Abstract 15813: Machine Learning Improves Prediction of Heart Failure and Cardiovascular Death Through a Signal Analytical Approach of Echocardiography
Jakob O Simonsen, Rune Hoejlund, Daniel Modin, Kristoffer G Skaarup, Jacob Christensen, Mats Lassen, Niklas D Johansen, Sergio Sanchez, Brian Claggett, Jacob Marott, Magnus Thorsten, Gorm Jensen, Peter Schnohhr, Rasmus Mogelvang, Tor Biering-Srensen- Physiology (medical)
- Cardiology and Cardiovascular Medicine
Introduction: Recently, speckle tracking echocardiography (STE) and tissue Doppler imaging (TDI) have gained increasing traction as non-invasive tissue characterization methods within cardiology. But until now many patterns from the strain and TDI curves remain uninvestigated. Signal analytical methods like wavelet analysis have shown promising potential in effectively identifying previously unknown pathological patterns present in ECG signals. Therefore, we hypothesized that a signal analytic approach combined with supervised machine learning (ML) on strain and TDI curves could identify unknown pathophysiological deformation drivers of heart failure (HF) and cardiovascular death (CV death) in the general population.
Methods: The analysis included 3781 subjects from the general population. In total 720 novel statistical parameters and wavelet signal parameters from 18 strain curves and 6 TDI curves were generated. The parameters were used to train an ensemble decision tree with 20-fold cross-validation and compared to a baseline ML model trained on 22 conventional echocardiographic parameters.
Results: Follow up-time was four years. In total 108 subjects (2.9%) met the outcome. By including statistical and wavelet-derived parameters along with 22 conventional echocardiographic parameters in a combined model, the ML model was significantly improved compared to the baseline model (AUC for conventional model: 0.76 vs combined model: 0.825, p = 0.0086). Both statistical parameters, such as the mean and skewness of the TDI curve, as well as wavelet parameters, such as mean on the first decomposition level of the strain curve, were found to be important in the model.
Conclusion Adding novel echocardiographic parameters based on signal analytical statistics and wavelet decomposition methods to preexisting conventional echocardiographic parameters significantly improved the prediction of incident HF or CV death in ML models.