Can Dialysis Patients Identify and Diagnose Pulmonary Congestion Using Self-Lung Ultrasound?

Abstract

Background: With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools. Methods: This prospective pilot study was conducted between November 2020 and September 2021. Nineteen chronic HD patients were enrolled in the Soroka University Medical Center (SUMC) Dialysis Clinic. First, we examined the patient’s ability to obtain a self-lung US. Then, we used interrater reliability (IRR) to compare the self-detection results reported by the patients to the observation of POCUS experts and an ultrasound (US) machine with an AI-based automatic B-line counting tool. All the videos were reviewed by a specialist blinded to the performer. We examined their agreement degree using the weighted Cohen’s kappa (Kw) index. Results: A total of 19 patients were included in our analysis. We found moderate to substantial agreement between the POCUS expert review and the automatic counting both when the patient performed the LUS (Kw = 0.49 [95% CI: 0.05–0.93]) and when the researcher performed it (Kw = 0.67 [95% CI: 0.67–0.67]). Patients were able to place the probe in the correct position and present a lung image well even weeks from the teaching session, but did not show good abilities in correctly saving or counting B-lines compared to an expert or an automatic counting tool. Conclusions: Our results suggest that LUS self-monitoring for pulmonary congestion can be a reliable option if the patient’s count is combined with an AI application for the B-line count. This study provides insight into the possibility of utilizing home US devices to detect pulmonary congestion, enabling patients to have a more active role in their health care.

The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students

“…We believe the implications of our findings are twofold: first, in training for quality control, establishing professional standards for student training; and second, in the clinical field, as previous research shows that POCUS integrated automated AI tool integration can support and validate clinical decision-making, particularly among less experienced users [ 19 ]. Regarding the cardiac ultrasound applications explored in this study, we advocate for the integration of these tools into new POCUS systems and in student POCUS training programs.…”

Análise dos internamentos hospitalares de pacientes dialíticos durante a pandemia de COVID-19

“…Uma vez que congestão pulmonar por hipervolemia é a principal complicação de pacientes com DRC em estágio terminal 21 , não é inesperado que este seja o principal diagnóstico diferencial na admissão dos pacientes cujo diagnóstico de COVID-19 foi descartado. Pacientes dialíticos com congestão pulmonar, além de manifestar sintomas respiratórios, estão mais propensos a necessitar hospitalização e apresentar mais eventos cardiovasculares e maior mortalidade.…”

“…Pacientes dialíticos com congestão pulmonar, além de manifestar sintomas respiratórios, estão mais propensos a necessitar hospitalização e apresentar mais eventos cardiovasculares e maior mortalidade. 21 A infecção de corrente sanguínea em pacientes hemodialíticos é outra grande complicação deste grupo com alta morbimortalidade e necessidade de hospitalização, e pode manifestar sintomas semelhantes à infecção por COVID-19, como febre, astenia e hipotensão. [22][23][24][25] Devido à alta prevalência de sintomas gastrointestinais como manifestação inicial da infecção por COVID-19 em pacientes dialíticos 8,16,20 , o diagnóstico diferencial com peritonite bacteriana relacionada à diálise também pode ser considerado.…”

Exploring the Feasibility of At‐Home Lung Ultra‐Portable Ultrasound: Parent‐Performed Pediatric Lung Imaging

“…However, there is a paucity of studies that have evaluated or discussed the use of remotely guiding patients who are using an ultra-portable ultrasound at home to obtain images of themselves or a partner. [9][10][11][12][13][14] Children in particular offer a unique population as they have a built-in caregiver that may facilitate the use of ultraportable ultrasound more easily. Respiratory illnesses in children are prime candidates to study using ultra-portable ultrasound as it can be used for serial assessments and to monitor progression of illness in select populations.…”