Pocket-sized echocardiography for screening structural heart disease: diagnostic accuracy and cost-effectiveness for population-based studies

Hand-held echocardiography during complex electrophysiologic procedures

Introduction

Complex electrophysiologic (EP) procedures are time consuming and open to complications. Accurate and rapid recognition of cardiac pathologies is essential before, during, and immediately after such procedures. In this study, we aimed to compare hand-held echocardiography (HHE) with standard echocardiography (SE) to determine whether HHE can be used as a practical and reliable diagnostic tool during such procedures.

Methods

One hundred consecutive patients undergoing complex EP procedures and catheter ablation were included in the study. All patients were evaluated with SE or HHE in terms of main cardiac pathologies at the beginning and immediately after the procedure. The diagnostic accuracy and evaluation time of both methods were compared at the beginning and after the procedure. The agreement between both methods was calculated.

Results

At the beginning and after the procedure, opening and evaluation times with HHE were significantly shorter than with SE (P<0.001 for all). There was significant agreement between the two methods in the diagnosis of cardiac pathologies (Agreement was 95% for minimal mild aortic regurgitation (AR), 99% for moderate/ severe AR, 93% for minimal/ mild mitral regurgitation (MR), 95% for moderate/ severe MR, 100% for pericardial effusion, and 100% for left ventricular thrombus at the beginning of the procedure).

Conclusion

With the use of HHE during complex EP procedures, cardiac pathologies can be diagnosed with similar accuracy as SE. In addition, HHE has a significant advantage over SE in terms of time to diagnosis.

Clinical validation of an artificial intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in real time by a novel handheld ultrasound device

Aims

We sought to evaluate the reliability and diagnostic accuracy of a novel handheld ultrasound device (HUD) with artificial intelligence (AI) assisted algorithm to automatically calculate ejection fraction (autoEF) in a real-world patient population.

Methods and results

We studied 100 consecutive patients (57 ± 15 years old, 61% male), including 38 with abnormal left ventricular (LV) function [LV ejection fraction (LVEF) < 50%]. The autoEF results acquired using the HUD were independently compared with manually traced biplane Simpson's rule measurements on cart-based systems to assess method agreement using intra-class correlation coefficient (ICC), linear regression analysis, and Bland-Altman analysis. The diagnostic accuracy for the detection of LVEF <50% was also calculated. Test-retest reliability of measured EF by the HUD was assessed by calculating the ICC and the minimal detectable change (MDC). The ICC, linear regression analysis, and Bland-Altman analysis revealed good agreement between autoEF and reference manual EF (ICC = 0.85; r = 0.87, P < 0.001; mean bias -1.42% with limits of agreement 14.5%, respectively). Detection of abnormal LV function (EF < 50%) by autoEF algorithm was feasible with sensitivity 90% (95% CI 75-97%), specificity 87% (95% CI 76-94%), PPV 81% (95% CI 66-91%), NPV 93% (95% CI 83-98%), and a total diagnostic accuracy of 88%. Test-retest reliability was excellent (ICC = 0.91, P < 0.001; r = 0.91, P < 0.001; mean difference ± SD: 0.54% ± 5.27%, P = 0.308) and MDC for LVEF measurement by autoEF was calculated at 4.38%.

Conclusion

Use of a novel HUD with AI-enabled capabilities provided similar LVEF results with those derived by manual biplane Simpson's method on cart-based systems and shows clinical potential.