<b><i>Introduction:</i></b> Although severe acute respiratory syndrome coronavirus-2 infection is causing mortality in considerable proportion of coronavirus disease-2019 (COVID-19) patients, however, evidence for the association of sex, age, and comorbidities on the risk of mortality is not well-aggregated yet. It was aimed to assess the association of sex, age, and comorbidities with mortality in COVID-2019 patients. <b><i>Methods:</i></b> Literatures were searched using different keywords in various databases. Relative risks (RRs) were calculated by RevMan software where statistical significance was set as <i>p</i> &#x3c; 0.05. <b><i>Results:</i></b> COVID-19 male patients were associated with significantly increased risk of mortality compared to females (RR 1.86: 95% confidence interval [CI] 1.67–2.07; <i>p</i> &#x3c; 0.00001). Patients with age ≥50 years were associated with 15.4-folds significantly increased risk of mortality compared to patients with age &#x3c;50 years (RR 15.44: 95% CI 13.02–18.31; <i>p</i> &#x3c; 0.00001). Comorbidities were also associated with significantly increased risk of mortality; kidney disease (RR 4.90: 95% CI 3.04–7.88; <i>p</i> &#x3c; 0.00001), cereborovascular disease (RR 4.78; 95% CI 3.39–6.76; <i>p</i> &#x3c; 0.00001), cardiovascular disease (RR 3.05: 95% CI 2.20–4.25; <i>p</i> &#x3c; 0.00001), respiratory disease (RR 2.74: 95% CI 2.04–3.67; <i>p</i> &#x3c; 0.00001), diabetes (RR 1.97: 95% CI 1.48–2.64; <i>p</i> &#x3c; 0.00001), hypertension (RR 1.95: 95% CI 1.58–2.40; <i>p</i> &#x3c; 0.00001), and cancer (RR 1.89; 95% CI 1.25–2.84; <i>p</i> = 0.002) but not liver disease (RR 1.64: 95% CI 0.82–3.28; <i>p</i>= 0.16). <b><i>Conclusion:</i></b> Implementation of adequate protection and interventions for COVID-19 patients in general and in particular male patients with age ≥50 years having comorbidities may significantly reduce risk of mortality associated with COVID-19.

Background Breath volatile organic compounds (VOCs) may be useful for asthma diagnosis and phenotyping, identifying patients who could benefit from personalised therapeutic strategies. The authors aimed to identify specific patterns of breath VOCs in patients with asthma and in clinically relevant disease phenotypes. Methods Breath samples were analysed by gas chromatographyemass spectrometry. The Asthma Control Questionnaire was completed, together with lung function and induced sputum cell counts. Breath data were reduced to principal components, and these principal components were used in multiple logistic regression to identify discriminatory models for diagnosis, sputum inflammatory cell profile and asthma control. Results The authors recruited 35 patients with asthma and 23 matched controls. A model derived from 15 VOCs classified patients with asthma with an accuracy of 86%, and positive and negative predictive values of 0.85 and 0.89, respectively. Models also classified patients with asthma based on the following phenotypes: sputum (obtained in 18 patients with asthma) eosinophilia $2% area under the receiver operating characteristics (AUROC) curve 0.98, neutrophilia $40% AUROC 0.90 and uncontrolled asthma (Asthma Control Questionnaire $1) AUROC 0.96. Conclusions Detection of characteristic breath VOC profiles could classify patients with asthma versus controls, and clinically relevant disease phenotypes based on sputum inflammatory profile and asthma control. Prospective validation of these models may lead to clinical application of non-invasive breath profiling in asthma.

BackgroundNon-invasive phenotyping of chronic respiratory diseases would be highly beneficial in the personalised medicine of the future. Volatile organic compounds can be measured in the exhaled breath and may be produced or altered by disease processes. We investigated whether distinct patterns of these compounds were present in chronic obstructive pulmonary disease (COPD) and clinically relevant disease phenotypes.MethodsBreath samples from 39 COPD subjects and 32 healthy controls were collected and analysed using gas chromatography time-of-flight mass spectrometry. Subjects with COPD also underwent sputum induction. Discriminatory compounds were identified by univariate logistic regression followed by multivariate analysis: 1. principal component analysis; 2. multivariate logistic regression; 3. receiver operating characteristic (ROC) analysis.ResultsComparing COPD versus healthy controls, principal component analysis clustered the 20 best-discriminating compounds into four components explaining 71% of the variance. Multivariate logistic regression constructed an optimised model using two components with an accuracy of 69%. The model had 85% sensitivity, 50% specificity and ROC area under the curve of 0.74. Analysis of COPD subgroups showed the method could classify COPD subjects with far greater accuracy. Models were constructed which classified subjects with ≥2% sputum eosinophilia with ROC area under the curve of 0.94 and those having frequent exacerbations 0.95. Potential biomarkers correlated to clinical variables were identified in each subgroup.ConclusionThe exhaled breath volatile organic compound profile discriminated between COPD and healthy controls and identified clinically relevant COPD subgroups. If these findings are validated in prospective cohorts, they may have diagnostic and management value in this disease.