Ecocardiograma portátil: ¿qué añade a la valoración cardiovascular inicial?

Martín-Peñato, Arturo; Zamorano, José Luís; Almerı́a, Carlos; Rodrigo, José Luis; Corros, Cecília; Luaces, María; Conde, Antonio Zárate; Vilacosta, Isidro; Macaya, Carlos

doi:10.1016/s0300-8932(03)77017-1

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the remaining two studies that used novice FoCUS operators, the sensitivity and specificity for an abnormal LVEF (defined as less than 50%) was 85–86% and 82–89%, respectively 21 , 22 . Furthermore, there are only two studies that classified the severity of LV dysfunction, with both studies using experienced operators 18 , 19 . While pivotal to the growth of FoCUS, there are methodologic concerns that limit the applicability of these studies to clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of point-of-care ultrasound with artificial intelligence-assisted assessment of left ventricular ejection fraction

Motazedian,

Marbach,

Prosperi-Porta

et al. 2023

npj Digit. Med.

View full text Add to dashboard Cite

Focused cardiac ultrasound (FoCUS) is becoming standard practice in a wide spectrum of clinical settings. There is limited data evaluating the real-world use of FoCUS with artificial intelligence (AI). Our objective was to determine the accuracy of FoCUS AI-assisted left ventricular ejection fraction (LVEF) assessment and compare its accuracy between novice and experienced users. In this prospective, multicentre study, participants requiring a transthoracic echocardiogram (TTE) were recruited to have a FoCUS done by a novice or experienced user. The AI-assisted device calculated LVEF at the bedside, which was subsequently compared to TTE. 449 participants were enrolled with 424 studies included in the final analysis. The overall intraclass coefficient was 0.904, and 0.921 in the novice (n = 208) and 0.845 in the experienced (n = 216) cohorts. There was a significant bias of 0.73% towards TTE (p = 0.005) with a level of agreement of 11.2%. Categorical grading of LVEF severity had excellent agreement to TTE (weighted kappa = 0.83). The area under the curve (AUC) was 0.98 for identifying an abnormal LVEF (<50%) with a sensitivity of 92.8%, specificity of 92.3%, negative predictive value (NPV) of 0.97 and a positive predictive value (PPV) of 0.83. In identifying severe dysfunction (<30%) the AUC was 0.99 with a sensitivity of 78.1%, specificity of 98.0%, NPV of 0.98 and PPV of 0.76. Here we report that FoCUS AI-assisted LVEF assessments provide highly reproducible LVEF estimations in comparison to formal TTE. This finding was consistent among senior and novice echocardiographers suggesting applicability in a variety of clinical settings.

show abstract