Key result
Image quality on handheld ultrasound devices was associated with the feasibility of autoEF and autoMAPSE (P<0.001) but did not explain their low agreement with reference evaluations.
Why the study?
The impact of user experience and image quality on automatic quantification of EF and MAPSE using handheld ultrasound devices in suspected HF was unclear.
Does image quality and user experience influence the feasibility, agreement, and reliability of automated LV function quantification on handheld ultrasound devices in patients with suspected heart failure?
Observational
Single-blind
Does image quality and user experience influence the feasibility, agreement, and reliability of automated LV function quantification on handheld ultrasound devices in patients with suspected heart failure?
p-value: p=<0.001
While image quality improves the feasibility of automated LV function quantification on handheld ultrasound devices, it does not improve its agreement with reference standards or within-patient reliability across user experience levels.
Image quality may boost feasibility of automated LV measures on handheld devices but not agreement; leaves open other factors limiting reliability in suspected HF.
Background and objectives Echocardiography is the cornerstone of heart failure (HF) diagnosis, but expertise is limited. Non-experts using handheld ultrasound devices (HUDs) challenge the clinical yield. Left ventricular (LV) ejection fraction (EF) is used for assessment and grading of HF. Mitral annular plane systolic excursion (MAPSE) reflects LV long-axis shortening. Automatic tools for quantification of EF (autoEF) and MAPSE (autoMAPSE) are available on HUDs. We aimed to explore the importance of user experience and image quality for autoEF and autoMAPSE on HUDs, and how image quality influences the feasibility, agreement and reliability in patients with suspected HF. Methods General practitioners, registered cardiac nurses and cardiologists represented the novice, intermediate and expert users, respectively, in this diagnostic accuracy study. 2543 images were evaluated by an external, blinded cardiologist by a five-parameter, prespecified score (four-chamber view, LV alignment, apical mispositioning, mitral annular assessment and number of visible endocardial segments) graded 0–6. Results Feasibility was higher with increasing image quality. In all recordings, irrespective of user, the average image quality score and the five prespecified scores were associated with the feasibility of autoEF and autoMAPSE (all p<0.001). Image quality was more important for the feasibility of autoMAPSE than autoEF. Image quality was not important for the agreement of autoEF (R2 2%) and autoMAPSE (R2 7%). Combining all user groups, the reliability was lower with larger within-patient variability in image quality of the repeated recordings (p≤0.005). Similar associations were not found in user group specific analyses (p≥0.16). Patients’ characteristics were only weakly associated with image quality score (R2≤4%). Discussion Image quality was important for feasibility but does not explain the low agreement with reference or the modest within-patient reliability of automatic decision-support software on HUDs for all user groups in patients with suspected HF.
No takes yet. Share an insight, caveat, or question.
A 2022 study conducted an observational in Suspected heart failure. Automated real-time quantification of left ventricular function (autoEF and autoMAPSE) on handheld ultrasound devices vs. Reference evaluation by blinded cardiologist was evaluated on Feasibility, agreement, and reliability of autoEF and autoMAPSE (p=<0.001). Image quality on handheld ultrasound devices was associated with the feasibility of autoEF and autoMAPSE (P<0.001) but did not explain their low agreement with reference evaluations.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: