Browsing by Subject "Lung ultrasound"
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Item B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review(Springer, 2021-06-30) Russell, Frances M.; Ehrman, Robert R.; Barton, Allen; Sarmiento, Elisa; Ottenhoff, Jakob E.; Nti, Benjamin K.; Emergency Medicine, School of MedicineBackground: The goal of this study was to assess the ability of machine artificial intelligence (AI) to quantitatively assess lung ultrasound (LUS) B-line presence using images obtained by learners novice to LUS in patients with acute heart failure (AHF), compared to expert interpretation. Methods: This was a prospective, multicenter observational study conducted at two urban academic institutions. Learners novice to LUS completed a 30-min training session on lung image acquisition which included lecture and hands-on patient scanning. Learners independently acquired images on patients with suspected AHF. Automatic B-line quantification was obtained offline after completion of the study. Machine AI counted the maximum number of B-lines visualized during a clip. The criterion standard for B-line counts was semi-quantitative analysis by a blinded point-of-care LUS expert reviewer. Image quality was blindly determined by an expert reviewer. A second expert reviewer blindly determined B-line counts and image quality. Intraclass correlation was used to determine agreement between machine AI and expert, and expert to expert. Results: Fifty-one novice learners completed 87 scans on 29 patients. We analyzed data from 611 lung zones. The overall intraclass correlation for agreement between novice learner images post-processed with AI technology and expert review was 0.56 (confidence interval [CI] 0.51-0.62), and 0.82 (CI 0.73-0.91) between experts. Median image quality was 4 (on a 5-point scale), and correlation between experts for quality assessment was 0.65 (CI 0.48-0.82). Conclusion: After a short training session, novice learners were able to obtain high-quality images. When the AI deep learning algorithm was applied to those images, it quantified B-lines with moderate-to-fair correlation as compared to semi-quantitative analysis by expert review. This data shows promise, but further development is needed before widespread clinical use.Item Bedside lung ultrasound for the diagnosis of pneumonia in children presenting to an emergency department in a resource-limited setting(BMC, 2023-01-09) Amatya, Yogendra; Russell, Frances M.; Rijal, Suraj; Adhikari, Sunil; Nti, Benjamin; House, Darlene R.; Emergency Medicine, School of MedicineBackground: Lung ultrasound (LUS) is an effective tool for diagnosing pneumonia; however, this has not been well studied in resource-limited settings where pneumonia is the leading cause of death in children under 5 years of age. Objective: The objective of this study was to evaluate the diagnostic accuracy of bedside LUS for diagnosis of pneumonia in children presenting to an emergency department (ED) in a resource-limited setting. Methods: This was a prospective cross-sectional study of children presenting to an ED with respiratory complaints conducted in Nepal. We included all children under 5 years of age with cough, fever, or difficulty breathing who received a chest radiograph. A bedside LUS was performed and interpreted by the treating clinician on all children prior to chest radiograph. The criterion standard was radiographic pneumonia, diagnosed by a panel of radiologists using the Chest Radiography in Epidemiological Studies methodology. The primary outcome was sensitivity and specificity of LUS for the diagnosis of pneumonia. All LUS images were later reviewed and interpreted by a blinded expert sonographer. Results: Three hundred and sixty-six children were enrolled in the study. The median age was 16.5 months (IQR 22) and 57.3% were male. Eighty-four patients (23%) were diagnosed with pneumonia by chest X-ray. Sensitivity, specificity, positive and negative likelihood ratios for clinician's LUS interpretation was 89.3% (95% CI 81-95), 86.1% (95%CI 82-90), 6.4, and 0.12 respectively. LUS demonstrated good diagnostic accuracy for pneumonia with an area under the curve of 0.88 (95% CI 0.83-0.92). Interrater agreement between clinician and expert ultrasound interpretation was excellent (k = 0.85). Conclusion: Bedside LUS when used by ED clinicians had good accuracy for diagnosis of pneumonia in children in a resource-limited setting.Item Clinical Prediction Models for Pneumonia in Children Presenting to an Emergency Department in a Resource-Limited Setting Using Lung Ultrasound Diagnosis as the Gold Standard(Springer Nature, 2025-03-28) House, Darlene R.; Amatya, Yogendra; Nti, Benjamin K.; Russell, Frances M.; Emergency Medicine, School of MedicineIntroduction: Clinical prediction rules for pediatric pneumonia often rely on radiographic pneumonia for diagnosis; however, lung ultrasound has higher diagnostic accuracy. Our objective was to derive a clinical prediction model for pneumonia in children under five using lung ultrasound as the criterion standard. Methods: This was a prospective study of children under five presenting to an emergency department (ED) with respiratory complaints in a resource-limited setting. Clinical findings, chest X-ray, and lung ultrasound results were recorded for each patient. Classification tree models were used to predict pneumonia using lung ultrasound as the criterion standard. Separate models were used without and with inclusion of chest X-ray results. Results: Of 386 patients enrolled, 125 patients (32.4%) had pneumonia on lung ultrasound. The mean age was 20.8 (SD 15.5) months. Using recursive feature selection, three variables provided the best prediction for pneumonia, namely, crepitations, retractions, and difficulty breathing, demonstrating a sensitivity of 74.2% and specificity of 38.5%. The algorithm including chest X-ray provided a sensitivity of 51.6% and specificity of 87.7%. Conclusions: Using lung ultrasound as the gold standard, no single clinical finding or combination of clinical findings provided enough accuracy to reliably diagnose pneumonia in children under five years.Item Impact of bedside lung ultrasound on physician clinical decision-making in an emergency department in Nepal(BMC, 2020) House, Darlene R.; Amatya, Yogendra; Nti, Benjamin; Russell, Frances M.; Emergency Medicine, School of MedicineBackground Lung ultrasound is an effective tool for the evaluation of undifferentiated dyspnea in the emergency department. Impact of lung ultrasound on clinical decisions for the evaluation of patients with dyspnea in resource-limited settings is not well-known. The objective of this study was to evaluate the impact of lung ultrasound on clinical decision-making for patients presenting with dyspnea to an emergency department in the resource-limited setting of Nepal. Methods A prospective, cross-sectional study of clinicians working in the Patan Hospital Emergency Department was performed. Clinicians performed lung ultrasounds on patients presenting with dyspnea and submitted ultrasounds with their pre-test diagnosis, lung ultrasound interpretation, post-test diagnosis, and any change in management. Results Twenty-two clinicians participated in the study, completing 280 lung ultrasounds. Diagnosis changed in 124 (44.3%) of patients with dyspnea. Clinicians reported a change in management based on the lung ultrasound in 150 cases (53.6%). Of the changes in management, the majority involved treatment (83.3%) followed by disposition (13.3%) and new consults (2.7%). Conclusions In an emergency department in Nepal, bedside lung ultrasound had a significant impact on physician clinical decision-making, especially on patient diagnosis and treatment.Item Lung ultrasound for the early diagnosis of COVID-19 pneumonia: an international multicenter study(Springer Nature, 2021) Volpicelli, Giovanni; Gargani, Luna; Perlini, Stefano; Spinelli, Stefano; Barbieri, Greta; Lanotte, Antonella; Casasola, Gonzalo García; Nogué-Bou, Ramon; Lamorte, Alessandro; Agricola, Eustachio; Villén, Tomas; Deol, Paramjeet Singh; Nazerian, Peiman; Corradi, Francesco; Stefanone, Valerio; Fraga, Denise Nicole; Navalesi, Paolo; Ferre, Robinson; Boero, Enrico; Martinelli, Giampaolo; Cristoni, Lorenzo; Perani, Cristiano; Vetrugno, Luigi; McDermott, Cian; Miralles-Aguiar, Francisco; Secco, Gianmarco; Zattera, Caterina; Salinaro, Francesco; Grignaschi, Alice; Boccatonda, Andrea; Giostra, Fabrizio; Infante, Marta Nogué; Covella, Michele; Ingallina, Giacomo; Burkert, Julia; Frumento, Paolo; Forfori, Francesco; Ghiadoni, Lorenzo; International Multicenter Study Group on LUS in COVID-19; Emergency Medicine, School of MedicinePurpose: To analyze the application of a lung ultrasound (LUS)-based diagnostic approach to patients suspected of COVID-19, combining the LUS likelihood of COVID-19 pneumonia with patient's symptoms and clinical history. Methods: This is an international multicenter observational study in 20 US and European hospitals. Patients suspected of COVID-19 were tested with reverse transcription-polymerase chain reaction (RT-PCR) swab test and had an LUS examination. We identified three clinical phenotypes based on pre-existing chronic diseases (mixed phenotype), and on the presence (severe phenotype) or absence (mild phenotype) of signs and/or symptoms of respiratory failure at presentation. We defined the LUS likelihood of COVID-19 pneumonia according to four different patterns: high (HighLUS), intermediate (IntLUS), alternative (AltLUS), and low (LowLUS) probability. The combination of patterns and phenotypes with RT-PCR results was described and analyzed. Results: We studied 1462 patients, classified in mild (n = 400), severe (n = 727), and mixed (n = 335) phenotypes. HighLUS and IntLUS showed an overall sensitivity of 90.2% (95% CI 88.23-91.97%) in identifying patients with positive RT-PCR, with higher values in the mixed (94.7%) and severe phenotype (97.1%), and even higher in those patients with objective respiratory failure (99.3%). The HighLUS showed a specificity of 88.8% (CI 85.55-91.65%) that was higher in the mild phenotype (94.4%; CI 90.0-97.0%). At multivariate analysis, the HighLUS was a strong independent predictor of RT-PCR positivity (odds ratio 4.2, confidence interval 2.6-6.7, p < 0.0001). Conclusion: Combining LUS patterns of probability with clinical phenotypes at presentation can rapidly identify those patients with or without COVID-19 pneumonia at bedside. This approach could support and expedite patients' management during a pandemic surge.Item Lung Ultrasound Guided Emergency Department Management of Acute Heart Failure (BLUSHED-AHF): A Randomized, Controlled Pilot Trial(Elsevier, 2021) Pang, Peter S.; Russell, Frances M.; Ehrman, Robert; Ferre, Rob; Gargani, Luna; Levy, Phillip D.; Noble, Vicki; Lane, Kathleen A.; Li, Xiaochun; Collins, Sean P.; Emergency Medicine, School of MedicineObjectives: The goal of this study was to determine whether a 6-hour lung ultrasound (LUS)-guided strategy-of-care improves pulmonary congestion over usual management in the emergency department (ED) setting. A secondary goal was to explore whether early targeted intervention leads to improved outcomes. Background: Targeting pulmonary congestion in acute heart failure remains a key goal of care. LUS B-lines are a semi-quantitative assessment of pulmonary congestion. Whether B-lines decrease in patients with acute heart failure by targeting therapy is not well known. Methods: A multicenter, single-blind, ED-based, pilot trial randomized 130 patients to receive a 6-hour LUS-guided treatment strategy versus structured usual care. Patients were followed up throughout hospitalization and 90 days' postdischarge. B-lines ≤15 at 6 h was the primary outcome, and days alive and out of hospital (DAOOH) at 30 days was the main exploratory outcome. Results: No significant difference in the proportion of patients with B-lines ≤15 at 6 hours (25.0% LUS vs 27.5% usual care; P = 0.83) or the number of B-lines at 6 hours (35.4 ± 26.8 LUS vs 34.3 ± 26.2 usual care; P = 0.82) was observed between groups. There were also no differences in DAOOH (21.3 ± 6.6 LUS vs 21.3 ± 7.1 usual care; P = 0.99). However, a significantly greater reduction in the number of B-lines was observed in LUS-guided patients compared with those receiving usual structured care during the first 48 hours (P = 0.04). Conclusions: In this pilot trial, ED use of LUS to target pulmonary congestion conferred no benefit compared with usual care in reducing the number of B-lines at 6 hours or in 30 days DAOOH. However, LUS-guided patients had faster resolution of congestion during the initial 48 hours.Item Lung ultrasound training and evaluation for proficiency among physicians in a low-resource setting(Springer, 2021-06-30) House, Darlene R.; Amatya, Yogendra; Nti, Benjamin; Russell, Frances M.; Emergency Medicine, School of MedicineBackground: Lung ultrasound (LUS) is helpful for the evaluation of patients with dyspnea in the emergency department (ED). However, it remains unclear how much training and how many LUS examinations are needed for ED physicians to obtain proficiency. The objective of this study was to determine the threshold number of LUS physicians need to perform to