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Objective:To explore the independent risk factors that predict 10-year mortality in patients with stable chronic obstructive pulmonary disease(COPD).Methods:The baseline data from a prospective cohort study were analyzed and long-term follow-up were performed. Patients with confirmed diagnosis of stable COPD were consecutively enrolled in the outpatient clinic from January 2010 to December 2010, and were followed up until December 31, 2020. Cox regression analysis was used to determine the independent risk factors for all-cause mortality and mortality from respiratory causes in stable COPD patients.Results:A total of 182 stable COPD patients were enrolled and followed up for a median of 89 months. The 10-year mortality was 51.1%(93/182), and 9 patients died within one year. The leading cause of death was respiratory disorder, followed by cardiovascular and cerebrovascular diseases. The risk factors independently associated with all-cause mortality included old age( HR=1.936,95% CI: 1.610~2.328, P<0.01), increased baseline COPD Assessment Test(CAT)( HR=1.331,95% CI: 1.049-1.689, P=0.02) and the increased CAT in one year( HR=1.314,95% CI: 1.197-1.420, P<0.01). The risk factors independently associated with respiratory cause mortality included increased baseline CAT( HR=1.719,95% CI: 1.026-2.880, P=0.04), emphysema index(LAA%)( HR=1.062,95% CI: 1.007-1.120, P=0.03), and one year inecreased CAT( HR=1.342,95% CI: 1.198-1.505, P<0.01)was a protective factor. Conclusions:Old age, baseline CAT, one year increased in CAT and LAA% were independent influencing factors for 10-year mortality of stable COPD patients.
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ObjectiveThis study aimed to summarize the characteristics of chest CT imaging in Chinese patients with Coronavirus Disease 2019 (COVID-19) to provide reliable evidence for further guiding clinical routine. MethodsPubMed, Embase and Web of Science databases were thoroughly searched to identified relevant articles involving the features of chest CT imaging in Chinese patients with COVID-19. All data were analyzed utilizing R software version i386 4.0.0. Random-effects models were employed to calculate pooled mean differences. Results19 trials incorporating 1332 cases were included in the study. The results demonstrated that the incidence of ground-glass opacities (GGO) was 0.79, consolidation was 0.34; mixed GGO and consolidation was 0.46; air bronchogram sign was 0.41; crazy paving pattern was 0.32; interlobular septal thickening was 0.55; reticulation was 0.30; bronchial wall thickening was 0.24; vascular enlargement was 0.74. subpleural linear opacity was 0.28; intrathoracic lymph node enlargement was 0.03; pleural effusions was 0.03. The distribution in lung: the incidence of central was 0.05; peripheral was 0.74; peripheral involving central was 0.38; diffuse was 0.19; unifocal involvement was 0.09; multifocal involvement was 0.57; unilateral was 0.16; bilateral was 0.83; The incidence of lobes involved (>2) was 0.70; lobes involved ([less double equals]2) was 0.35. ConclusionGGO, vascular enlargement, interlobular septal thickening more frequently occurred in patients with COVID-19. Peripheral, bilateral, involved lobes >2 might be the features of COVID-19 in the distribution aspect. Therefore, based on the aboved features of COVID-19 in chest CT imaging, it might be a promising means for identifying COVID-19.
