Feasibility and Reproducibility of Left Atrium Measurements Using Different Three-Dimensional Echocardiographic Modalities.
Andreea Iulia MotocBram RoosensEsther ScheirlynckKaoru TanakaMaria Luiza LuchianJulien MagneGiulia Elena MandoliRocio HinojarMatteo CameliJose Luis ZamoranoSteven DroogmansBernard CosynsPublished in: Diagnostics (Basel, Switzerland) (2020)
Left atrium (LA) volume is a biomarker of cardiovascular outcomes. Three-dimensional echocardiography (3DE) provides an accurate LA evaluation, but data regarding the optimal 3DE method is scarce. We assessed the feasibility and reproducibility of LA measurements using different 3DE methods. One hundred and ninety-four patients were prospectively analyzed. Conventional 3DE and two semi-automatic 3DE algorithms (Tomtec™ and Dynamic Heart Model (DHM)) were used in 110 patients. Intra- and interobserver reproducibility and intervendor comparison were performed in additional patients' subsets. Forty patients underwent cardiac magnetic resonance (CMR). Feasibility was 100% for Tomtec, 98.2% for DHM, and 72.8% for conventional 3DE. Tomtec volumes were higher than 3DE and DHM (p < 0.001). Reproducibility was better for DHM (intraobserver LA maximum volume (LAmax) ICC 0.99 (95% CI 1.0-0.99), LA minimum volume (LAmin) 0.98 (95% CI 0.95-0.99), LApreA 0.96 (95% CI 0.91-0.98); interobserver LAmax ICC 0.98 (95% CI 0.96-0.99), LAmin 0.99 (95% CI 0.99-1.00), and LApreA 0.97 (95% CI 0.94-0.99)). Intervendor comparison showed differences between left ventricle (LV) software adapted for LA (p < 0.001). Tomtec underestimated the least LA volumes compared to CMR. These findings emphasize that dedicated software should be used for LA assessment, for consistent clinical longitudinal follow-up and research.
Keyphrases
- end stage renal disease
- ejection fraction
- chronic kidney disease
- magnetic resonance
- newly diagnosed
- machine learning
- prognostic factors
- heart failure
- magnetic resonance imaging
- pulmonary hypertension
- deep learning
- high resolution
- atrial fibrillation
- mitral valve
- patient reported outcomes
- cross sectional
- data analysis
- artificial intelligence
- big data
- neural network