Overview

Transthyretin amyloid cardiomyopathy is a progressive and fatal disease. While TTR-specific therapies reduce mortality, up to 30% of patients continue to progress, underscoring the need for reliable, sensitive monitoring tools. Echocardiography is widely available and cost-effective, yet real-world data on echocardiographic changes over time and their response to therapy have remained limited. This study investigated whether AI-based echocardiography can detect attenuation of disease progression in response to disease-modifying therapy.

 

Study Design

This retrospective cohort study included 335 patients with wild-type ATTR-CM from two multisite registries in Zurich and Bern. All echocardiograms from multiple time points were fully quantified using Us2.ai, with NT-proBNP values extracted from clinical records. The time scale was aligned with initiation of disease-modifying therapy at time zero. Untreated patients were modelled as pre-treatment. Only data from six years before to six years after treatment initiation were included to avoid outlier effects. Changes over time were analysed using linear mixed-effects models with restricted cubic splines.

 

Key Results

  • 335 patients included (93% male, median age 78 years); median 4 echocardiograms per patient; 73% received disease-modifying therapy
  • All metrics worsened before treatment initiation (all p<0.05)
  • After therapy initiation, significant attenuation of progression was observed for IVST (p for non-linearity=0.001), LV GLS (p=0.008), TAPSE (p<0.001), LA volume (p<0.001), LA reservoir strain (p<0.001), and NT-proBNP (p=0.005)
  • E/e' even improved after treatment initiation (p<0.001)

 

Why This Matters

Monitoring treatment response in ATTR-CM requires sensitive tools capable of detecting subtle changes across serial assessments. This study demonstrates that Us2.ai can automatically quantify multiple echocardiographic parameters across longitudinal datasets, detecting meaningful signals of disease attenuation that support clinical decision-making about therapy continuation and intensity. The ability to automate this analysis at scale, without operator variability, is particularly valuable for long-term monitoring programmes.

 

Conclusion

AI-based echocardiography captures early attenuation of disease progression after disease-modifying therapy in ATTR-CM, supporting its use as a sensitive and accessible tool to monitor treatment response in this population.

 

 

Walser, A., Clerc, O. F., Flammer, A. J., Hundertmark, M. J., Shiri, I., Schwotzer, R., Ruschitzka, F., Tanner, F. C., Gräni, C., & Benz, D. C. (2026, August 28-31). Changes over time in AI-based echocardiographic metrics before and after therapy in ATTR cardiomyopathy [ePoster presentation]. ESC Congress 2026, Munich, Germany.