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.

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.

In this retrospective multicenter observational study, we describe the Israeli experience with veno-venous extracorporeal membrane oxygenation (VV ECMO) for the treatment of COVID-19-induced severe adult respiratory distress syndrome (ARDS), in which ECMO cannulation was done while the patients were awake and spontaneously breathing without endotracheal tube, namely “awake ECMO.” We enrolled all adult patients with severe ARDS due to COVID-19, treated with VV ECMO between March 1, 2020, and November 30, 2021, in which cannulation was done while the patient was awake and spontaneously breathing. During the study period, 365 COVID-19 ARDS patients were treated with VV ECMO. Of these, 25 (6.8%) were treated as awake ECMO. The patient’s mean age was 52 years, and 80% were male. Nine of the 25 patients (36%) remained awake throughout their intensive care unit stay and were not sedated and mechanically ventilated at all. Sixteen (64%) were eventually intubated while being on ECMO. Six months survival was 76%. Median mechanical ventilation-free days on ECMO was 8 (interquartile range 5–12) days. This hypothesis-generating study suggests that treating COVID-19 ARDS patients with VV ECMO without sedation and mechanical ventilation is feasible, yet, additional research will be required in order to determine if this modality offers a survival benefit and to identify who are the patients most likely to benefit from it.

Point-of-care ultrasound is rapidly gaining traction in clinical practice, including primary care. Yet, logistical challenges and geographical isolation hinder skill acquisition. Concurrently, an evidentiary gap exists concerning such guidance's effectiveness and optimal implementation in these settings. We developed a lung point-of-care ultrasound (POCUS) curriculum for primary care physicians in a rural, medically underserved region of the south of Israel. The course included recorded lectures, pre-course assessments, hands-on training, post-workshop lectures, and individual practice. To evaluate our course, we measured learning outcomes and physicians’ proficiency in different lung POCUS domains using hands-on technique assessment and gathered feedback on the course with a multi-modal perception approach: an original written pre- and post-perception and usage questionnaire. Fifty primary care physicians (PCPs) showed significant improvement in hands-on skills, increasing from 6 to 76% proficiency (p < 0.001), and in identifying normal versus abnormal views, improving from 54 to 74% accuracy (p < 0.001). Ten weeks after training, primary care physicians reported greater comfort using lung ultrasound, rising from 10 to 54% (p < 0.001), and improved grasp of its potential and limits, increasing from 27.5% to 84% (p < 0.001). Weekly usage increased from none to 50%, and the number of primary care physicians not using at all decreased from 72 to 26% (p < 0.001). A two-day focused in-person and remote self-learning lung-POCUS training significantly improved primary care physicians' lung ultrasound skills, comfort, and implementation.

Acute kidney injury (AKI) is a prevalent condition in ICU patients. However, inconsistencies in urine charting and guideline interpretations hinder accurate diagnosis and research. This study aimed to derive and validate a standardization for the processing of big urine output datasets to improve consistency in AKI diagnosis and staging. Using a derivation cohort from 14 ICUs at Beth Israel Deaconess Medical Center (2008–2019) and a validation cohort from an academic center in Amsterdam (2003–2016), we developed and validated an algorithm for computing hourly urine output rates and identifying oliguric AKI across its definitions. Peak AKI stages computed using the method were significantly associated with all clinical outcomes, including severity scores, serum creatinine levels, ICU and hospital lengths of stay, renal replacement therapy requirements, and hospital mortality (all p < 0.001). Adjusted 30-day mortality odds ratios for AKI stages 1–3 were 1.58, 2.93, and 5.24 in the derivation cohort and 2.91, 5.16, and 13.59 in the validation cohort (all p < 0.001). Tested on over 85,000 multinational ICU admissions, this approach demonstrated robust performance and consistent results across diverse settings; it has the potential to enhance clinical practice through e-alerts and support future AKI and fluid balance research, including ML model training and inference. Supported by open-source code, the proposed method advances the standardization of AKI diagnostic criteria and can be applied to other EHR-based databases.

Background: Pain and fever are common in early pregnancy, yet their management poses a major clinical dilemma. Although not confirmed, recent studies have raised safety concerns regarding acetaminophen. Evidence on the use of nonsteroidal anti-inflammatory drugs (NSAID) in the first trimester remains inconclusive. This uncertainty has left clinicians with limited evidence to guide treatment decisions. This study evaluated the association between first-trimester NSAID exposure and the risk of major congenital malformations (MCMs) in a large, population-based cohort of pregnancies. Methods and findings: We conducted a population-based retrospective cohort study within the Southern Israeli Pregnancy Registry (siPREG) project, including all singleton pregnancies of women aged 15–45 years resulting in live births, stillbirths, or elective terminations for fetal malformations at a Soroka University Medical Center between 1998 and 2018. Pregnancies exposed to established teratogens, multiple gestations, and those with documented genetic or chromosomal anomalies were excluded. First-trimester NSAID exposure was defined by pharmacy dispensations (overall and by specific agents). MCMs were identified from linked clinical, hospitalization, and termination records through the first postnatal year. Propensity scores were estimated using covariates selected via a directed acyclic graph, including maternal age, ethnicity, diabetes, medical indication for NSAID use, exposure to other antipyretics, obesity, smoking, folic-acid use, gravidity, perinatal care, and year of pregnancy. Generalized full matching was used to balance covariates. Adjusted risk ratios were derived using weighted Poisson regression with G-computation, and two-way cluster-robust standard errors, jointly clustering by maternal identifier and matching subclass. Sensitivity analyses included a dose–response assessment across defined-daily-dose (DDD) categories and a tipping-point analysis evaluating the impact of potential misclassification from unrecorded over-the-counter NSAID use. A total of 264,858 singleton pregnancies were included in the final cohort; 20,202 (7.6%) were exposed to NSAID, most commonly ibuprofen (5.1%), diclofenac (1.6%), and naproxen (1.2%). NSAID exposure, in total and as individual agents, was not associated with MCMs overall (8.2% versus 7.0%; matched-adjusted-Relative Risk (aRR) = 0.99 (95% CI [0.90,1.10])) or with organ-system-specific MCMs, including cardiovascular (matched-aRR = 1.05 (95% CI [0.92,1.20]), musculoskeletal (matched-aRR = 1.03 (95% CI [0.77,1.39])), central nervous system (matched-aRR = 0.77 (95% CI [0.53,1.11])), cleft palate (matched-aRR = 0.95 (95% CI [0.47–1.91])), gastrointestinal (matched-aRR = 1.03 (95% CI [0.64–1.63])), and genitourinary (matched-aRR = 0.99 (95% CI [0.72,1.35])) malformations. Dose–response analyses showed no significant association with MCMs across cumulative NSAID exposure: short-term (1–7 DDD, matched-aRR = 1.06 (95% CI [0.97,1.15]), medium-term (8–21 DDD, matched-aRR = 1.10 (95% CI [0.99,1.22]), and long-term (>21 DDD, matched-aRR = 1.24 (95% CI [0.94,1.63])). The main limitation was the potential for minor exposure misclassification due to over-the-counter availability of ibuprofen, although sensitivity analyses simulating such misclassification suggested minimal impact on the risk estimates. Conclusion: In this large, population-based cohort, we found no evidence supporting an association between first-trimester exposure to NSAID and MCMs, providing reassuring evidence regarding their fetal safety in early pregnancy.