Structured reporting platform improves CAD-RADS assessment.

BACKGROUND: Structured reporting in cardiac imaging is strongly encouraged to improve quality through consistency. The Coronary Artery Disease - Reporting and Data System (CAD-RADS) was recently introduced to facilitate interdisciplinary communication of coronary CT angiography (CTA) results. We aimed to assess the agreement between manual and automated CAD-RADS classification using a structured reporting platform. METHODS: Five readers prospectively interpreted 500 coronary CT angiographies using a structured reporting platform that automatically calculates the CAD-RADS score based on stenosis and plaque parameters manually entered by the reader. In addition, all readers manually assessed CAD-RADS blinded to the automatically derived results, which was used as the reference standard. We evaluated factors influencing reader performance including CAD-RADS training, clinical load, time of the day and level of expertise. RESULTS: Total agreement between manual and automated classification was 80.2%. Agreement in stenosis categories was 86.7%, whereas the agreement in modifiers was 95.8% for "N", 96.8% for "S", 95.6% for "V" and 99.4% for "G". Agreement for V improved after CAD-RADS training (p = 0.047). Time of the day and clinical load did not influence reader performance (p > 0.05 both). Less experienced readers had a higher total agreement as compared to more experienced readers (87.0% vs 78.0%, respectively; p = 0.011). CONCLUSIONS: Even though automated CAD-RADS classification uses data filled in by the readers, it outperforms manual classification by preventing human errors. Structured reporting platforms with automated calculation of the CAD-RADS score might improve data quality and support standardization of clinical decision making

CT in planning transcatheter aortic valve implantation procedures and risk assessment.

For surgical aortic valve replacement, the Society of Thoracic Surgeons score (STSS) is the reference standard for the prediction of operative risk. In transcatheter aortic valve implantation (TAVI) though, where the procedure itself is minimally invasive, the traditional risk assessment is supplemented by CTA. Through a consistent approach to the acquisition of high-quality images and the standardised reporting of annular measurements and adverse root and vascular features, patients at risk of complications can be identified. In turn, this may allow for a personalised procedural approach and treatment strategies devised to potentially reduce or mitigate this risk. This article provides a systematic and standardised approach to pre-procedural work-up with computed tomography angiography (CTA) and explores the current state of evidence and future areas of development in this rapidly developing field

Phenotyping COPD exacerbations using imaging and blood-based biomarkers

Nawaf M Alotaibi,1,2 Virginia Chen,1,3,4 Zsuzsanna Hollander,1,3,4 Cameron J Hague,5 Darra T Murphy,5 Jonathon A Leipsic,5 Mari L DeMarco,1,6 J Mark FitzGerald,7,8 Bruce M McManus,1,3,4,6 Raymond T Ng,4,9 Don D Sin1,3,7 1Centre for Heart Lung Innovation, James Hogg Research Centre, St Paul’s Hospital, Vancouver, BC, Canada; 2Division of Pulmonary Medicine, Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 3Institute for Heart Lung Health, 4PROOF Centre of Excellence, 5Department of Radiology, St Paul’s Hospital, 6Department of Pathology and Laboratory Medicine, 7Division of Respiratory Medicine, Department of Medicine, University of British Columbia, 8The Lung Centre, Vancouver General Hospital, 9Department of Computer Sciences, University of British Columbia, Vancouver, BC, Canada Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are caused by a variety of different etiologic agents. Our aim was to phenotype COPD exacerbations using imaging (chest X-ray [CXR] and computed tomography [CT]) and to determine the possible role of the blood tests (C-reactive protein [CRP], the N-terminal prohormone brain natriuretic peptide [NT-proBNP]) as diagnostic biomarkers. Materials and methods: Subjects who were hospitalized with a primary diagnosis of AECOPD and who had had CXRs, CT scans, and blood collection for CRP and NT-proBNP were assessed in this study. Radiologist blinded to the clinical and laboratory characteristics of the subjects interpreted their CXRs and CT images. ANOVA and Spearman’s correlation were performed to test for associations between these imaging parameters and the blood-based biomarkers NT-proBNP and CRP; logistic regression models were used to assess the performance of these biomarkers in predicting the radiological parameters. Results: A total of 309 subjects were examined for this study. Subjects had a mean age of 65.6±11.1 years, 66.7% of them were males, and 62.4% were current smokers, with a mean FEV1 54.4%±21.5% of predicted. Blood NT-proBNP concentrations were associated with cardiac enlargement (area under the curve [AUC] =0.72, P<0.001), pulmonary edema (AUC =0.63, P=0.009), and pleural effusion on CXR (AUC =0.64, P=0.01); whereas on CT images, NT-proBNP concentrations were associated with pleural effusion (AUC =0.71, P=0.002). Serum CRP concentrations, on the other hand, were associated with consolidation on CT images (AUC =0.75, P<0.001), ground glass opacities (AUC =0.64, P=0.028), and pleural effusion (AUC =0.72, P<0.001) on CT images. A serum CRP sensitivity-oriented cutoff point of 11.5 mg/L was selected for the presence of consolidation on CT images in subjects admitted as cases of AECOPD, which has a sensitivity of 91% and a specificity of 53% (P<0.001). Conclusion: Elevated CRP may indicate the presence of pneumonia, while elevated NT-proBNP may indicate cardiac dysfunction. These readily available blood-based biomarkers may provide more accurate phenotyping of AECOPD and enable the discovery of more precise therapies. Keywords: chronic obstructive pulmonary disease, exacerbation, biomarker, CT scan, chest X-ra

