Efficacy and feasibility of heart failure nurses to deliver a lungs and inferior vena cava ultrasound assessment (LUICA) protocol and prediction of outcomes. (14th October 2021) - Zisis, G; Yang, Y; Whitmore, K; Lay, M; Huynh, Q; Neil, C; Carrington, M; Marwick, T H
Abstract: Background/Introduction: Congestion is the main cause for hospital admission and readmission in heart failure (HF), with almost half being discharged with symptoms that suggest residual congestion. Novel non-invasive assessments (eg. lung ultrasound; [LUS], and Inferior Vena Cava [IVC]) may be used to assess congestion. A LUs and IvC Assessment (LUICA) delivered by HF nurses before discharge, could detect residual congestion, optimise diuresis and guide post discharge treatment. Purpose: To determine the ability of HF nurses to successfully learn a LUICA protocol, obtain interpretable images, provide diagnostic reports and predict outcomes. Methods: A teaching program focused on quantification of congestion by counting B-lines and reporting estimated right atrial pressure (e-RAP) from IVC congestion. LUICA readings were dichotomised based on lung congestion (≥10 vs <10 B-lines), the presence of lung pathology (consolidation, atelectasis, effusion) and IVC congestion (e-RAP >3mmHg vs 3mmHg). LUICA (8 lung zones + 2 IVC zones) was added to pre-discharge review (4 days post-admission) by HF nurses in 108 hospitalised pts (72±13 years; 58% male) with acute HF, fluid overload, and a variety of lung pathology. Images were assessed for quality (good, usable, measurable or not measurable) and interpreted by a LUICA expert and HF nurses, blinded to admission diagnosis. The predictive value of LUICA readings for patient outcomes (readmission or mortality) was expressed as risk ratio. Results: The quality at the majority of the images was assessed as good or usable (expert: 82/107, nurses: 66/107, p=0.02). Readmission was predicted by both experts and nurses (picture 1). Conclusions: HF nurses can be adequately trained in a 10 Zone LUICA and successfully obtain interpretable images. The predictive power of their interpretation is similar to experts. Funding Acknowledgement: Type of funding sources: Other. Main funding source(s): The University of MelbourneBaker Heart & Diabetes Institute