achieve proficiency for interpreting LUS on ED patients with dyspnea. Methods: A prospective study was performed at Patan Hospital in Nepal, evaluating proficiency of physicians novice to LUS. After eight hours of didactics and hands-on training, physicians independently performed and interpreted ultrasounds on patients presenting to the ED with dyspnea. An expert sonographer blinded to patient data and LUS interpretation reviewed images and provided an expert interpretation. Interobserver agreement was performed between the study physician and expert physician interpretation. Cumulative sum analysis was used to determine the number of scans required to attain an acceptable level of training. Results: Nineteen physicians were included in the study, submitting 330 LUS examinations with 3288 lung zones. Eighteen physicians (95%) reached proficiency. Physicians reached proficiency for interpreting LUS accurately when compared to an expert after 4.4 (SD 2.2) LUS studies for individual zone interpretation and 4.8 (SD 2.3) studies for overall interpretation, respectively. Conclusions: Following 1 day of training, the majority of physicians novice to LUS achieved proficiency with interpretation of lung ultrasound after less than five ultrasound examinations performed independently.Item Prognostic value of lung ultrasound in patients hospitalized for heart disease irrespective of symptoms and ejection fraction(Wiley, 2021) Gargani, Luna; Pugliese, Nicola Riccardo; Frassi, Francesca; Frumento, Paolo; Poggianti, Elisa; Mazzola, Matteo; De Biase, Nicolò; Landi, Patrizia; Masi, Stefano; Taddei, Stefano; Pang, Peter S.; Sicari, Rosa; Emergency Medicine, School of MedicineAims: Lung ultrasound B-lines are the sonographic sign of pulmonary congestion and can be used in the differential diagnosis of dyspnoea to rule in or rule out acute heart failure (AHF). Our aim was to assess the prognostic value of B-lines, integrated with echocardiography, in patients admitted to a cardiology department, independently of the initial clinical presentation, thus in patients with and without AHF, and in AHF with reduced and preserved ejection fraction (HFrEF and HFpEF). Methods and results: We enrolled consecutive patients admitted for various cardiac conditions. Patients were classified into three groups: (i) acute HFrEF; (ii) acute HFpEF; and (iii) non-AHF. All patients underwent an echocardiogram coupled with lung ultrasound at admission, according to standardized protocols. We followed up 1021 consecutive inpatients (69 ± 12 years) for a median of 14.4 months (interquartile range 4.6-24.3) for death and rehospitalization for AHF. During the follow-up, 126 events occurred. Admission B-lines > 30, ejection fraction < 50%, tricuspid regurgitation velocity > 2.8 m/s, and tricuspid annular plane systolic excursion < 17 mm were independent predictors at multivariable analysis. B-lines > 30 had a strong predictive value in HFpEF and non-AHF, but not in HFrEF. Conclusions: Ultrasound B-lines can detect subclinical pulmonary interstitial oedema in patients thought to be free of congestion and provide useful information not only for the diagnosis but also for the prognosis in different cardiac conditions. Their added prognostic value among standard echocardiographic parameters is more robust in patients with HFpEF compared with HFrEF.Item The Prognostic Value of Improving Congestion on Lung Ultrasound During Treatment for Acute Heart Failure Differs Based on Patient Characteristics at Admission(Elsevier, 2024) Harrison, Nicholas E.; Ehrman, Robert; Collins, Sean; Desai, Ankit A.; Duggan, Nicole M.; Ferre, Rob; Gargani, Luna; Goldsmith, Andrew; Kapur, Tina; Lane, Katie; Levy, Phillip; Li, Xiaochun; Noble, Vicki E.; Russell, Frances M.; Pang, Peter; Emergency Medicine, School of MedicineBackground: Lung ultrasound congestion scoring (LUS-CS) is a congestion severity biomarker. The BLUSHED-AHF trial demonstrated feasibility for LUS-CS-guided therapy in acute heart failure (AHF). We investigated two questions: 1) does change (∆) in LUS-CS from emergency department (ED) to hospital-discharge predict patient outcomes, and 2) is the relationship between in-hospital decongestion and adverse events moderated by baseline risk-factors at admission? Methods: We performed a secondary analysis of 933 observations/128 patients from 5 hospitals in the BLUSHED-AHF trial receiving daily LUS. ∆LUS-CS from ED arrival to inpatient