Background: Chronic obstructive pulmonary disease (COPD) was a significant public health concern, with smoking being the primary risk factor for its development and progression. The impact of smoking on respiratory rehabilitation efficacy in COPD patients remains an area of interest and investigation. This study aimed to assess the influence of smoking on the efficacy of respiratory rehabilitation in patients with COPD.

Methods: Data of patients with COPD from October 2015 to October 2023 were retrospectively analyzed in this case-control study. The patients who had previously participated in a pulmonary rehabilitation program were excluded. Pulmonary function, exercise capacity, quality of life, and sleep patterns were evaluated before and after rehabilitation.

Results: A total of 40 patients were included and categorized into non-smoking (n=20) and smoking groups (n=20) based on their smoking history. Before rehabilitation, no significant differences were observed between the groups in forced expiratory volume in one second (FEV₁) (P=0.96), forced vital capacity (FVC) (P=0.97), FEV₁/FVC ratio (P=0.73), maximal voluntary ventilation (MVV) (P=0.69), and diffusing capacity of the lung for carbon monoxide (DLCO) (P=0.63). After rehabilitation, FEV₁ (P=0.02), FVC (P=0.009), FEV₁/FVC ratio (P=0.03), MVV (P=0.004), DLCO (P=0.01), these pulmonary functions for non-smokers were much better than the smokers. Similarly, the non-smoking group exhibited significantly greater improvements in 6-minute walk distance (P=0.03), peak oxygen consumption (VO₂) (P=0.01), Borg scale ratings (P=0.02), St. George’s Respiratory Questionnaire (SGRQ) scores (P=0.004), and Medical Research Council (MRC) dyspnea scale scores (P=0.005) compared to the smoking group after rehabilitation. The non-smoking patients have more better quality of life compared to the smokers after rehabilitation, which demonstrated by the quality of life scores and Sleep Quality Score, including somatization (P=0.01), emotion management (P=0.009), role play (P=0.008), cognitive function (P=0.04), return to social function (P=0.01), Sleep Quality Score (P=0.02).

Conclusions: Smoking negatively impacts the efficacy of respiratory rehabilitation in COPD patients, leading to poorer pulmonary function, exercise capacity, quality of life, and sleep patterns.

Background: Over recent decades, findings on the potential correlation between type II diabetes mellitus (T2DM) and the risk of esophageal cancer (EC) have displayed considerable heterogeneity. Furthermore, metformin has emerged as a potentially protective agent against certain site-specific malignancies. This study aims to explore the causal relationship between T2DM, medication treatments (metformin, insulin, gliclazide), and EC risk while addressing the notable variability in previous research findings.

Methods: To elucidate the causal associations between T2DM, medication treatments, and EC, we employed a synergistic methodology that integrates the two-sample Mendelian randomization (MR) approach with meta-analysis. The genome-wide association studies (GWAS) pertaining to each exposure and EC were acquired from a publicly accessible database.

Results: For MR analyses, three out of seven GWAS datasets within the T2DM cohort exhibited statistical significance. Conversely, all MR analyses yielded non-significant results in the medication cohort. Meta-analyses suggested that a genetic predisposition to T2DM correlated with a reduced risk of EC [odds ratio (OR), 0.999612; 95% confidence interval (CI): 0.999468–0.999756; P=0.01; I²=0%]. Moreover, metformin intake was causally linked to a decreased prevalence of EC (OR, 0.988954; 95% CI: 0.979044–0.998963; P=0.03; I²=0%), whereas neither insulin nor gliclazide manifests statistical significance.

Conclusions: Our findings indicate T2DM and metformin are causally associated with diminished risk of EC, while no causal associations exist between insulin, gliclazide, and EC.

Background: Postoperative coagulation dysfunction is one of the common complications after coronary artery bypass grafting (CABG), especially in elderly patients. The aim of this study was to establish a risk prediction model for coagulation disorders in elderly patients after CABG, effectively identify high-risk patients who are prone to coagulation disorders, and strengthen postoperative treatment monitoring for these patients.

Methods: Patients who underwent CABG were retrospectively included between February 2019 and December 2020, and were randomly divided into a derivation set and a validation set at a ratio of 7:3. The disseminated intravascular coagulation (DIC) score of ≥2 was defined as coagulation disorder. The least absolute shrinkage and selection operator (LASSO) regression was used for variable selection and the establishment of a regression model. The confusion matrix and receiver operating characteristic (ROC) curve were used to evaluate the model prediction effect.

Results: The risk factors associated with postoperative coagulation dysfunction, selected by LASSO regression, including patient weight, preoperative baseline estimated glomerular filtration rate (eGFR), B-type natriuretic peptide (BNP), platelet count (PLT), preoperative use of heparin and angiotensin receptor-neprilysin inhibitor (ARNI), as well as intraoperative utilization of epinephrine, norepinephrine, dopamine, cephalosporins, cardiopulmonary bypass (CPB), intra-aortic balloon pump (IABP), extracorporeal membrane oxygenation (ECMO), operation duration, and total intraoperative fluid input. The area under curve (AUC) of the derivation set was 0.818 [95% confidence interval (CI): 0.775−0.862], while the AUC of the validation set was 0.827 (95% CI: 0.755−0.898). The sensitivity and specificity of the model in the derivation set were 80.0% and 70.0%. In the validation set, the sensitivity was 76.6% and the specificity was 81.7%, indicating that the model has good predictive performance.

Conclusions: The LASSO regression model for predicting coagulation disorders after CABG showed a good predictive performance in both the derivation set and the validation set, which is helpful for early identification of high-risk patients with coagulation disorders after CABG.