HumanInsight User experience and image quality influence on performance of automated real-time quantification of left ventricular function by handheld ultrasound devices: a diagnostic accuracy study with data from general practitioners, nurses and cardiologists
Open Heart. 2022 Oct;9(2):e002083. doi: 10.1136/openhrt-2022-002083.
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.
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