Validation of the GOLD 2013 classification in predicting exacerbations and mortality in Taiwanese patients with chronic obstructive pulmonary disease

Serum interleukin-6 in chronic obstructive pulmonary disease patients and its relation to severity and acute exacerbation

Background and objectives

The role of interleukins in the severity and clinical profile of chronic obstructive pulmonary disease (COPD) is not known, but evidence supports the contribution of systemic inflammation to disease pathophysiology. This study evaluated the relationship of serum interleukin-6 (IL-6) to the severity and clinical parameters of COPD.

Aim of work

The aim of the work is to estimate the level of IL-6 in COPD patients and its relation to COPD severity and acute exacerbation.

Patients and method

We analyzed 45 COPD patients and 45 normal population as control. We estimate the IL-6 level by ELISA and correlate it with the severity and frequency of COPD exacerbation.

Results

In the current study, we noticed that IL-6 level was high in COPD patients and in those who experience frequent exacerbation. Also, IL-6 show a relation with the parameter of pulmonary function test; there is a statistically significant negative correlation with p-value < 0.05 between the level of IL-6 and the forced expired volume in 1 s/forced vital capacity (EFV1/FVC) among cases with COPD, which indicated that decrease in EFV/FVC will associate with the increase in IL-6 level.

Conclusions

The study revealed that serum IL-6 level elevated with increasing severity of airflow limitation in COPD patients, particularly in acute exacerbation phase. This increase was associated with a reduced quality of life and increased severity of hypoxemia.

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.

GOLD Classifications, COPD Hospitalization, and All-Cause Mortality in Chronic Obstructive Pulmonary Disease: The HUNT Study

Purpose

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) has published three classifications of COPD from 2007 to 2017. No studies have investigated the ability of these classifications to predict COPD-related hospitalizations. We aimed to compare the discrimination ability of the GOLD 2007, 2011, and 2017 classifications to predict COPD hospitalization and all-cause mortality.

Patients and Methods

We followed 1300 participants with COPD aged ≥40 years who participated in the HUNT Study (1995-1997) through to December 31, 2015. Survival analysis and time-dependent area under receiver operating characteristics curves (AUC) were used to compare the discrimination abilities of the GOLD classifications.

Results

Of the 1300 participants, 522 were hospitalized due to COPD and 896 died over 20.4 years of follow-up. In adjusted models, worsening GOLD 2007, GOLD 2011, or GOLD 2017 categories were associated with higher hazards for COPD hospitalization and all-cause mortality, except for the GOLD 2017 classification and all-cause mortality (ptrend=0.114). In crude models, the AUCs (95% CI) for the GOLD 2007, GOLD 2011, and GOLD 2017 for COPD hospitalization were 63.1 (58.7-66.9), 60.9 (56.1-64.4), and 56.1 (54.0-58.1), respectively, at 20-years' follow-up. Corresponding estimates for all-cause mortality were 57.0 (54.8-59.1), 54.1 (52.1-56.0), and 52.6 (51.0-54.3). The differences in AUCs between the GOLD classifications to predict COPD hospitalization and all-cause mortality were constant over the follow-up time.

Conclusion

The GOLD 2007 classification was better than the GOLD 2011 and 2017 classifications at predicting COPD hospitalization and all-cause mortality.

Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis

Preventing exacerbation in chronic obstructive pulmonary disease (COPD) patients is crucial, but requires identification of the exacerbating factors. To date, no integrated analysis of patient-derived and external factors has been reported. To identify factors associated with COPD exacerbation, we collected data, including smoking status, lung function, and COPD assessment test scores, from 594 COPD patients in the Korean COPD subgroup study (KOCOSS), and merged these data with patients’ Korean Health Insurance Review and Assessment Service data for 2007–2012. We also collected primary weather variables, including levels of particulate matter <10 microns in diameter, daily minimum ambient temperature, as well as respiratory virus activities, and the logs of web queries on COPD-related issues. We then assessed the associations between these patient-derived and external factors and COPD exacerbations. Univariate analysis showed that patient factors, air pollution, various types of viruses, temperature, and the number of COPD-related web queries were associated with COPD exacerbation. Multivariate analysis revealed that the number of exacerbations in the preceding year, female sex, COPD grade, and influenza virus detection rate, and lowest temperature showed significant association with exacerbation. Our findings may help COPD patients predict when exacerbations are likely, and provide intervention as early as possible.

