The role of imaging in the assessment of severe asthma

The role of radiological imaging in the management of severe and difficult-to-treat asthma

Radiological imaging has proven to be a useful tool in the assessment of asthma, its comorbidities and potential complications. Characteristic chest radiograph and computed tomography scan findings can be seen in asthma and in other conditions that can coexist with or be misdiagnosed as asthma, including chronic rhinosinusitis, inducible laryngeal obstruction, excessive dynamic airway collapse, tracheobronchomalacia, concomitant COPD, bronchiectasis, allergic bronchopulmonary aspergillosis, eosinophilic granulomatosis with polyangiitis, and eosinophilic pneumonia. The identification of the characteristic radiological findings of these conditions is often essential in making the correct diagnosis and provision of appropriate management and treatment. Furthermore, radiological imaging modalities can be used to monitor response to therapy.

Phenotyping of Severe Asthma in the Era of Broad-Acting Anti-Asthma Biologics

Severe asthma is associated with significant morbidity and mortality despite the maximal use of inhaled corticosteroids and additional controller medications, and has a high economic burden. Biologic therapies are recommended for the management of severe, uncontrolled asthma to help to prevent exacerbations and to improve symptoms and health-related quality of life. The effective management of severe asthma requires consideration of clinical heterogeneity that is driven by varying clinical and inflammatory phenotypes, which are reflective of distinct underlying disease mechanisms. Phenotyping patients using a combination of clinical characteristics such as the age of onset or comorbidities and biomarker profiles, including blood eosinophil counts and levels of fractional exhaled nitric oxide and serum total immunoglobulin E, is important for the differential diagnosis of asthma. In addition, phenotyping is beneficial for risk assessment, selection of treatment, and monitoring of the treatment response in patients with asthma. This review describes the clinical and inflammatory phenotypes of asthma, provides an overview of biomarkers routinely used in clinical practice and those that have recently been explored for phenotyping, and aims to assess the value of phenotyping in severe asthma management in the current era of biologics.

A rare mimic of adult-onset asthma: Case report and review of the literature

Adult-onset asthma is extremely variable in its phenotypic presentation and has gained notoriety for the overall poorer treatment outcomes even on standard asthma therapy. Tracheal tumours are rare but when present, exhibit asthma-like phenomenon in adult patients posing great diagnostic challenges. We report two adult patients with tracheal adenoid cystic tumours who were initially treated for adult-onset asthma. Patient 1, a 45-year-old man was diagnosed and managed for adult-onset asthma over a 12 months period without satisfactory control of his symptoms. Following a late episode of hemoptysis, a chest Computed Tomography (CT) scan done revealed an occluding tracheal tumour. Patient 2 is a 28-year-old female who was diagnosed with adult-onset asthma for over 2 years with poor symptom control despite optimal asthma therapy. She developed cough-induced subcutaneous emphysema for which a chest CT scan revealed a tracheal mass. The patient had surgery with incomplete resection of tumour and adjuvant radiotherapy.

Application of deep learning for bronchial asthma diagnostics using respiratory sound recordings

Methods of computer-assisted diagnostics that utilize deep learning techniques on recordings of respiratory sounds have been developed to diagnose bronchial asthma. In the course of the study an anonymous database containing audio files of respiratory sound recordings of patients suffering from different respiratory diseases and healthy volunteers has been accumulated and used to train the software and control its operation. The database consists of 1,238 records of respiratory sounds of patients and 133 records of volunteers. The age of tested persons was from 18 months to 47 years. The sound recordings were captured during calm breathing at four points: in the oral cavity, above the trachea, at the chest, the second intercostal space on the right side, and at the point on the back. The developed software provides binary classifications (diagnostics) of the type: "sick/healthy" and "asthmatic patient/non-asthmatic patient and healthy". For small test samples of 50 (control group) to 50 records (comparison group), the diagnostic sensitivity metric of the first classifier was 88%, its specificity metric -86% and accuracy metric -87%. The metrics for the classifier "asthmatic patient/non-asthmatic patient and healthy" were 92%, 82%, and 87%, respectively. The last model applied to analyze 941 records in asthmatic patients indicated the correct asthma diagnosis in 93% of cases. The proposed method is distinguished by the fact that the trained model enables diagnostics of bronchial asthma (including differential diagnostics) with high accuracy irrespective of the patient gender and age, stage of the disease, as well as the point of sound recording. The proposed method can be used as an additional screening method for preclinical bronchial asthma diagnostics and serve as a basis for developing methods of computer assisted patient condition monitoring including remote monitoring and real-time estimation of treatment effectiveness.

