Canadian Respiratory Journal
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Acceptance rate21%
Submission to final decision111 days
Acceptance to publication23 days
CiteScore3.500
Journal Citation Indicator0.460
Impact Factor2.130

Article of the Year 2021

A Follow-Up Study of Lung Function and Chest Computed Tomography at 6 Months after Discharge in Patients with Coronavirus Disease 2019

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 Journal profile

Canadian Respiratory Journal provides a multidisciplinary forum for research in all areas of respiratory medicine. The journal publishes research related to asthma, allergy, COPD, non-invasive ventilation, therapeutic intervention etc.

 Editor spotlight

Chief Editor, Dr Alice M Turner, is based at the University of Birmingham, UK. Her main research interests are the clinical aspects of alpha 1 antitrypsin deficiency and chronic obstructive pulmonary disease.

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Do you think there is an emerging area of research that really needs to be highlighted? Or an existing research area that has been overlooked or would benefit from deeper investigation? Raise the profile of a research area by leading a Special Issue.

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Research Article

Deep Learning Models to Predict Fatal Pneumonia Using Chest X-Ray Images

Background and Aims. Chest X-ray (CXR) is indispensable to the assessment of severity, diagnosis, and management of pneumonia. Deep learning is an artificial intelligence (AI) technology that has been applied to the interpretation of medical images. This study investigated the feasibility of classifying fatal pneumonia based on CXR images using deep learning models on publicly available platforms. Methods. CXR images of patients with pneumonia at diagnosis were labeled as fatal or nonfatal based on medical records. We applied CXR images from 1031 patients with nonfatal pneumonia and 243 patients with fatal pneumonia for training and self-evaluation of the deep learning models. All labeled CXR images were randomly allocated to the training, validation, and test datasets of deep learning models. Data augmentation techniques were not used in this study. We created two deep learning models using two publicly available platforms. Results. The first model showed an area under the precision-recall curve of 0.929 with a sensitivity of 50.0% and a specificity of 92.4% for classifying fatal pneumonia. We evaluated the performance of our deep learning models using sensitivity, specificity, PPV, negative predictive value (NPV), accuracy, and F1 score. Using the external validation test dataset of 100 CXR images, the sensitivity, specificity, accuracy, and F1 score were 68.0%, 86.0%, 77.0%, and 74.7%, respectively. In the original dataset, the performance of the second model showed a sensitivity, specificity, and accuracy of 39.6%, 92.8%, and 82.7%, respectively, while external validation showed values of 38.0%, 92.0%, and 65.0%, respectively. The F1 score was 52.1%. These results were comparable to those obtained by respiratory physicians and residents. Conclusions. The deep learning models yielded good accuracy in classifying fatal pneumonia. By further improving the performance, AI could assist physicians in the severity assessment of patients with pneumonia.

Research Article

Cost-Effectiveness of a New Outpatient Pulmonology Care Model Based on Physician-to-Physician Electronic Consultation

Introduction. This study assesses the impact of an electronic physician-to-physician consultation program on the waiting list and the costs of a Pulmonology Unit. Materials and Methods. A prepost intervention study was conducted after a new ambulatory pulmonary care protocol was implemented and the capacity of the unit was adopted. In the new model, physicians at all levels of healthcare send electronic consultations to specialists. Results. In the preintervention year (2019), the Unit of Pulmonology attended 7,055 consultations (466 e-consultations and 6,589 first face-to-face visits), which decreased to 6,157 (3,934 e-consultations and 2,223 first face-to-face visits; 12.7% reduction) in the postintervention year (all were e-consultations). The mean wait time for the first appointment was 25.7 days in 2019 versus 3.2 days in 2021 (). In total, 43.5% of cases were solved via physician-to-physiciane-consultation. A total of 2,223 patients needed a face-to-face visit, with a mean wait time of 7.5 days. The mean of patients in the waiting listing decreased from 450.8 in 2019 to 44.8 in 2021 (90% reduction). The annual time devoted to e-consultations and first face-to-face visits following an e-consultation diminished significantly after the intervention (1,724 hours versus 2,312.8; 25.4% reduction). Each query solved via e-consultation represented a saving of €652.8, resulting in a total annual saving of €827,062. Conclusions. Physician-to-physiciane-consultations reduce waiting times, improve access of complex patients to specialty care, and ensure that cases are managed at the appropriate level. E-consultation reduces costs, which benefits both, society and the healthcare system.

Research Article

Efficacy of Intrapleural or Intrapericardial Injection of Single Bevacizumab in the Treatment of Lung Cancer-Mediated Malignant Effusion

The usage of bevacizumab for malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE) has attracted increasing interest from researchers, but the precise ways of bevacizumab administration remain unknown. Patients with histologically or cytologically confirmed non-small-cell lung cancer (NSCLC) with MPE or MPCE were enrolled in the study and treated with a low dose of single bevacizumab (100 mg) intrapleurally or intrapericardially injected after the drainage of the effusions. The Lung Cancer Symptom Scale (LCSS), efficacy, and safety of drug administration were used as evaluation parameters in this study. The results indicated that lung cancer-related symptoms were significantly improved following treatment, compared with symptoms before the treatment (LCSS, score 494 ± 78 vs. score 377 ± 77, mean ± SD) (). Malignant effusions were well controlled, and the median time to progression (TTP) was 91 days and 111 days in MPE and MPCE, respectively. In addition, no severe side effects were observed, except in one patient with mild dizziness. In summary, the low dose of single bevacizumab (100 mg) with intrapleural or intrapericardial injection is effective and safe in the treatment of lung cancer-mediated malignant effusion, rapidly improving the malignant effusion-related symptoms and quality of life in patients with NSCLC.

