BACKGROUND: The development of heart failure is a turning point in the natural course of aortic stenosis (AS). Pulmonary oedema and elevated left ventricular pressure (LVP) are cardinal features of heart failure. Evaluating pulmonary oedema by lung ultrasound involves taking the upper hand with a bedside noninvasive tool that may reflect LVP.
OBJECTIVE: We sought to assess the correlation between sonographic pulmonary congestion, invasive LV pre-A pressure, and echocardiographic LV end-diastolic pressure (LVEDP) in symptomatic AS patients receiving transcatheter aortic valve replacement.
METHODS: Forty-eight consecutive patients with severe AS and planned transcatheter aortic valve implantation (TAVI) were enrolled. LVEDP was estimated to be normal or elevated using the ASE/EACVI algorithm and transmitral Doppler indices, the E/A ratio, the E/e\', and the left atrial volume index. Invasive LV pre-A pressure was used as a reference, with > 12 mm Hg defined as elevated.
RESULTS: Forty-eight patients (25 women (52%), mean age 75 years, standard deviation (SD) ± 7.7 years) were enrolled in the study. We detected severe B-lines (≥ 30) in 13 (27%) patients and moderate B-lines (15-30) in 33 (68.6%) patients. The number of B-lines increased significantly with the severity of New York Heart Association (NYHA) functional classes (Fig. 1). The B-line count was 14 ± 13 in NYHA class I patients, 20 ± 20 in class II patients, and 44 ± 35 in class III patients (p < 0.05, rho = 0.384). The number of B-lines was correlated with the E/E\' ratio (R = 0.664, p < 0.0001) and the proBNP level (R = 0. 882, p < 0.008). We found no significant correlation with the LVEDP or LVEF. The LVEDP correlated well with the E/E\' ratio (R = 0.491, p < 0.001) but not at all with E/A, DT, or LAVI. All patients had an elevated LVEDP > 12, with a mean pressure of 26 mmHg, a minimum of 13 mmHg, and a maximum of 45 mmHg, with an SD of 7.85.
CONCLUSIONS: Assessing lung ultrasonic B-lines is a straightforward and practical approach to identifying pulmonary oedema in AS patients. The number of B-lines correlated with the E/E\' ratio and the functional status of patients but did not correlate with invasive LVEDP or LVEF. All patients had elevated LVEDP that correlated with E/E\'.

Assessing for volume overload is a key component of both short and long-term management of heart failure patients. Physical examination findings are neither sensitive nor specific for detecting congestion, and subclinical congestion may not be evident at the time of examination. Point of care ultrasound (POCUS) is an efficient and non-invasive way to assess heart failure patients for volume overload. The aim of our narrative review is to summarize how each of the following ultrasound modalities can be used to assess for congestion in the heart failure population: 2D and Doppler echocardiography, lung ultrasound, inferior vena cava ultrasound, internal jugular vein ultrasound, and venous excess grading. While each of these modalities has their limitations, their use in the acute and outpatient space offers the potential to reduce heart failure readmissions and mortality.

Lung involvement represents a fearful complication in rheumatoid arthritis (RA), potentially involving all compartments of the pulmonary system. Regarding interstitial lung disease (ILD), the HRCT represents the gold standard technique for its diagnosis; however, the examination is burdened by radiation exposure and high costs. In addition, although some risk factors for ILD are known, no algorithms exist to know which patients to submit to HRCT and when. In this context, lung ultrasound (LUS) showed promising results for at least 10 years, demonstrating correlation with high resolution computed tomography (HRCT) findings in other rheumatic diseases. Here, LUS may represent a screening test providing additional information to clinical examination and pulmonary function tests. The data deriving from LUS experience in other rheumatic diseases could steer the future towards the use of this technique also in RA patients, and in this review, we report the most relevant literature regarding LUS in RA-ILD.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality.
OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data.
METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips.
RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts\' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance.
CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