The Buddhasothorn Asthma Severity Score (BASS): A practical screening tool for predicting severe asthma exacerbations for pediatric patients

BACKGROUND AND AIM

A precise scaling system of acute asthma leads to an accurate assessment of disease severity. This study aimed to compare the accuracy of the Buddhasothorn Asthma Severity Score (BASS) with the Wood-Downes-Ferrés Scale (WDFS) to recognize the severity level of acute asthma.

MATERIALS AND METHODS

A cross-sectional study was conducted comprising Thai children aged 2-15 years with acute asthma. The BASS and WFDS were rated once in the emergency department. The degree of severity was determined by frequency and type of nebulized bronchodilator administrations at the time of initial treatment. The optimum cutoff points for the area under the curve (AUC) were established to predict severe asthma exacerbations.

RESULTS

All 73 episodes of asthma exacerbations (EAEs) in 35 participants were analyzed. Fifty-nine (80.8%) EAEs were classified as severe. Both scales had good significance to recognize the selection of nebulized bronchodilator treatments by AUC of 0.815 (95% Confidence Interval [CI]: 0.680-0.950) in case of BASS, and AUC of 0.822 (95% CI: 0.70-0.944) in case of WDFS. Cutoff points of BASS ≥ 8 had sensitivity 72.9%, specificity 64.3%, positive predictive value (PPV) 89.6%, negative predictive value (NPV) 36.0% at an AUC of 0.718 (95% CI: 0.563-0.873) for severe exacerbations. These results were consistent for cutoff points of WDFS ≥ 5 with sensitivity 78.0%, specificity 50.0%, PPV 86.8%, NPV 35.0% at an AUC of 0.768 (95% CI: 0.650-0.886) for predicting severe exacerbations. There was no significant difference between the AUCs of both scales.

CONCLUSIONS

Both the BASS and WDFS were good and accurate scales and effective screening tools for predicting severe asthma exacerbations in pediatric patients by optimal cutoff points.

Risk factors for the development of bronchiectasis in patients with asthma

Though asthma and bronchiectasis are two different diseases, their coexistence has been demonstrated in many patients. The aim of the present study is to compare the characteristics of asthmatic patients with and without bronchiectasis and to assess risk factors for the development of this condition. Two hundred and twenty-four moderate-severe asthmatic patients were included. The severity of bronchiectasis was assessed by Reiff and FACED parameters. Logistic regression was used to identify independent factors associated with bronchiectasis. Bronchiectasis was identified in 78 asthma patients. In severe asthma patients, its prevalence was 56.9%. Bronchiectasis was defined as mild in81% of patients using modified Reiff criteria and in 74% using FACED criteria. Asthmatic patients with bronchiectasis had decreasing FEV1, FVC and FEV1/FVC (p = 0.002, 0.005 and 0.014 respectively), presented more frequent asthma exacerbations (p < 0.001) and worse asthma control (ACT 21 vs 16pts, p < 0.001). Factors independently associated with bronchiectasis were older age (42-65 years: OR, 3.99; 95% CI 1.60 to 9.95, P = 0.003; ≥ 65 years: OR, 2.91; 95% CI 1.06 to 8.04, P = 0.039), severe asthma grade (OR, 8.91; 95% CI 3.69 to 21.49; P < 0.001) and frequency of asthma exacerbations (OR, 4.43; 95% CI 1.78 to 11.05; P < 0.001). In patients with severe asthma, age of asthma onset (OR, 1.02; 95% CI 1.01 to 1.04; P = 0.015) and asthma exacerbations (OR, 4.88; 95% CI 1.98 to 12.03; P = 0.001) were independently associated with the development of bronchiectasis. The prevalence of bronchiectasis in severe asthmatic patients is high. Age of asthma onset and exacerbations were independent factors associated with the occurrence of bronchiectasis.

