An Evaluation of quantitative body composition on thoracic computed tomography and the effect on clinical severity in patients with chronic obstructive pulmonary disease

The aim of this study was to evaluate the effect on disease severity of the quantitative measurements of pectoral muscle area (PMA), pectoral muscle index (PMI), pectoral muscle density (PMD), subcutaneous adipose tissue (SAT) and mediastinal adipose tissue (MAT) taken on thoracic computed tomography (CT) of patients with chronic obstructive pulmonary disease (COPD), according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. A retrospective screening was made of patients diagnosed with COPD and applied with thoracic CT and respiratory function tests. For patients with obstructive findings, a record was made of height, weight, body mass index, and smoking history (packet/year). On thoracic axial CT images, the PMA, PMI, PMD, SAT, and MAT values at the aortic arch level were calculated quantitatively using OsiriX software (Pixmeo, Switzerland). The patients were grouped as A-B-C-D according to the GOLD 2018 guidelines. Then two groups were formed as mild-moderate COPD (GOLD A-B) and severe COPD (GOLD C-D). The relationship was evaluated between clinical severity and quantitative body composition values according to the GOLD classification. A total of 80 patients diagnosed with COPD were included in the study comprising 61 males and 19 females. The GOLD A-B group included 43 (53.75%) patients and the GOLD C-D group, 37 (46.25%) patients. No significant difference was determined between the two groups in respect of the PMA, PMI, and PMD values (p=0.001). A statistically significant difference was determined between the groups in respect of the SAT and MAT values (p=0.001, p=0.002, respectively). A cutoff value of <30.04 in PMD (0.964; 95%CI:0.928-1) showed the best performance in predicting the mild-moderate COPD patients (GOLD A-B) with 92% sensitivity and 93% specificity. The results of this study demonstrated that PMD showed the best quantitative body composition performance in the differentiation of mild-moderate and severe COPD disease.

Simulation-based optimisation to quantify heterogeneity of specific ventilation and perfusion in the lung by the Inspired Sinewave Test

The degree of specific ventilatory heterogeneity (spatial unevenness of ventilation) of the lung is a useful marker of early structural lung changes which has the potential to detect early-onset disease. The Inspired Sinewave Test (IST) is an established noninvasive ‘gas-distribution’ type of respiratory test capable of measuring the cardiopulmonary parameters. We developed a simulation-based optimisation for the IST, with a simulation of a realistic heterogeneous lung, namely a lognormal distribution of spatial ventilation and perfusion. We tested this method in datasets from 13 anaesthetised pigs (pre and post-lung injury) and 104 human subjects (32 healthy and 72 COPD subjects). The 72 COPD subjects were classified into four COPD phenotypes based on ‘GOLD’ classification. This method allowed IST to identify and quantify heterogeneity of both ventilation and perfusion, permitting diagnostic distinction between health and disease states. In healthy volunteers, we show a linear relationship between the ventilatory heterogeneity versus age ($${R}^{2}=0.42$$ R 2 = 0.42 ). In a mechanically ventilated pig, IST ventilatory heterogeneity in noninjured and injured lungs was significantly different (p < 0.0001). Additionally, measured indices could accurately identify patients with COPD (area under the receiver operating characteristic curve is 0.76, p < 0.0001). The IST also could distinguish different phenotypes of COPD with 73% agreement with spirometry.

Pulmonary complications and survival after elective infrarenal endovascular abdominal aneurysm repair in patients with documented chronic obstructive pulmonary disease