Purpose The number of B-lines on lung ultrasound at hospital discharge in patients admitted with acute heart failure (AHF) is associated with poor outcomes. Assessing B-lines can be challenging to execute and replicate, depending on the clinical context. This study aims to determine whether the lung ultrasound score (LUS) at discharge predicts hospital readmission or emergency department (ED) visits in the 30 days after an AHF hospital admission. Methods  We conducted an observational study at the medical ward of the emergency unit of the Clinics Hospital of the Ribeirao Preto Medical School, University of Sao Paulo, a tertiary university hospital in Ribeirao Preto, Sao Paulo, Brazil, where consecutive adults admitted with AHF were included. On the day of hospital discharge, we measured the LUS and tracked these patients for up to 30 days to monitor emergency department visits, hospital readmission, and the number of days free from hospital stay. Results  A total of 46 patients were included in the study. A composite outcome of ED visits or hospital readmission in the 30 days after hospital discharge was achieved for 22 (47.8%) patients. The LUS at hospital discharge had a receiver operating characteristic (ROC) area of 0.93 (95% CI, 0.82-0.99) to predict the composite outcome, against 0.67 (95% CI, 0.52-0.81) for the clinical congestion score (CCS). A LUS ≥ 7 at discharge had a sensitivity of 95.5% and a specificity of 87.5% to predict the composite outcome. The average exam duration was 176±65 (sd) seconds. Conclusions The LUS at hospital discharge following admission for AHF proves to be an accurate tool for predicting the likelihood of return to the ED and/or hospital readmission within 30 days post discharge.

Background: Shiga toxin-producing Escherichia coli-haemolytic uremic syndrome (STEC-HUS) can result in kidney and neurological complications. Early volume-expansion therapy has been shown to improve outcomes, but caution is required to avoid fluid overload. Lung ultrasound scanning (LUS) can be used to detect fluid overload and may be useful in monitoring hydration therapy. Methods: This prospective observational pilot study involved children with STEC-HUS who were recruited from a regional paediatric nephrology centre. B-line quantification by LUS was used to assess fluid status at the emergency department (ED) admission and correlated with the decrease in patient weight from the target weight. A control group of children on chronic dialysis therapy with episodes of symptomatic fluid overload was also enrolled in order to establish a B-line threshold indicative of severe lung congestion. Another cohort of \"healthy\" children, without renal or lung-related diseases, and without clinical signs of fluid overload was also enrolled in order to establish a B-line threshold indicative of euvolemia. Results: LUS assessment was performed in 10 children with STEC-HUS at ED admission, showing an average of three B-lines (range 0-10). LUS was also performed in 53 euvolemic children admitted to the ED not showing kidney and lung disease (healthy controls), showing a median value of two B-lines (range 0-7), not significantly different from children with STEC-HUS at admission (p = 0.92). Children with STEC-HUS with neurological involvement during the acute phase and those requiring dialysis presented a significantly lower number of B-lines at admission compared to patients with a good clinical course (p < 0.001). Patients with long-term renal impairment also presented a lower number of B-lines at disease onset (p = 0.03). Conclusions: LUS is a useful technique for monitoring intravenous hydration therapy in paediatric patients with STEC-HUS. A low number of B-lines at ED admission (<5 B-lines) was associated with worse short-term and long-term outcomes. Further studies are needed to determine the efficacy and safety of an LUS-guided strategy for reducing complications in children with STEC-HUS.

OBJECTIVE: The Killip scale remains a fundamental tool for prognostic assessment in ST-segment elevation myocardial infarction (STEMI) due to its simplicity and predictive value. Lung ultrasound (LUS) has emerged as a valuable adjunct for diagnosing and predicting outcomes in heart failure (HF) and STEMI patients, even those with subclinical congestion. We created a new classification (Killip pLUS), which reclassifies Killip I and II patients into an intermediate category (Killip I pLUS) based on LUS results. This category included Killip I patients and ≥1 positive zone (≥3 B-lines) and Killip II with 0 positive zones. We aimed to evaluate this new classification by comparing it with the Killip scale and a previous LUS-based reclassification scale (LUCK scale).
RESULTS: Lung ultrasound was performed within 24 h of admission in a multicentre cohort of 373 patients admitted for STEMI. In-hospital mortality and major adverse cardiovascular events within one year after admission, comprising mortality or readmission for HF, acute coronary syndrome, or stroke, were analysed. When predicting in-hospital mortality, the global comparison of these three classifications was statistically significant: Killip pLUS area under the curve (AUC) 0.90 (95% CI 0.85-0.95) vs. Killip AUC 0.85 (95% CI 0.73-0.96) vs. LUCK 0.83 (95% CI 0.70-0.95), P = 0.024. To predict events during follow-up, the comparison between scales was also significant: Killip pLUS 0.77 (95% CI 0.71-0.85) vs. Killip 0.72 (95% CI 0.65-0.79) vs. LUCK 0.73 (95% CI 0.66-0.81), P = 0.033.
CONCLUSIONS: The Killip pLUS scale provides enhanced risk stratification compared to the Killip and LUCK scales while preserving simplicity.