Obese, non-eosinophilic asthma: frequent exacerbators in a real-world setting

OBJECTIVE

In the UK, asthma deaths are at their highest level this century. Increased recognition of at-risk patients is needed. This study phenotyped frequent asthma exacerbators and used machine learning to predict frequent exacerbators.

METHODS

Patients admitted to a district general hospital with an asthma exacerbation between 1st March 2018 and 1st March 2020 were included. Patients were organized into two groups: "Infrequent Exacerbators" (1 admission in the previous 12 months) and "Frequent Exacerbators" (≥2 admissions in the previous 12 months). Patient data were retrospectively collected from hospital and primary care records. Machine learning models were used to predict frequent exacerbators.

RESULTS

200 patients admitted for asthma exacerbations were randomly selected (73% female; mean age 47.8 years). Peripheral eosinophilia was uncommon in either group (21% vs 19%). More frequent exacerbators were being treated with high-dose ICS than infrequent exacerbators (46.5% vs 23.2%; P < 0.001), and frequent exacerbators used more SABA inhalers (10.9 vs 7.40; P = 0.01) in the year preceding the current admission. BMI was raised in both groups (34.2 vs 30.9). Logistic regression was the most accurate machine learning model for predicting frequent exacerbators (AUC = 0.80).

CONCLUSIONS

Patients admitted for asthma are predominately female, obese and non-eosinophilic. Patients who require multiple admissions per year have poorer asthma control at baseline. Machine learning algorithms can predict frequent exacerbators using clinical data available in primary care. Instead of simply increasing the dose of corticosteroids, multidisciplinary management targeting Th2-low inflammation should be considered for these patients.

Physicians' Knowledge and Practices Regarding Asthma in Jordan: A Cross-Sectional Study

Objectives: Asthma is a chronic non-communicable disease that causes significant morbidity and mortality and requires ongoing clinical care. Appropriate knowledge by physicians is vital in the management of asthma. Therefore, this study aims to explore and identify the gaps in physicians' knowledge and practices concerning the management of asthma.

Methods: A cross-sectional study using an online survey was conducted in Jordan to understand the gap in knowledge and practices in the management of asthma among physicians. A previously validated questionnaire was employed, the Physicians' Practice Assessment Questionnaire (PPAQ). The knowledge questions based on the Global Initiative for Asthma (GINA) guidelines were used to assess knowledge and practice among the study's participants. Predictors of good practice were identified using logistic regression.

Results: A total of 271 physicians participated in this survey. The overall knowledge among physicians scored above 78%. However, gaps were pertinent to identifying the signs of asthma attacks that accounted for 61.9% of the participants, whereas only 67.6% of the physicians knew the drugs used for the management of asthma. The study revealed alarming results when practices were assessed, with the overall percentage of physicians applying the required practices did not exceed 57.1 ± 25.7%. Logistic regression analysis to determine predictors of good practice showed that out of several independent variables, physicians who see 6–10 asthma patients per day are five times more likely to follow the guidelines' recommendations in their practice; senior physicians (>50 years old) and those who see 1–5 asthma patients daily are around two times more likely to follow the guidelines (p 0.001).

Conclusions: The findings of this study identified the need to transform knowledge into practice. This could be achieved through professional education and constant reminders to physicians in a simple form, as well as a clinical audit of practice. There is a need for novel knowledge transfer approaches to induce behavioral and practice change toward the management of asthma.

The emerging role of quantitative imaging in asthma

Quantitative imaging of the lung has proved to be a valuable tool that has improved our understanding of asthma. CT, MRI, and positron emission tomography have all been utilized in asthma with each modality having its own distinct advantages and disadvantages. Research has now demonstrated that quantitative imaging plays a valuable role in characterizing asthma phenotypes and endotypes, as well as potentially predicting future asthma morbidity. Nonetheless, future research is needed in order to minimize radiation exposure, standardize reporting, and further delineate how imaging can predict longitudinal outcomes. With future work, quantitative imaging may make its way into the clinical care of asthma and change our practice.

