Objective To explore the relationship between uric acid (UA) level and cardiovascular disease in patients with OSAHS and its clinical significance. Methods The electronic medical record system of the First hospital of Lanzhou University was used to collect 475 subjects who completed polysomnography (PSG) during hospitalization from January 2019 to May 2020. According to the Guidelines for the Diagnosis and Treatment of Obstructive Sleep Apnea Hypopnea Syndrome (Basic Version), the patients were divided into four group: control group [apnea-hypopnea index (AHI) <5 times/h, n=96], mild group (5≤AHI≤15 times/h, n=130), moderate group (15<AHI≤30 times/h, n=112), and severe group (AHI>30 times/h, n=137). The age, gender, body mass index (BMI), smoking history, drinking history, hypertension, diabetes mellitus, cardiovascular disease and biochemical indexes [including triglyceride, total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, glucose, UA, blood urea nitrogen (BUN), serum creatinine, lactate dehydrogenase, homocysteine], PSG indexes were observed and compared among the four groups, and the differences were compared by appropriate statistical methods. Binary logistic regression model was used to evaluate the correlation between various risk factors and cardiovascular disease. Results There were statistically significant differences in age, gender, BMI, drinking history, hypertension and cardiovascular disease among the 4 groups (P<0.05). The levels of UA and BUN in mild, moderate and severe groups were higher than those in the control group, with statistical significance (P<0.05). With the increasing of OSAHS severity, the level of UA increased. There was statistical significance in the incidence of cardiovascular disease among the four groups (P<0.05), and the highest incidence of arrhythmia was found among the four groups. And the incidence of cardiovascular disease increases with the increasing of OSAHS severity. Binary Logistic regression analysis showed that the risk factors for cardiovascular disease in OSAHS patients were age, UA and BUN (P<0.05). Conclusions The occurrence of cardiovascular disease in OSAHS patients is positively correlated with the severity of OSAHS. The level of UA can be used as an independent risk factor for cardiovascular disease in OSAHS patients. Therefore, reducing the level of UA may have positive significance for the prevention and control of the prevalence and mortality of cardiovascular disease in OSAHS patients.
ObjectiveTo systematically review the efficacy and safety of simvastatin and its different doses in the adjunct therapy of chronic obstructive pulmonary disease (COPD).MethodsPubMed, EMbase, Web of Science, The Cochrane Library, CNKI, WanFang Data, CBM and VIP databases were electronically searched to collect randomized controlled trials (RCTs) on adjunct therapy of simvastatin in patients with COPD from inception to May 15th, 2020. Two reviewers independently screened literature, extracted data and assessed risk bias of included studies; then, meta-analysis was performed by using Stata 14.0 software.ResultsA total of 22 RCTs involving 2 377 patients were included. The results of meta-analysis showed that treatment with 20 mg simvastatin could improve FEV1%pred, FEV1/FVC, and reduce inflammatory indexes such as CRP, hs-CRP, IL-8 and TNF-α, while 40 mg failed to improve. Simvastatin could reduce COPD score (CAT), but failed to increase the 6-minute walking distance or alleviate acute exacerbation.ConclusionsCurrent evidence shows that treatment with 20 mg simvastatin can improve pulmonary function, reduce inflammatory index and optimize CAT score in COPD patients, but it cannot increase the 6-minute walking distance and reduce the number of acute exacerbations of COPD. Due to the limited quantity and quality of included studies, the above conclusions are needed to be verified by more high-quality studies.
Objective To investigate the predictive value of the systemic coagulation-inflammation index (SCI) for in-hospital adverse outcomes in patients hospitalized with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods This single-center retrospective study included 325 AECOPD patients hospitalized in the Department of Respiratory and Critical Care Medicine at the First Hospital of Lanzhou University from June 1, 2024, to June 1, 2025. Based on the occurrence of composite adverse outcomes during hospitalization, the patients were divided into an adverse outcome group (n=48) and a non-adverse outcome group (n=277). Baseline data and laboratory indicators within 48 hours of admission were collected, and SCI was calculated. Baseline characteristics were compared between the two groups. The diagnostic performance of SCI and commonly used hematological indicators for in-hospital adverse outcomes in the AECOPD patients was evaluated using receiver operating characteristic (ROC) curve analysis. Variables were screened by LASSO regression to develop a baseline predictive model (Model 1) incorporating age, history of acute exacerbation, C-reactive protein, partial pressure of carbon dioxide, and lymphocyte count. An enhanced model (Model 2) was constructed by adding SCI. Model performance was assessed using the area under the ROC curve (AUC), Bootstrap internal validation, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Results The median SCI in the adverse outcome group was 68.24, significantly lower than the 95.74 in the non-adverse outcome group (P<0.001), and low SCI was identified as an independent risk factor for in-hospital adverse outcomes in AECOPD patients. The AUC of Model 1 was 0.893 (95%CI 0.839-0.946). After adding SCI, Model 2 achieved an AUC of 0.909 (95%CI 0.859-0.960, P=0.044). Furthermore, after Bootstrap internal validation, the calibrated AUC of Model 2 was 0.899, confirming good model robustness.The IDI of Model 2 compared to Model 1 was 0.0426 (95%CI 0.0104-0.0790, P=0.012), indicating significant incremental predictive value. DCA demonstrated that using Model 2 for risk prediction provided greater net clinical benefit across a wide range of clinical decision thresholds. Conclusions Low SCI is an independent risk factor for in-hospital adverse outcomes in AECOPD patients. A new predictive model combining traditional clinical indicators with SCI shows favorable diagnostic performance, supporting early identification of high-risk patients in clinical practice.