Background: Heart failure (HF) affects around 60 million individuals worldwide. The primary aim of this study was to evaluate the efficacy of lung ultrasound (LUS) in managing HF with the goal of reducing hospital readmission rates. Methods: A systematic search was conducted on PubMed, Embase, Google Scholar, Web of Science, and Scopus, covering clinical trials, meta-analyses, systematic reviews, and original articles published between 1 January 2019 and 31 December 2023, focusing on LUS for HF assessment in out-patient settings. There is a potential for bias as the effectiveness of interventions may vary depending on the individuals administering them. Results: The PRISMA method synthesized the findings. Out of 873 articles identified, 33 were selected: 19 articles focused on prognostic assessment of HF, 11 centred on multimodal diagnostic assessments, and two addressed therapeutic guidance for HF diagnosis. LUS demonstrates advantages in detecting subclinical congestion, which holds prognostic significance for readmission and mortality during out-patient follow-up post-hospital-discharge, especially in complex scenarios, but there is a lack of standardization. Conclusions: there are considerable uncertainties in their interpretation and monitoring changes. The need for an updated international consensus on the use of LUS seems obvious.

OBJECTIVE: To explore the diagnostic value of lung ultrasound (LUS) and pleural shear wave elastography (SWE) for connective tissue disease-interstitial lung disease (CTD-ILD).
METHODS: We selected 104 patients diagnosed with connective tissue disease (CTD) at our hospital. All patients underwent LUS, SWE, and high-resolution computed tomography (HRCT). With HRCT as the imaging gold standard for diagnosis, patients were categorized into CTD-ILD and CTD-non-ILD groups. We employed paired chi-square tests to compare the diagnostic differences between HRCT and LUS for ILD. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of pleural SWE for ILD. Correlation analysis was performed between pleural elasticity values and lung ultrasound scores.
RESULTS: The sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of LUS for diagnosing CTD-ILD were 93.3%, 86.2%, 6.761, and 0.078, respectively. There was no statistically significant difference in the results between HRCT and LUS (P = 1.000), with a kappa value of 0.720 (P < 0.001). There was a statistically significant difference in the pleural elasticity in the bilateral lower back region between the case and control groups (P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) for pleural SWE in diagnosing CTD-ILD was 0.685. In CTD-ILD patients, there was no significant correlation between pleural elasticity values and LUS scores (P > 0.05).
CONCLUSIONS: The LUS can serve as an important imaging method for screening for CTD-ILD and assessing the severity of the disease. However, pleural SWE has been shown to demonstrate lower diagnostic efficacy for CTD-ILD, and its ability to assess disease severity is limited.

BACKGROUND: Lung ultrasound (LUS) is often used to assess congestion in heart failure (HF). In this study, we assessed the prognostic role of LUS in HF patients at admission and hospital discharge, and in an out-patient setting and explored whether clinical factors (age, sex, left ventricular ejection fraction (LVEF) and atrial fibrillation) impact the prognostic value of LUS findings. Further, we assessed the incremental prognostic value of LUS on top of AHEAD and MAGGIC clinical risk scores.
RESULTS: We pooled data of patients hospitalized for HF or followed-up in out-patient clinics from international cohorts. We enrolled 1,947 patients, at admission (n=578), discharge (n=389) and in out-patient clinic (n=980). Total LUS B-line count was calculated for the 8-zone scanning protocol. The primary outcome was a composite of re-hospitalization for HF and all-cause death. Compared to those in the lower tertiles of B-lines, patients in the highest tertile were older, more likely to have signs of HF and higher NT-proBNP levels. A higher number of B-lines was associated with increased risk of primary outcome at discharge (Tertile3 vs Tertile1: adjustedHR= 5.74 (3.26- 10.12), p<0.0001) and in out-patients (Tertile3 vs Tertile1: adjustedHR= 2.66 (1.08- 6.54), p=0.033). Age and LVEF did not influence the prognostic capacity of LUS in different clinical settings. Adding B-line count to MAGGIC and AHEAD scores improved net reclassification significantly in all three clinical settings.
CONCLUSIONS: A higher number of B-lines in patients with HF was associated with increased risk of morbidity and mortality, regardless of the clinical setting.