Phenotyping and outcomes of hospitalized COPD patients using rapid molecular diagnostics on sputum samples

Nawaf M Alotaibi,1,2 Virginia Chen,1,3,4 Zsuzsanna Hollander,1,3,4 Jonathon A Leipsic,5 Cameron J Hague,5 Darra T Murphy,5 Mari L DeMarco,1,6 JM FitzGerald,3,7,8 Bruce M McManus,1,3,4,6 Raymond T Ng,4,9 Don D Sin1,3,7 1Centre for Heart Lung Innovation, James Hogg Research Centre, St Paul’s Hospital, Vancouver, BC, Canada; 2Department of Medicine, Division of Pulmonary Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 3Institute for Heart and Lung Health, Vancouver, BC, Canada; 4PROOF Centre of Excellence, Vancouver, BC, Canada; 5Department of Radiology, St Paul’s Hospital, Vancouver, BC, Canada; 6Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada; 7Division of Respiratory Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada; 8The Lung Centre, Vancouver General Hospital, Vancouver, BC, Canada; 9Department of Computer Sciences, University of British Columbia, Vancouver, BC, Canada Background: Etiologies of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are heterogeneous. We phenotyped severe AECOPD based on molecular pathogen detection of sputum samples collected at hospitalization of COPD patients and determined their outcomes.Methods: We phenotyped 72 sputum samples of COPD patients who were hospitalized with a primary diagnosis of AECOPD using a molecular array that detected common bacterial and viral respiratory pathogens. Based on these results, the patients were classified into positive or negative pathogen groups. The pathogen-positive group was further divided into virus or bacteria subgroups. Admission day 1 blood samples were assayed for N-terminal prohormone brain natriuretic peptide, CRP, and complete blood counts.Results: A total of 52 patients had a positive result on the array, while 20 patients had no pathogens detected. The most common bacterial pathogen detected was Haemophilus influenzae and the most common virus was rhinovirus. The pathogen-negative group had the worse outcomes with longer hospital stays (median 6.5 vs 5 days for bacteria-positive group, P=0.02) and a trend toward increased 1-year mortality (P=0.052). The bacteria-positive group had the best prognosis, whereas the virus-positive group had outcomes somewhere in between the bacteria-positive and pathogen-negative groups.Conclusion: Molecular diagnostics on sputum can rapidly phenotype serious AECOPD into bacteria-, virus-, or pathogen-negative groups. The bacteria-positive group appears to have the best prognosis, while pathogen-negative group has the worst. These data suggest that AECOPD is a heterogeneous event and that accurate phenotyping of AECOPD may lead to novel management strategies that are personalized and more precise. Keywords: COPD, molecular pathogen detection, exacerbation phenotype