Associations between statins and coronary artery disease and stroke risks in patients with asthma-chronic obstructive pulmonary disease overlap syndrome: A time-dependent regression study

BACKGOUND AND AIMS

We aimed at determining the effects of statin use on coronary artery disease (CAD) and stroke risks in patients with asthma-chronic obstructive pulmonary disease overlap syndrome (ACOS).

METHODS

We retrospectively enrolled patients with ACOS treated with (N = 916) and without (N = 6338) statins. The cumulative incidence of CAD and stroke (ischemic and hemorrhagic) was analyzed through time-dependent Cox proportional regression. After adjustment for sex, age, comorbidities, inhaled corticosteroid steroid (ICS) use, and oral steroid (OS) use, we calculated the adjusted hazard ratios (aHRs) and their 95% confidence intervals (CIs) for CAD or stroke in the statin users (long-term [>600 days] and short-term [≤600 days]) compared with the non-users.

RESULTS

Among the statin users, aHRs (95% CIs) for CAD and stroke were 0.50 (0.41-0.62) and 0.83 (0.63-1.09), respectively; moreover, aHRs were 0.30 (0.09-0.99) and 0.90 (0.68-1.20) for ischemic and hemorrhagic stroke, respectively. aHRs (95% CIs) for CAD and stroke were 0.58 (0.47-0.71) and 0.93 (0.70-1.23), respectively, in the short-term users and 0.23 (0.13-0.41) and 0.42 (0.19-0.89), respectively, in the long-term users.

CONCLUSIONS

CAD risk was lower in all statin users, regardless of the duration of use, whereas ischemic stroke risk was lower only in the long-term statin users. No association was observed between hemorrhagic stroke risk and statin use.

Validation of the GOLD 2017 and new 16 subgroups (1A-4D) classifications in predicting exacerbation and mortality in COPD patients

BACKGROUND AND OBJECTIVE

A multidimensional assessment of COPD was recommended by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) in 2013 and revised in 2017. We examined the ability of the GOLD 2017 and the new 16 subgroup (1A-4D) classifications to predict clinical outcomes, including exacerbation and mortality, and compared them with the GOLD 2013 classifications.

METHODS

Patients with COPD were recruited from January 2006 to December 2017. The predictive abilities of grades 1-4 and groups A-D were examined through a logistic regression analysis with receiver operating curve estimations and area under the curve (AUC).

RESULTS

A total of 553 subjects with COPD were analyzed. The mortality rate was 48.6% during a median follow-up period of 5.2 years. Both the GOLD 2017 and the 2013 group A-D classifications had good predictive ability for total and severe exacerbations, for which the AUCs were 0.79 vs 0.77 and 0.79 vs 0.78, respectively. The AUCs for the GOLD 2017 groups A-D, grades 1-4, and the GOLD 2013 group A-D classifications were 0.70, 0.66, and 0.70 for all-cause mortality and 0.73, 0.71, and 0.74 for respiratory cause mortality, respectively. Combining the spirometric staging with the grouping for the GOLD 2017 subgroups (1A-4D), the all-cause mortality rate for group B and D patients was significantly increased from subgroups 1B-4B (27.7%, 50.6%, 53.3%, and 69.2%, respectively) and groups 1D-4D (55.0%, 68.8%, 82.1%, and 90.5%, respectively). The AUCs of subgroups (1A-4D) were 0.73 and 0.77 for all-cause and respiratory mortality, respectively; the new classification was determined more accurate than the GOLD 2017 for predicting mortality (P<0.0001).

CONCLUSION

The GOLD 2017 classification performed well by identifying individuals at risk of exacerbation, but its predictive ability for mortality was poor among COPD patients. Combining the spirometric staging with the grouping increased the predictive ability for all-cause and respiratory mortality.

SUMMARY AT A GLANCE

We validate the ability of the GOLD 2017 and 16 subgroup (1A-4D) classifications to predict clinical outcome for COPD patients. The GOLD 2017 classification performed well by identifying individuals at risk of exacerbation, but its predictive ability for mortality was poor. The new 16 subgroup (1A-4D) classification combining the spirometric 1-4 staging and the A-D grouping increased the predictive ability for mortality and was better than the GOLD 2017 for predicting all-cause and respiratory mortality among COPD patients.