Focused cardiac ultrasound: Competency among pre‐internship medical officers in diagnosing cardiac causes of dyspnea

Background Differentiating cardiovascular causes of dyspnea in resource‐limited healthcare settings can be challenging. The use of easy‐to‐train, point‐of‐care, focused cardiac ultrasound (FoCUS) protocols may potentially alleviate this challenge. Research Question Can novices attain competency in FoCUS use after training using the cardiac ultrasound for resource‐limited settings (CURLS) protocol? Methods A quasi‐experimental study was conducted at the Kenyatta National Hospital in Nairobi, Kenya. Forty‐five graduate medical pre‐interns, novices in cardiac ultrasound, received simulated didactic and hands‐on FoCUS skills training using the CURLS protocol and 2018 European Association of Cardiovascular Imaging (EACVI) FoCUS training and competence assessment recommendations. Competency was assessed in image interpretation, image acquisition, and image quality. Results Aggregate image interpretation competency was attained by n = 38 (84%) of trainees with a median score of 80%. The proportion of trainees attaining category‐specific image interpretation competency was as follows: pericardial effusion n = 44 (98%), left atrial enlargement n = 40 (89%), cardiomyopathy n = 38 (84%), left ventricular hypertrophy n = 37 (82%), and right ventricular enlargement n = 29 (64%). Image acquisition skills competency was attained by n = 36 (80%) of trainees. Three‐quarters of trainee‐obtained images were of good quality. Conclusion Majority of the trainees attained competency. Training constraints limit the generalizability of our findings. We trained 45 novice medical officer pre‐interns in cardiovascular dyspnea evaluation using cardiac ultrasound for resource‐limited settings protocol and assessed competency. Outcomes: 84% attained competency in image interpretation, 80% in image acquisition, and 69% obtained good quality images. Focused cardiac ultrasound trainings are potentially effective in resource‐limited settings, but training constrains limit results generalizability.