Characterization Associated with the Frequent Severe Exacerbator Phenotype in COPD Patients

A predictive model for frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease

Background

The frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is characterized by experiencing at least two exacerbations per year, leading to a significant economic burden on healthcare systems worldwide. Although several biomarkers have been shown to be effective in assessing AECOPD severity in recent years, there is a lack of studies on markers to predict the frequent exacerbator phenotype of AECOPD. The current study aimed to develop a new predictive model for the frequent exacerbator phenotype of AECOPD based on rapid, inexpensive, and easily obtained routine markers.

Methods

This was a single-center, retrospective study that enrolled a total of 2,236 AECOPD patients. The participants were divided into two groups based on the frequency of exacerbations: infrequent group (n=1,827) and frequent group (n=409). They underwent a complete blood count, as well as blood biochemistry, blood lipid and coagulation testing, and general characteristics were also recorded. Univariate analysis and binary multivariate logistic regression analyses were used to explore independent risk factors for the frequent exacerbator phenotype of AECOPD, which could be used as components of a new predictive model. The receiver operator characteristic (ROC) curve was used to assess the predictive value of the new model, which consisted of all significant risk factors predicting the primary outcome. The nomogram risk prediction model was established using R software.

Results

Age, gender, length of stay (LOS), neutrophils, monocytes, eosinophils, direct bilirubin (DBil), gamma-glutamyl transferase (GGT), and the glucose-to-lymphocyte ratio (GLR) were independent risk factors for the frequent exacerbator phenotype of AECOPD. The area under the curve (AUC) of the new predictive model was 0.681 [95% confidence interval (CI): 0.653-0.708], and the sensitivity was 63.6% (95% CI: 58.9-68.2%) and the specificity was 65.0% (95% CI: 60.3-69.6%).

Conclusions

A new predictive model based on demographic characteristics and blood parameters can be used to predict the frequency of acute exacerbations in the management of chronic obstructive pulmonary disease (COPD).

Effect of Broncho-Vaxom (OM-85) on the frequency of chronic obstructive pulmonary disease (COPD) exacerbations

BACKGROUND

Efforts have been made to reduce the risk of chronic obstructive pulmonary disease (COPD) exacerbations using a variety of measures. Broncho-Vaxom (BV) is an immunomodulating agent that has shown potential benefit by balancing between immune stimulation and regulation in patients with COPD. In this study, we evaluated the clinical efficacy of BV for reducing the risk of COPD exacerbations.

METHODS

This study was based on the Korean National Health Insurance database, which contains reimbursement information for almost the entire population of South Korea. We extracted data from 2016 to 2019 for patients started on BV during 2017-2018. We collected baseline data on demographics, comorbidities, inhaler use, hospital type, and insurance type 1 year before starting BV. We also analyzed exacerbation history, starting from the year before BV initiation.

RESULTS

In total, 238 patients were enrolled in this study. Their mean age was 69.2 ± 9.14 years, 79.8% were male, and 45% experienced at least one exacerbation. BV reduced the risk of moderate (odds ratio [OR] = 0.59, 95% confidence interval [CI]: 0.38-0.91) and moderate-to-severe exacerbations compared to pre- and post-BV (OR = 0.571, 95% CI: 0.37-0.89). BV use also reduced the incidence of moderate and moderate-to-severe exacerbations (incidence rate ratio [IRR] = 0.75, p = 0.03; and IRR = 0.77, p = 0.03, respectively). The use of BV was significantly delayed moderate exacerbations (hazard ratio = 0.68, p = 0.02), but not with moderate-to-severe or severe exacerbations.

CONCLUSION

The use of BV was associated with fewer moderate and moderate-to-severe exacerbations. Additionally, BV was associated with a delay in moderate COPD exacerbations.

The Clinical Characteristics and Outcomes in Non-Frequent Exacerbation Patients with Chronic Obstructive Pulmonary Disease in the Chinese Population

Background

We analyzed the clinical characteristics and outcomes in non-frequent exacerbation patients with chronic obstructive pulmonary disease (COPD).

Methods

In this retrospective cohort study, we enrolled patients with stable COPD from 12 hospitals. Non-frequent exacerbation was defined as less than two times of exacerbations in the past year. The non-frequent exacerbation patients were classified into less and more symptomatic groups based on the COPD Assessment Test (CAT) and modified Medical Research Council (mMRC). Finally, the non-frequent exacerbation patients with less and more symptomatic were classified into the long-acting muscarinic antagonist (LAMA), long-acting β2-agonist (LABA)+inhaled corticosteroids (ICS), LABA+LAMA, and LABA+LAMA+ICS groups. Minimum clinically important difference (MCID) was defined as a CAT score decrease of ≥ 2 during six months of follow-up. We recorded the number of exacerbations and mortality during one year of follow-up.