Imaging of Airway Obstruction in Children

Various imaging techniques may be used to diagnose airway obstruction in children. Digital radiography, computed tomography and magnetic resonance imaging are the most important modalities, but the choice of technique will depend on the level and nature of suspected obstruction, as well as patient-specific factors such as age and ability to cooperate. This review examines the forms of airway obstruction that are commonly encountered in childhood.

Obstructions in the lower airways lead to altered airflow patterns in the central airway

A characteristic feature of obstructive lung diseases is the narrowing of small airways, which affects regional airflow patterns within the lung. However, the extent to which these patterns differ between healthy and diseased states is unknown. To investigate airflow patterns in detail, we first used particle image velocimetry measurements to validate a large eddy simulation model of flow in a patient-specific geometry. We then predicted flow patterns in the central airway under exhalation for three flow conditions-normal, intermediate, and severe-where boundary conditions represented the effect of lower airway obstructions. We computed Pearson correlation coefficients (R) to assess the similarity of flow patterns, and found that flow patterns demonstrated the greatest differentiation between flow conditions in the right main bronchi (R ≤0.60), whereas those in the secondary branches and regions of the trachea showed high correlation (R ≥0.90). These results indicate that although flow patterns are distinct between flow conditions, the choice of measurement location is critical for differentiation.

FASE-CPHG study: a panoramic snapshot of difficult-to-treat, severe asthma in French nonacademic hospitals

BACKGROUND

Real-world data describing management of patients with severe asthma are limited. To address this issue, we conducted FASE-CPHG (France Asthme Sevère - Collège des Pneumologues des Hôpitaux Généraux), a descriptive, multicentric, and observational cross-sectional study.

METHODS

French pulmonologists from nonacademic hospitals completed questionnaires on patient characteristics and ongoing asthma treatment for severe asthmatic patients observed during the inclusion period. In addition, we collected data from patients via self-assessment questionnaires.

FINDINGS

104 physicians recruited 1502 patients within 1 year. The mean age of the 1465 patients analysed was 54.4±16.1 years. Severe asthmatic patients were more frequently female (63%), with a history of atopy (65%). Most patients remained poorly controlled or uncontrolled, with an important difference between physicians' opinion and the Global Initiative for Asthma criteria (63% versus 96%). The most common comorbidities included ear, nose and throat diseases (59% of cases); anxiety (40%); and gastro-oesophageal reflux disease (39%). Allergic sensitisation tests and/or blood eosinophil count evaluation, and spirometry were performed in 92% and 98% of patients, respectively. The mean eosinophil count and total serum IgE were 437 cells·mm-3 and 546 UI·L-1, respectively. In addition to high doses of inhaled corticosteroids plus long-acting β2-agonists, patients were receiving leukotriene receptor antagonists (52%), anticholinergic drugs (34%), anti IgE (27%) and oral corticosteroids (17%); 65% adhered to their treatment.

INTERPRETATION

This study provides insight into the characteristics and management of severe asthma in France and may help improve knowledge on this pathology, which represents a high burden to healthcare.

Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.

Imaging approaches to understand disease complexity: chronic obstructive pulmonary disease as a clinical model

The clinical manifestations of chronic obstructive pulmonary disease (COPD) reflect an aggregate of multiple pulmonary and extrapulmonary processes. It is increasingly clear that full assessment of these processes is essential to characterize disease burden and to tailor therapy. Medical imaging has advanced such that it is now possible to obtain in vivo insight in the presence and severity of lung disease-associated features. In this review, we have assembled data from multiple disciplines of medical imaging research to review the role of imaging in characterization of COPD. Topics include imaging of the lungs, body composition, and extrapulmonary tissue metabolism. The primary focus is on imaging modalities that are widely available in clinical care settings and that potentially contribute to describing COPD heterogeneity and enhance our insight in underlying pathophysiological processes and their structural and functional effects.