A train-the-trainer curriculum to scale up artificial intelligence-supported echocardiography for rheumatic heart disease screening in a public health care system.

BACKGROUND: This study aimed to test a train-the-trainer model for scaling up echocardiographic screening for rheumatic heart disease (RHD).

METHODS: This was a two-phased, prospective, pilot cohort study conducted in Lira, Uganda. In phase one, four nurse centres underwent a three-day training on echocardiographic screening for RHD. Thereafter, nurses integrated screening of patients aged 5-40 years into their routine clinic workflow. Echocardiograms were uploaded to a cloud-based server for review by an expert, who served as the gold standard for interpretation. In phase two, 12 nurses were trained by trainees included in phase one.

RESULTS: A total of 16 nurses (four in phase one and 12 in phase two) were recruited. All nurses achieved the target of acquiring 100 echocardiograms, resulting in a total of 1620 screening echocardiograms (n = 406 in phase one and n = 1214 in phase two).Over 95% of the studies were of diagnostic quality. There were 44/1473 (3%) screen-positive participants, 40 with RHD. There was a decline in sensitivity from 70% to 50% (p = 0.33) and an increase in specificity from 86% to 94% (p < 0.0001) between phases one and two. All four cases of moderate/severe RHD were correctly identified. In phase one, 56 cases were incorrectly classified as left ventricular dysfunction using the auto ejection fraction function; this option was omitted for phase two.

CONCLUSION: This pilot study showed that utilising a train-the-trainer model to implement echocardiographic screening for RHD into primary health care in a low-resource setting is feasible.