Results

A total of 834 (67.5%) non-frequent exacerbation patients with COPD were included in this study. The non-frequent exacerbation patients had a higher education level and body mass index (BMI), and lower CAT and mMRC scores (P<0.05). In addition, the non-frequent exacerbation patients had lower mortality and risk of future exacerbation, and were more likely to attain MCID (P<0.05). Furthermore, the non-frequent exacerbation patients with more symptomatic COPD treated with LABA+LAMA or LABA+LAMA+ICS were more likely to attain MCID and had a lower risk of future exacerbation (P<0.05). However, there were no significant differences among the different inhalation therapies in non-frequent exacerbation patients with less symptomatic COPD.

Conclusion

The non-frequent exacerbation patients with COPD had a higher education level and BMI, a lower symptom burden, and better outcomes. In addition, LABA+LAMA should be recommended to non-frequent exacerbation patients with more symptomatic COPD, while mono-LAMA should be recommended to non-frequent exacerbation patients with less symptomatic COPD as the initial inhalation therapy.

Plasma Proteomics Study Between the Frequent Exacerbation and Infrequent Exacerbation Phenotypes of Chronic Obstructive Pulmonary Disease

Background

Frequent exacerbation (FE) and infrequent exacerbation (IE) are two phenotypes of chronic obstructive pulmonary disease (COPD), of which FE is associated with a higher incidence of exacerbation and a serious threat to human health. Because the pathogenesis mechanisms of FE are unclear, this study aims to identify FE-related proteins in the plasma via proteomics for use as predictive, diagnostic, and therapeutic biomarkers of COPD.

Methods

A cross-sectional study was conducted in which plasma protein profiles were analyzed in COPD patients at stable stage, and differentially expressed proteins (DEPs) were screened out between the FE and IE patients. FE-related DEPs were identified using data-independent acquisition-based proteomics and bioinformatics analyses. In addition, FE-related candidates were verified by enzyme-linked immunosorbent assay.

Results

In this study, 47 DEPs were screened out between the FE and IE groups, including 20 upregulated and 27 downregulated proteins. Key biological functions (eg, neutrophil degranulation, extracellular exosome, protein homodimerization activity) and signaling pathways (eg, arginine and proline metabolism) were enriched in association with the FE phenotype. Receiver operating characteristic (ROC) analysis of the 11 combined DEPs revealed an area under the curve of 0.985 (p <0.05) for discriminating FE from IE. Moreover, correlation and ROC curve analyses indicated that creatine kinase, M-type (CKM) and fat storage-inducing transmembrane protein 1 (FITM1) might be clinically significant in patients with the FE phenotype. In addition, plasma expression levels of CKM and FITM1 were validated to be significantly decreased in the FE group compared with the IE group (CKM: p <0.01; FITM1: p <0.05).

Conclusion

In this study, novel insights into COPD pathogenesis were provided by investigating and comparing plasma protein profiles between the FE and IE patients. CKM, FITM1, and a combinative biomarker panel may serve as useful tools for assisting in the precision diagnosis and effective treatment of the FE phenotype of COPD.

Identification of frequent acute exacerbations phenotype in COPD patients based on imaging and clinical characteristics

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity, with acute exacerbations manifesting as a worsening of respiratory symptoms. This study aimed to identify the frequent acute exacerbation phenotype in patients with COPD based on imaging and clinical characteristics.

METHODS

Patients with COPD (n = 201) were monitored for acute exacerbations one year after their initial hospital admission and further divided into frequent and non-frequent exacerbation groups according to the frequency and severity of acute exacerbations. All patients underwent high resolution CT scans and low attenuation area less than -950Hu (LAA-950) in the whole lung was measured. Differences in visual subtypes, LAA-950, and clinical basic characteristics were compared between groups. The clinical factors influencing frequent exacerbation were determined using binary logistic regression. Finally, based on imaging and clinical factors, the receiver operating characteristic curve was used to identify the phenotype of COPD with frequent acute exacerbations.