Background Elective abdominal aortic aneurysm (AAA) repair is advocated in patients where risk of rupture exceeds the risks of peri-procedural morbidity and mortality. Chronic obstructive pulmonary disease (COPD) is a known risk factor for AAA and increased operative morbidity in general. Since literature on the correlation between the clinical classification of COPD and morbidity following endovascular infrarenal AAA repair (EVAR) is scarce, assessment per individual remains a challenge. Objective To analyse the pulmonary and all-cause morbidity and mortality in patients with documented COPD and relate this to their GOLD classification. Methods Sixty-eight patients with COPD, documented by a lung function test, who underwent elective EVAR between July 2002 and July 2018 were retrospectively reviewed. The primary endpoint was the incidence of 30-day pulmonary adverse events. Procedural characteristics, length of hospital stay, pulmonary and all-cause morbidity including major adverse events (MAEs) during follow-up and five-year survival divided per GOLD classification were the secondary endpoints. Results There was no statistically significant difference in the incidence of pulmonary adverse events between GOLD I/II and GOLD III/IV patients. There was neither procedural nor 30-day mortality in either group. Through 30 days and 1 year, there was no difference in pulmonary and all-cause morbidity between groups. Three MAEs occurred in the GOLD I/II group versus 2 MAE in the GOLD III/IV group during the first postoperative year. The five-year survival was 66.0%, 60.9% and 61.9% for patients with GOLD I, GOLD II and GOLD III, respectively. Three of four GOLD IV died within the first year after EVAR. Conclusion EVAR can be safely performed in patients with COPD, with low 30-day morbidity and mortality rates. Although severe co-morbidity should be taken into account, EVAR seems to be justified in patients with COPD with a GOLD classification I, II or III. Further research should focus on optimising the pulmonary condition in patients selected for EVAR.

Evaluation of severity of chronic obstructive pulmonary disease using bronchial obstruction, dyspnea, exercise index and global initiative for chronic obstructive lung disease classification and it’s correlation with physical quality of life measured by St. George’s respiratory disease questionnaire score

Background: The Global initiative for chronic Obstructive Lung Disease (GOLD) criteria classified chronic obstructive pulmonary disease (COPD) by severity into four stages. Recently, Body mass index (BMI), Bronchial Obstruction, Dyspnea, Exercise (BODE) index, was proposed to provide useful prognostic information of COPD patients. Health-related quality of life (HRQOL) is measured by St. George’s Respiratory disease Questionnaire (SGRQ). Study was undertaken to assess correlations between BODE index and GOLD classification with SGRQ score.Methods: Fifty five COPD patients were included. Spirometry was performed in all patients. Modified Medical Research Council (mMRC) scale was used to label severity of dyspnea. Six-minute walking distance (6 MWD) was performed. BODE index was calculated by giving points to BMI, forced expiratory volume in one minute (FEV1), 6 MWD, and mMRC. SGRQ was used to determine HRQOL. Correlation analysis was done using Pearson’s method.Results: Mean symptoms, mean activity, mean impacts and mean total SGRQ score were significantly higher in patients having mMRC scale 0-1, patients who could walk ≤149 meters, and in patients who had GOLD III and IV class. Lesser the FEV1, higher the mMRC grade, and lesser the 6 MWD, worse was the quality of life. BODE index (r = 0.72) and GOLD classification (r =0.59) were significantly and positively correlated with symptoms score, activity score, impacts score and total SGRQ score.Conclusions: BODE index correlated better than the Gold classification with SGRQ score implying that apart from the airflow limitation, functional impairment measured by the 6MWT and mMRC also affect HRQOL.

Latent class cluster analysis

This paper describes the technique of exploratory latent class cluster analysis. The classical analysis is a model-based statistical approach for identifying unobserved subgroups from observed categorical data and for classifying cases into the identified subgroups based on membership probabilities estimated directly from the statistical model. In the first part on mathematical modeling of the paper, we introduce the data and the sampling distribution for the data as required in the analysis of latent classes, the fundamental model assumptions are reviewed, and the general unrestricted latent class model is presented. Classification of cases into the clusters using modal assignment is discussed. In the second part on inferential statistics of the paper, we briefly review the classical maximum likelihood methodology related to parameter estimation and model testing, and the information criteria AIC and SIC for model selection. In the third part on case study of the paper, the General Social Survey data are analyzed using the software Latent GOLD. We present the Latent GOLD profile plot and tri plot options for the graphical representation of the results. The Latent GOLD classification output illustrating the assignment of respondents to the latent survey respondent types is also shown.