in a multiparameter model. Future work includes external validation.