Performance of Lung Ultrasound for Monitoring Interstitial Lung Disease

Development of an Automated Ultrasound Signal Indicator of Lung Interstitial Syndrome

OBJECTIVES

The number and distribution of lung ultrasound (LUS) imaging artifacts termed B-lines correlate with the presence of acute lung disease such as infection, acute respiratory distress syndrome (ARDS), and pulmonary edema. Detection and interpretation of B-lines require dedicated training and is machine and operator-dependent. The goal of this study was to identify radio frequency (RF) signal features associated with B-lines in a cohort of patients with cardiogenic pulmonary edema. A quantitative signal indicator could then be used in a single-element, non-imaging, wearable, automated lung ultrasound sensor (LUSS) for continuous hands-free monitoring of lung fluid.

METHODS

In this prospective study a 10-zone LUS exam was performed in 16 participants, including 12 patients admitted with acute cardiogenic pulmonary edema (mean age 60 ± 12 years) and 4 healthy controls (mean age 44 ± 21). Overall,160 individual LUS video clips were recorded. The LUS exams were performed with a phased array probe driven by an open-platform ultrasound system with simultaneous RF signal collection. RF data were analyzed offline for candidate B-line indicators based on signal amplitude, temporal variability, and frequency spectrum; blinded independent review of LUS images for the presence or absence of B-lines served as ground truth. Predictive performance of the signal indicators was determined with receiving operator characteristic (ROC) analysis with k-fold cross-validation.

RESULTS

Two RF signal features-temporal variability of signal amplitude at large depths and at the pleural line-were strongly associated with B-line presence. The sensitivity and specificity of a combinatorial indicator were 93.2 and 58.5%, respectively, with cross-validated area under the ROC curve (AUC) of 0.91 (95% CI = 0.80-0.94).

CONCLUSION

A combinatorial signal indicator for use with single-element non-imaging LUSS was developed to facilitate continuous monitoring of lung fluid in patients with respiratory illness.

Lung Ultrasound and Pleural Artifacts: A Pictorial Review

Lung ultrasound is a well-established diagnostic approach used in detecting pathological changes near the pleura of the lung. At the acoustic boundary of the lung surface, it is necessary to differentiate between the primary visualization of pleural parenchymal pathologies and the appearance of secondary artifacts when sound waves enter the lung or are reflected at the visceral pleura. The aims of this pictorial essay are to demonstrate the sonographic patterns of various pleural interface artifacts and to illustrate the limitations and pitfalls of the use of ultrasound findings in diagnosing any underlying pathology.

Predicting In-Hospital Acute Heart Failure Worsening in the Oldest Old: Insights from Point-of-Care Ultrasound

The decompensation trajectory check is a basic step to assess the clinical course and to plan future therapy in hospitalized patients with acute decompensated heart failure (ADHF). Due to the atypical presentation and clinical complexity, trajectory checks can be challenging in older patients with acute HF. Point-of-care ultrasound (POCUS) has proved to be helpful in the clinical decision-making of patients with dyspnea; however, to date, no study has attempted to verify its role in predicting determinants of ADHF in-hospital worsening. In this single-center, cross-sectional study, we consecutively enrolled patients aged 75 or older hospitalized with ADHF in a tertiary care hospital. All of the patients underwent a complete clinical examination, blood tests, and POCUS, including Lung Ultrasound and Focused Cardiac Ultrasound. Out of 184 patients hospitalized with ADHF, 60 experienced ADHF in-hospital worsening. By multivariable logistic analysis, total Pleural Effusion Score (PEFs) [aO.R.: 1.15 (CI95% 1.02-1.33), p = 0.043] and IVC collapsibility [aO.R.: 0.90 (CI95% 0.83-0.95), p = 0.039] emerged as independent predictors of acute HF worsening after extensive adjustment for potential confounders. In conclusion, POCUS holds promise for enhancing risk assessment, tailoring diuretic treatment, and optimizing discharge timing for older patients with ADHF.

Where Are We? The Past, Present and Future of Thoracic Ultrasound

The technique of thoracic ultrasound is living through a progressive rise in clinical routine [...].

Novel diagnostic techniques in interstitial lung disease

Research into novel diagnostic techniques and targeted therapeutics in interstitial lung disease (ILD) is moving the field toward increased precision and improved patient outcomes. An array of molecular techniques, machine learning approaches and other innovative methods including electronic nose technology and endobronchial optical coherence tomography are promising tools with potential to increase diagnostic accuracy. This review provides a comprehensive overview of the current evidence regarding evolving diagnostic methods in ILD and to consider their future role in routine clinical care.

Imaging in children with ataxia-telangiectasia-The radiologist's approach

Ataxia-telangiectasia (A-T) is a syndromic inborn error of immunity (IEI) characterized by genomic instability, defective reparation of the DNA double-strand breaks, and hypersensitivity to ionizing radiation disturbing cellular homeostasis. The role of imaging diagnostics and the conscious choice of safe and advantageous imaging technique, as well as its correct interpretation, are crucial in the diagnostic process and monitoring of children with A-T. This study aimed at defining the role of a radiologist in the early diagnosis of A-T, as well as in detecting and tracking disease complications associated with infections, inflammation, lymphoproliferation, organ-specific immunopathology, and malignancy. Based on our single-center experience, retrospective analysis of investigations using ionizing radiation-free techniques, ultrasound (US), and Magnetic Resonance Imaging (MRI), was performed on regularly followed-up 11 pediatric A-T patients, 6 girls and 5 boys, aged from 2 to 18 years, with the longest period of observation coming to over 13 years. Our attention was especially drawn to the abnormalities that were observed in the US and MRI examinations of the lungs, abdominal cavity, and lymph nodes. The abdominal US showed no abnormalities in organ dimensions or echostructure in 4 out of 11 children studied, yet in the other 7, during follow-up examinations, hepato- and/or splenomegaly, mesenteric, visceral, and paraaortic lymphadenopathy were observable. In 2 patients, focal changes in the liver and spleen were shown, and in one patient progressive abdominal lymphadenopathy corresponded with the diagnosis of non-Hodgkin lymphoma (NHL). The lung US revealed multiple subpleural consolidations and B line artifacts related to the interstitial-alveolar syndrome in 5 patients, accompanied by pleural effusion in one of them. The MRI investigation of the lung enabled the detection of lymphatic nodal masses in the mediastinum, with concomitant airway lesions characteristic of bronchiectasis and focal parenchymal consolidations in one A-T patient with chronic respiratory failure. This patient also manifested organomegaly and granulomatous liver disease in abdominal MRI examination. Our study shows that the use of modern US capabilities and MRI is safe and efficient, thereby serving as a recommended advantageous imaging diagnostic tool in monitoring children with IEI and DNA instability syndromes.

A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes. Method: Here, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data. Results: In terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed. Conclusions: Despite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.