discharge (scale -160 to +160, where negative = improving congestion) was compared to a primary outcome of 30-day death/AHF-rehospitalization. Cox regression was used to adjust for mortality risk at admission [Get-With-The-Guidelines HF risk score (GWTG-RS)] and the discharge LUS-CS. An interaction between ∆LUS-CS and GWTG-RS was included, under the hypothesis that the association between decongestion intensity (by ∆LUS-CS) and adverse outcomes would be stronger in admitted patients with low-mortality risk but high baseline congestion. Results: Median age was 65 years, GWTG-RS 36, left ventricular ejection fraction 36 %, and ∆LUS-CS -20. In the multivariable analysis ∆LUS-CS was associated with event-free survival (HR = 0.61; 95 % CI: 0.38-0.97), while discharge LUS-CS (HR = 1.00; 95%CI: 0.54-1.84) did not add incremental prognostic value to ∆LUS-CS alone. As GWTG-RS rose, benefits of LUS-CS reduction attenuated (interaction p < 0.05). ∆LUS-CS and event-free survival were most strongly correlated in patients without tachycardia, tachypnea, hypotension, hyponatremia, uremia, advanced age, or history of myocardial infarction at ED/baseline, and those with low daily loop diuretic requirements. Conclusions: Reduction in ∆LUS-CS during AHF treatment was most associated with improved readmission-free survival in heavily congested patients with otherwise reassuring features at admission. ∆LUS-CS may be most useful as a measure to ensure adequate decongestion prior to discharge, to prevent early readmission, rather than modify survival.Item What are the minimum requirements to establish proficiency in lung ultrasound training for quantifying B-lines?(Wiley, 2020-07-22) Russell, Frances M.; Ferre, Robinson; Ehrman, Robert R.; Noble, Vicki; Gargani, Luna; Collins, Sean P.; Levy, Phillip D.; Fabre, Katarina L.; Eckert, George J.; Pang, Peter S.; Emergency Medicine, School of MedicineAims The goal of this study was to determine the number of scans needed for novice learners to attain proficiency in B‐line quantification compared with expert interpretation. Methods and results This was a prospective, multicentre observational study of novice learners, physicians and non‐physicians from three academic institutions. Learners received a 2 h lung ultrasound (LUS) training session on B‐line assessment, including lecture, video review to practice counting and hands‐on patient scanning. Learners quantified B‐lines using an eight‐zone scanning protocol in patients with suspected acute heart failure. Ultrasound (US) machine settings were standardized to a depth of 18 cm and clip length of 6 s, and tissue harmonics and multibeam former were deactivated. For quantification, the intercostal space with the greatest number of B‐lines within each zone was used for scoring. Each zone was given a score of 0–20 based on the maximum number of B‐lines counted during one respiratory cycle. The B‐line score was determined by multiplying the percentage of the intercostal space filled with B‐lines by 20. We compared learner B‐line counts with a blinded expert reviewer (five US fellowship‐trained faculty with > 5 years of clinical experience) for each lung zone scanned; proficiency was defined as an intraclass correlation of > 0.7. Learning curves for each learner were constructed using cumulative sum method for statistical analysis. The Wilcoxon rank‐sum test was used to compare the number of scans required to reach proficiency between different learner types. Twenty‐nine learners (21 research associates, 5 residents and 3 non‐US‐trained emergency medicine faculty) scanned 2629 lung zones with acute pulmonary oedema. After a mean of 10.8 (standard deviation 14.0) LUS zones scanned, learners reached the predefined proficiency standard. The number of scanned zones required to reach proficiency was not significantly different between physicians and non‐physicians (P = 0.26), learners with no prior US experience vs. > 25 prior patient scans (P = 0.64) and no prior vs. some prior LUS experience (P = 0.59). The overall intraclass correlation for agreement between learners and experts was 0.74 and 0.80 between experts. Conclusions Our results show that after a short, structured training, novice learners are able to achieve proficiency for quantifying B‐lines on LUS after scanning 11 zones. These findings support the use of LUS for B‐line quantification by non‐physicians in clinical and research applications.