Aims In transthyretin amyloid cardiomyopathy ( ATTR ‐ CM ), reduced stroke volume ( SV ) portends a poor prognosis. Artificial intelligence ( AI ) enables rapid, standardized assessment of left ventricular outflow tract velocity‐time integral ( LVOT ‐ VTI ), which is a reliable surrogate for SV . We investigated longitudinal changes in AI ‐derived LVOT ‐ VTI as outcome predictors in ATTR ‐ CM. Methods and results Consecutive patients with ATTR ‐ CM underwent baseline and 12 ± 1 month transthoracic echocardiography between 2007 and 2021. Scans were processed by an AI platform for fully automated measurements including LVOT ‐ VTI . Changes in echocardiographic variables were related to all‐cause mortality in a landmark analysis using multivariable Cox models adjusting for clinical covariates (age, sex, TTR genotype, atrial fibrillation status, New York Heart Association class and National Amyloidosis Centre stage). Time‐dependent receiver‐operating characteristic analysis identified the optimal threshold of LVOT ‐ VTI change. A total of 752 patients (74 ± 9 years; 88% men; 66% wild‐type) were followed for a median of 3.3 years (interquartile range 2.1–5.0 years), during which 334 (44.4%) died. Among changes in echocardiographic parameters over 12 months, only LVOT ‐ VTI change remained independently prognostic (adjusted hazard ratio HR per 1% decrease 0.994, p = 0.025). A ≥5% decrease ( n = 377 patients, 50%) independently predicted all‐cause mortality (adjusted HR 1.41, 95% confidence interval 1.13–1.76; p = 0.003), and improved risk reclassification (integrated discrimination improvement = 0.012; continuous net reclassification improvement = 0.21, both p < 0.001). Conclusions A ≥5% decrease of AI ‐derived LVOT ‐ VTI over 12 months, a simple indicator of SV loss, is independently associated with worse outcome in ATTR ‐ CM . Routine monitoring of this automated AI metric may guide earlier therapeutic escalation and is a possible endpoint for future trials.
Venneri et al. (Thu,) studied this question.