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ObjectiveTo evaluate the diagnostic efficiency of different methods in detecting COVID-19 to provide preliminary evidence on choosing favourable method for COVID-19 detection. MethodsPubMed, Web of Science and Embase databases were searched for identifing eligible articles. All data were calculated utilizing Meta Disc 1.4, Revman 5.3.2 and Stata 12. The diagnostic efficiency was assessed via these indicators including summary sensitivity and specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), summary receiver operating characteristic curve (sROC) and calculate the AUC. Results18 articles (3648 cases) were included. The results showed no significant threshold exist. EPlex: pooled sensitivity was 0.94; specificity was 1.0; PLR was 90.91; NLR was 0.07; DOR was 1409.49; AUC=0.9979, Q*=0.9840. Panther Fusion: pooled sensitivity was 0.99; specificity was 0.98; PLR was 42.46; NLR was 0.02; DOR was 2300.38; AUC=0.9970, Q*=0.9799. Simplexa: pooled sensitivity was 1.0; specificity was 0.97; PLR was 26.67; NLR was 0.01; DOR was 3100.93; AUC=0.9970, Q*=0.9800. Cobas(R): pooled sensitivity was 0.99; specificity was 0.96; PLR was 37.82; NLR was 0.02; DOR was 3754.05; AUC=0.9973, Q*=0.9810. RT-LAMP: pooled sensitivity was 0.98; specificity was 0.99; PLR was 36.22; NLR was 0.04; DOR was 751.24; AUC=0.9905, Q*=0.9596. Xpert Xpress: pooled sensitivity was 0.99; specificity was 0.97; PLR was 27.44; NLR was 0.01; DOR was 3488.15; AUC=0.9977, Q*=0.9829. ConclusionsThese methods (ePlex, Panther Fusion, Simplexa, Cobas(R), RT-LAMP and Xpert Xpress) bear higher sensitivity and specificity, and might be efficient methods complement to the gold standard.
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PURPOSE: Chronic cough in allergic rhinitis (AR) patients is common with multiple etiologies including cough variant asthma (CVA), non-asthmatic eosinophilic bronchitis (NAEB), gastroesophageal reflux-related cough (GERC), and upper airway cough syndrome (UACS). Practical indicators that distinguish these categories are lacking. We aimed to explore the diagnostic value of the fraction of exhaled nitric oxide (FeNO) and forced expiratory flow at 25% and 75% of pulmonary volume (FEF(25–75)) in specifically identifying CVA and NAEB in these patients. METHODS: Consecutive AR patients with chronic cough were screened and underwent induced sputum, FeNO, nasal nitric oxide, spirometry, and methacholine bronchial provocation testing. All patients also completed gastroesophageal reflux disease questionnaires. RESULTS: Among 1,680 AR patients, 324 (19.3%) were identified with chronic cough, of whom 316 (97.5%) underwent etiology analyses. Overall, 87 (27.5%) patients had chronic cough caused by NAEB, 78 (24.7%) by CVA, 16 (5.1%) by GERC, and 81 (25.6%) by UACS. Patients with either NAEB or CVA (n = 165, in total) were further assigned to a common group designated as CVA/NAEB, because they both responded to corticosteroid therapy. Receiver operating characteristic curves of FeNO revealed obvious differences among CVA, NAEB, and CVA/NAEB (area under the curve = 0.855, 0.699, and 0.923, respectively). The cutoff values of FeNO at 43.5 and 32.5 ppb were shown to best differentiate CVA and CVA/NAEB, respectively. FEF(25–75) was significantly lower in patients with CVA than in those with other causes. A FEF(25–75) value of 74.6% showed good sensitivity and specificity for identifying patients with CVA. CONCLUSIONS: NAEB, CVA, and UACS are common causes of chronic cough in patients with AR. FeNO can first be used to discriminate patients with CVA/NAEB, then FEF(25–75) (or combined with FeNO) can further discriminate patients with CVA from those with CVA/NAEB.
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Humanos , Asma , Pruebas de Provocación Bronquial , Bronquitis , Tos , Eosinófilos , Reflujo Gastroesofágico , Cloruro de Metacolina , Óxido Nítrico , Rinitis Alérgica , Curva ROC , Sensibilidad y Especificidad , Espirometría , EsputoRESUMEN
Aim To establish a mouse model of AIDS and observe the effects of Combivir,an anti-AIDS drug.Methods The model was set up by infecting infant mice with L6565 murine leukemia virus.Quantitative RT-PCR method was used to measure viral load in plasma.The percentage of CD4~+ and CD8~+ lymphacytes was estimated by flowcytometric method.The number of WBC,RBC and marrow karyote was counted.indexes of immune organs were weighed and calculated.Results Compared with control,the viral load in plasma in the model was at high level.The percentage of CD4~+ lymphocyte,the ratio of CD4~+/CD8~+,the number of RBC and marrow karyote,and thymus index were significantly decreased in the model,while the number of WBC and spleen index was remarkably increased.Conclusion AIDS patient symptom is shown in mouse infected with L6565 murine leukemia virus.This suggests that this model is likely to be used as a mice model to evaluate the effect of anti-AIDS drugs.