RESULTS

Patients with frequent exacerbations had a larger LAA-950 than those non-frequent exacerbations patients (p＜0.001). Frequent acute exacerbations were associated with worsening visual subtypes. Multivariate binary logistic regression illustrated that age, smoking status, BMI, FEV1 pred, and LAA-950 were associated with frequent exacerbations of COPD. The area under the receiver operating characteristic curve for predicting frequent exacerbations based on age, smoking status, BMI, FEV1 pred, and LAA-950 was 0.907 (p＜0.001).

CONCLUSION

The combination of imaging and clinical characteristics reached high diagnostic efficacy in the identification of frequent acute exacerbations in patients with COPD.

Comparation of predictive value of CAT and change in CAT in the short term for future exacerbation of chronic obstructive pulmonary disease

PURPOSE

Our study aimed to compare the predictive value of the COPD Assessment Test (CAT) score at baseline and short-term change in CAT for future exacerbations in chronic obstructive pulmonary disease (COPD) patients.

METHODS

This was a multicentre prospective study. Patients with COPD were recruited into the study and followed up for one year. CAT score and exacerbation in the previous year were collected at baseline. Change in CAT was defined as CAT score changing between baseline and the 6-month follow-up. Exacerbation was recorded during the one-year follow-up from 0^th to 12^th month.

RESULT

A total of 536 patients were enrolled for final analysis. The mean baseline CAT score was 14.5 ± 6.6 and the median (IQR) change in CAT was -2 (8). On Cox regression analysis, baseline CAT score, change in CAT and history of exacerbation were independent risk factors for exacerbation in the one-year follow-up. Compared with the r value of correlation between baseline CAT score and frequency of exacerbations during the one-year follow-up (r = 0.286, p < .001), that correlation between the change in CAT and frequency of exacerbations during follow-up was higher (r = 0.421, p < .001). The receiver operating characteristic (ROC) curves showed that change in CAT had a better predictive capacity for future exacerbation than baseline CAT (0.789 versus 0.609, p = .001). The ROC showed that change in CAT also had a better predictive capacity for future exacerbation than exacerbation in the previous year (0.789 versus 0.689, p = .011).

CONCLUSION

The correlation between baseline CAT score and future exacerbation was weak, however, the correlation between change in CAT and future exacerbation was moderate. Change in CAT in the short term had a better predictive value for future exacerbations of COPD than baseline CAT and exacerbation in the previous year.

Remote Monitoring for Prediction and Management of Acute Exacerbations in Chronic Obstructive Pulmonary Disease (AECOPD)

The progression of chronic obstructive pulmonary disease (COPD) is characterized by episodes of acute exacerbation (AECOPD) of symptoms, decline in respiratory function, and reduction in quality-of-life increasing morbi-mortality and often requiring hospitalization. Exacerbations can be triggered by environmental exposures, changes in lifestyle, and/or physiological and psychological factors to greater or lesser extents depending on the individual’s COPD phenotype. The prediction and early detection of an exacerbation might allow patients and physicians to better manage the acute phase. We summarize the recent scientific data on remote telemonitoring (TM) for the prediction and management of acute exacerbations in COPD patients. We discuss the components of remote monitoring platforms, including the integration of environmental monitoring data; patient reported outcomes collected via interactive Smartphone apps, with data from wearable devices that monitor physical activity, heart rate, etc.; and data from medical devices such as connected non-invasive ventilators. We consider how telemonitoring and the deluge of data it potentially generates could be combined with electronic health records to provide personalized care and multi-disease management for COPD patients.

Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality and contributes to high morbidity worldwide. Patients with COPD have a higher risk for acute respiratory failure, ventilator dependence, and mortality after hospitalization compared with the general population. Accurate and early risk detection will provide more information for early management and better decision making. This study aimed to build prediction models using patients’ characteristics, laboratory data, and comorbidities for early detection of acute respiratory failure, ventilator dependence, and mortality in patients with COPD after hospitalization. We retrospectively collected the electronic medical records of 5061 patients with COPD in three hospitals of the Chi Mei Medical Group, Taiwan. After data cleaning, we built three prediction models for acute respiratory failure, ventilator dependence, and mortality using seven machine learning algorithms. Based on the AUC value, the best model for mortality was built by the XGBoost algorithm (AUC = 0.817), the best model for acute respiratory failure was built by random forest algorithm (AUC = 0.804), while the best model for ventilator dependence was built by LightGBM algorithm (AUC = 0.809). A web service application was implemented with the best models and integrated into the existing hospital information system for physician’s trials and evaluations. Our machine learning models exhibit excellent predictive quality and can therefore provide physicians with a useful decision-making reference for the adverse prognosis of COPD patients.