Background. Chronic obstructive pulmonary disease (COPD) is a common chronic disease. Progression is further exacerbated by the coexistence of cardiovascular disease (CVD). We aim to construct a diagnostic nomogram for predicting the risk of coexisting CVD and a prognostic nomogram for predicting long-term survival in COPD. Methods. The 540 eligible participants selected from the NHANES 2005–2010 were included in this study. Logistic regression analysis was used to construct a diagnostic nomogram for the diagnosis of coexisting CVD in COPD. Cox regression analyses were used to construct a prognostic nomogram for COPD. A risk stratification system was developed based on the total score generated from the prognostic nomogram. We used C-index and ROC curves to evaluate the discriminant ability of the newly built nomograms. The models were also validated utilizing calibration curves. Survival curves were made using the Kaplan–Meier method and compared by the Log-rank test. Results. Logistic regression analysis showed that gender, age, neutrophil, RDW, LDH, and HbA1c were independent predictors of coexisting CVD and were included in the diagnostic model. Cox regression analysis indicated that CVD, gender, age, BMI, RDW, albumin, LDH, creatinine, and NLR were independent predictors of COPD prognosis and were incorporated into the prognostic model. The C-index and ROC curves revealed the good discrimination abilities of the models. And the calibration curves implied that the predicted values by the nomograms were in good agreement with the actual observed values. In addition, we found that coexisting with CVD had a worse prognosis compared to those without CVD, and the prognosis of the low-risk group was better than that of the high-risk group in COPD. Conclusions. The nomograms we developed can help clinicians and patients to identify COPD coexisting CVD early and predict the 5-year and 10-year survival rates of COPD patients, which has some clinical practical values.

Purpose High levels of red blood cell distribution width (RDW) and hypoalbuminemia are markers of poor prognosis in chronic obstructive pulmonary disease (COPD) patients. However, few studies have shown that the red blood cell distribution width–albumin ratio (RAR) is related to the mortality of COPD. This study aimed to explore the relationship between RAR and hospital mortality in COPD patients admitted to the intensive care unit (ICU). Patients and Methods Patients were retrospectively incorporated from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and divided into two groups by a cutoff value of RAR. Propensity score matching (PSM) was performed to adjust for the imbalance of covariates. Logistic regression models and subgroup analyses were carried out to investigate the relationship between RAR and hospital mortality. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of RAR and decision curve analysis (DCA) to assess the clinical utility. Results In total, 1174 patients were finally identified from the MIMIC-IV database. The cutoff value for RAR was 5.315%/g/dL. After PSM at a 1:1 ratio, 638 patients were included in the matched cohort. In the original and matched cohorts, the high RAR group had higher hospital mortality and longer hospital stays. Logistic regression analysis suggested that RAR was an independent risk factor for hospital mortality. The areas under the ROC curve in the original and matched cohorts were 0.706 and 0.611, respectively, which were larger than applying RDW alone (the original cohort: 0.600, the matched cohort: 0.514). The DCA indicated that RAR had a clinical utility. Conclusion A higher RAR (>5.315%/g/dL) was associated with hospital mortality in COPD patients admitted to ICU. As an easily available peripheral blood marker, RAR can predict hospital mortality in critically ill patients with COPD independently.

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ObjectiveThis study aimed to analyze the correlation between quantitative computed tomography (CT) parameters and airflow obstruction in patients with COPD.MethodsPubMed, Embase, Cochrane and Web of Knowledge were searched by two investigators from inception to July 2022, using a combination of pertinent items to discover articles that investigated the relationship between CT measurements and lung function parameters in patients with COPD. Five reviewers independently extracted data, and evaluated it for quality and bias. The correlation coefficient was calculated, and heterogeneity was explored. The following CT measurements were extracted: percentage of lung attenuation area <−950 Hounsfield Units (HU), mean lung density, percentage of airway wall area, air trapping index, and airway wall thickness. Two airflow obstruction parameters were extracted: forced expiratory volume in the first second as a percentage of prediction (FEV1%pred) and FEV1 divided by forced expiratory volume lung capacity.ResultsA total of 141 studies (25,214 participants) were identified, which 64 (6,341 participants) were suitable for our meta-analysis. Results from our analysis demonstrated that there was a significant correlation between quantitative CT parameters and lung function. The absolute pooled correlation coefficients ranged from 0.26 (95% CI, 0.18 to 0.33) to 0.70 (95% CI, 0.65 to 0.75) for inspiratory CT and 0.56 (95% CI, 0.51 to 0.60) to 0.74 (95% CI, 0.68 to 0.80) for expiratory CT.ConclusionsResults from this analysis demonstrated that quantitative CT parameters are significantly correlated with lung function in patients with COPD. With recent advances in chest CT, we can evaluate morphological features in the lungs that cannot be obtained by other clinical indices, such as pulmonary function tests. Therefore, CT can provide a quantitative method to advance the development and testing of new interventions and therapies for patients with COPD.