Research Article

Blood Eosinophil Endotypes across Asthma and Chronic Obstructive Pulmonary Disease (COPD)

Background. Eosinophils were common inflammatory cells involved in the occurrence and development of various inflammatory diseases. Multiple recent studies have pointed to the increasingly important role of eosinophils in respiratory diseases. This article aims to compare the expression differences of blood eosinophil counts between asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap (ACO). Methods. Patients with asthma, COPD, and ACO who were seen in the First Affiliated Hospital of Guangzhou Medical University from January 2012 to June 2019 were included. We collected information such as age, gender, diagnosis, the eosinophil counts from the medical records. Moreover, the levels of 10 cytokines in the plasma of each group were detected by using the Meso Scale Discovery method. Results. We included 9787 patients with asthma, 15806 patients with COPD, and 831 ACO patients. From our results, it can be first found that eosinophil levels were age-related in the three diseases (asthma and ACO: ; COPD: ); in asthma and COPD, the number of eosinophils in males was more significant than that in females (asthma: ; COPD: ). Second, asthma patients had higher blood eosinophil counts than those with COPD and ACO (). Moreover, we found out that eosinophil levels were highly expressed in the stable group of all three diseases. Finally, we found that most cytokines in ACO patients showed a downward trend when the level of eosinophils was low, whereas the results were reversed in asthma patients; 7 cytokines had similar trends in COPD and ACO patients. Conclusions. In conclusion, eosinophils have their own unique endotypes in asthma, COPD, and ACO patients, which were reflected in the fluctuation of their levels and changes in cytokine secretion.

Research Article

Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules

Ground-glass nodule (GGN)-like adenocarcinoma is a special subtype of lung cancer. The invasiveness of the nodule correlates well with the patient’s prognosis. This study aimed to establish a radiomic model for invasiveness differentiation of malignant nodules manifesting as ground glass on high-resolution computed tomography (HRCT). Between January 2014 and July 2019, 276 pulmonary nodules manifesting as GGNs on preoperative HRCTs, whose histological results were available, were collected. The nodules were randomly classified into training (n = 221) and independent testing (n = 55) cohorts. Three logistic models using features derived from HRCT were fit in the training cohort and validated in both aforementioned cohorts for invasive adenocarcinoma and preinvasive-minimally invasive adenocarcinoma (MIA) differentiation. The model with the best performance was presented as a nomogram and was validated using a calibration curve before performing a decision curve analysis. The benefit of using the proposed model was also shown by groups of management strategies recommended by The Fleischner Society. The combined model showed the best differentiation performance (area under the curve (AUC), training set = 0.89, and testing set = 0.92). The quantitative texture model showed better performance (AUC, training set = 0.87, and testing set = 0.91) than the semantic model (AUC, training set = 0.83, and testing set = 0.79). Of the 94 type 2 nodules that were IACs, 66 were identified by this model. Models using features derived from imaging are effective for differentiating between preinvasive-MIA and IACs among lung adenocarcinomas appearing as GGNs on CT images.

Research Article

The Nutritional Status of Chronic Obstructive Pulmonary Disease Exacerbators

Introduction. Malnutrition is underdiagnosed in chronic obstructive pulmonary disease. Objectives. This study aimed to evaluate the nutritional status of COPD patients and the link between dyspnea and nutritional status. Methods. This longitudinal observational study included patients hospitalized with exacerbated COPD. Nutritional status was assessed using Nutrition Risk Screening 2002, anthropometric, and biochemical assessments, in the first 48 hours of hospitalization. Results. Thirty patients were evaluated. According to the Nutrition Risk Screening 2002, half of the patients were at increased risk of malnutrition. 36.7% were classified as malnourished if we only considered the body mass index. From the evaluation of the tricipital skin fold, 69.0% were classified as malnourished, with 48.3% having severe malnutrition. According to the serum albumin level, 29.6% had malnutrition criteria. A significant association between dyspnea and increasing age () was found. There was a strong association between the fold classification and the degrees of severity of dyspnea (Fisher exact test: 13.60, , V Cramer = 0.826). Most patients were malnourished and had higher grades of dyspnea. Tricipital skinfold reflects subcutaneous adipose tissue; this anthropometric measurement seems to be a good method to classify the nutritional status of COPD patients. It classified the biggest portion of patients as malnourished. Conclusion. The number of patients classified as malnourished changed with the method under analysis. The tricipital skin fold parameter was strongly associated with the dyspnea score. Most patients had adipose tissue and muscular mass depletion.

Canadian Respiratory Journal
 Journal metrics
See full report
Acceptance rate21%
Submission to final decision111 days
Acceptance to publication23 days
CiteScore3.500
Journal Citation Indicator0.460
Impact Factor2.130
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.