Nurse-led home-based detection of cardiac dysfunction by ultrasound: Results of the CUMIN pilot study

Abstract

Access to echocardiography is a significant barrier to heart failure (HF) care in many low- and middle-income countries. We hypothesised that an artificial intelligence (AI) enhanced point-of-care ultrasound (POCUS) device could enable the detection of cardiac dysfunction by nurses in Tunisia. The CUMIN study was a prospective feasibility pilot assessing the diagnostic accuracy of home-based AI-POCUS for HF conducted by novice nurses compared to conventional clinic-based transthoracic echocardiography (TTE). Seven nurses underwent a one-day training program in AI-POCUS. 94 patients without a previous HF diagnosis received home-based AI-POCUS, POC NTproBNP testing, and clinic-based TTE. The primary outcome was the sensitivity of AI-POCUS in detecting left ventricular ejection fraction (LVEF) <50% or left atrial volume index (LAVI) >34 mL/m2, using clinic-based TTE as the reference. Out of 7 nurses, 5 achieved a minimum standard to participate in the study. Out of 94 patients (60% women, median age 67), 16 (17%) had LVEF<50% or LAVI >34 mL/m2. AI-POCUS provided interpretable LVEF in 75 (80%) patients and LAVI in 64 (68%). The only significant predictor of an interpretable LVEF or LAVI proportion was the nurse operator. The sensitivity for the primary outcome was 92% (95% CI 62-99) for AI-POCUS compared to 87% (95% CI 60-98) for NT-proBNP>125 pg/mL, with AI-POCUS having a significantly higher AUC (P=0.040). The study demonstrated the feasibility of novice nurse-led home-based detection of cardiac dysfunction using AI-POCUS in HF patients, which could alleviate the burden on under-resourced healthcare systems