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.
ABSTRACT
BACKGROUND: Blood stream infections are a significant cause of morbidity and mortality in burns, and pathogen identification is important for treatment. This study aims to characterize the microbiology of these infections and the association between the infecting pathogen and the hospitalization course. METHODS: We conducted a cohort study that included records of burn patients treated at the Soroka University Medical Center between 2007-2020. Statistical analysis of demographic and clinical data was performed to explore relationships between burn characteristics and outcomes. Patients with positive blood cultures were divided into four groups: Gram-positive, Gram-negative, mixed-bacterial, and fungal. RESULTS: Of the 2029 burn patients hospitalized, 11.7% had positive blood cultures. The most common pathogens were Candida and Pseudomonas. We found significant differences in ICU admission, need for surgery, and mortality between the infected and non-infected groups (p < 0.001). Pathogen groups differed significantly mean TBSA, ICU admission, need for surgery, and mortality (p < 0.001). Multivariate analysis showed flame (OR 2.84) and electric burns (OR 4.58) were independent risk factors for ICU admission and surgical intervention (p < 0.001). Gram-negative bacterial infection was found to be an independent predictor of mortality (OR = 9.29, p < 0.001). CONCLUSIONS: Anticipating specific pathogens which are associated with certain burn characteristics may help guide future therapy.
