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Background: ARDS is a heterogeneous syndrome with distinct clinical phenotypes. Here we investigate whether the presence or absence of large pulmonary ultrasonographic consolidations can categorize COVID-19 ARDS patients requiring mechanical ventilation into distinct clinical phenotypes. Methods: This is a retrospective study performed in a tertiary-level intensive care unit in Israel between April and September 2020. Data collected included lung ultrasound (LUS) findings, respiratory parameters, and treatment interventions. The primary outcome was a composite of three ARDS interventions: prone positioning, high PEEP, or a high dose of inhaled nitric oxide. Results: A total of 128 LUS scans were conducted among 23 patients. The mean age was 65 and about two-thirds were males. 81 scans identified large consolidation and were classified as "C-type", and 47 scans showed multiple B-lines with no or small consolidation and were classified as "B-type". The presence of a "C-type" study had 2.5 times increased chance of receiving the composite primary outcome of advanced ARDS interventions despite similar SOFA scores, Pao2/FiO2 ratio, and markers of disease severity (OR = 2.49, %95CI 1.40–4.44). Conclusion: The presence of a "C-type" profile with LUS consolidation potentially represents a distinct COVID-19 ARDS subphenotype that is more likely to require aggressive ARDS interventions. Further studies are required to validate this phenotype in a larger cohort and determine causality, diagnostic, and treatment responses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]