A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients

Comparison of BODE and ADO Indices in Predicting COPD-Related Medical Costs

Background and Objectives:The ADO (age, dyspnea, and airflow obstruction) and BODE (body mass index, airflow obstruction, dyspnea, and exercise capacity) indices are often used to evaluate the prognoses for chronic obstructive pulmonary disease(COPD); however, an index suitable for predicting medical costs has yet to be developed. Materials and Methods: We investigated the BODE and ADO indices to predict medical costs and compare their predictive power. A total of 396 patients with COPD were retrospectively enrolled. Results: For hospitalization frequencies, BODE was R2 = 0.093 (p < 0.001), and ADO was R2 = 0.065 (p < 0.001); for hospitalization days, BODE was R2 = 0.128 (p < 0.001), and ADO was R2 = 0.071 (p < 0.001); for hospitalization expenses, BODE was R2 = 0.020 (p = 0.047), and ADO was R2 = 0.012 (p = 0.179). BODE and ADO did not differ significantly in the numbers of outpatient visits (BODE, R2 = 0.012, p = 0.179; ADO, R2 = 0.017, p = 0.082); outpatient medical expenses (BODE, R2 = 0.012, p = 0.208; ADO, R2 = 0.008, p = 0.364); and total medical costs (BODE, R2 = 0.018, p = 0.072; ADO, R2 = 0.016, p = 0.098). In conclusion, BODE and ADO indices were correlated with hospitalization frequency and hospitalization days. However, the BODE index exhibits slightly better predictive accuracy than the ADO index in these items.

Development of a model for predicting the severity of chronic obstructive pulmonary disease

Background

Several models have been developed to predict the severity and prognosis of chronic obstructive pulmonary disease (COPD). This study aimed to identify potential predictors and construct a prediction model for COPD severity using biochemical and immunological parameters.

Methods

A total of 6,274 patients with COPD were recruited between July 2010 and July 2018. COPD severity was classified into mild, moderate, severe, and very severe based on the Global Initiative for Chronic Obstructive Lung Disease guidelines. A multivariate logistic regression model was constructed to identify predictors of COPD severity. The predictive ability of the model was assessed by measuring sensitivity, specificity, accuracy, and concordance.

Results

Of 6,274 COPD patients, 2,644, 2,600, and 1,030 had mild/moderate, severe, and very severe disease, respectively. The factors that could distinguish between mild/moderate and severe cases were vascular disorders (OR: 1.44; P < 0.001), high-density lipoprotein (HDL) (OR: 1.83; P < 0.001), plasma fibrinogen (OR: 1.08; P = 0.002), fructosamine (OR: 1.12; P = 0.002), standard bicarbonate concentration (OR: 1.09; P < 0.001), partial pressure of carbon dioxide (OR: 1.09; P < 0.001), age (OR: 0.97; P < 0.001), eosinophil count (OR: 0.66; P = 0.042), lymphocyte ratio (OR: 0.97; P < 0.001), and apolipoprotein A1 (OR: 0.56; P = 0.003). The factors that could distinguish between mild/moderate and very severe cases were vascular disorders (OR: 1.59; P < 0.001), HDL (OR: 2.54; P < 0.001), plasma fibrinogen (OR: 1.10; P = 0.012), fructosamine (OR: 1.18; P = 0.001), partial pressure of oxygen (OR: 1.00; P = 0.007), plasma carbon dioxide concentration (OR: 1.01; P < 0.001), standard bicarbonate concentration (OR: 1.13; P < 0.001), partial pressure of carbon dioxide (OR: 1.16; P < 0.001), age (OR: 0.91; P < 0.001), sex (OR: 0.71; P = 0.010), allergic diseases (OR: 0.51; P = 0.009), eosinophil count (OR: 0.42; P = 0.014), lymphocyte ratio (OR: 0.93; P < 0.001), and apolipoprotein A1 (OR: 0.45; P = 0.005). The prediction model correctly predicted disease severity in 60.17% of patients, and kappa coefficient was 0.35 (95% CI: 0.33-0.37).

Conclusion

This study developed a prediction model for COPD severity based on biochemical and immunological parameters, which should be validated in additional cohorts.

Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on health care. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically provide rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested before for asthma outcome prediction but not for COPD outcome prediction.

Objective

This study aims to assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations.

Methods

The patient cohort included all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019. In a secondary analysis of 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model’s predictions and suggest tailored interventions.

Results

Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months and the predictions for 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months.

Conclusions

Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope to use it to facilitate future clinical use of our model.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes.

Objective

The aim of this study is to develop a more accurate model to predict severe COPD exacerbations.

Methods

We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD.

Results

The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347).

Conclusions

Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

CONQUEST Quality Standards: For the Collaboration on Quality Improvement Initiative for Achieving Excellence in Standards of COPD Care

Background

Chronic obstructive pulmonary disease (COPD) are managed predominantly in primary care. However, key opportunities to optimize treatment are often not realized due to unrecognized disease and delayed implementation of appropriate interventions for both diagnosed and undiagnosed individuals. The COllaboratioN on QUality improvement initiative for achieving Excellence in STandards of COPD care (CONQUEST) is the first-of-its-kind, collaborative, interventional COPD registry. It comprises an integrated quality improvement program focusing on patients (diagnosed and undiagnosed) at a modifiable and higher risk of COPD exacerbations. The first step in CONQUEST was the development of quality standards (QS). The QS will be imbedded in routine primary and secondary care, and are designed to drive patient-centered, targeted, risk-based assessment and management optimization. Our aim is to provide an overview of the CONQUEST QS, including how they were developed, as well as the rationale for, and evidence to support, their inclusion in healthcare systems.

Methods

The QS were developed (between November 2019 and December 2020) by the CONQUEST Global Steering Committee, including 11 internationally recognized experts with a specialty and research focus in COPD. The process included an extensive literature review, generation of QS draft wording, three iterative rounds of review, and consensus.

Results

Four QS were developed: 1) identification of COPD target population, 2) assessment of disease and quantification of future risk, 3) non-pharmacological and pharmacological intervention, and 4) appropriate follow-up. Each QS is followed by a rationale statement and a summary of current guidelines and research evidence relating to the standard and its components.

Conclusion

The CONQUEST QS represent an important step in our aim to improve care for patients with COPD in primary and secondary care. They will help to transform the patient journey, by encouraging early intervention to identify, assess, optimally manage and followup COPD patients with modifiable high risk of future exacerbations.

Stem cell therapy for chronic obstructive pulmonary disease

ABSTRACT

Chronic obstructive pulmonary disease (COPD), characterized by persistent and not fully reversible airflow restrictions, is currently one of the most widespread chronic lung diseases in the world. The most common symptoms of COPD are cough, expectoration, and exertional dyspnea. Although various strategies have been developed during the last few decades, current medical treatment for COPD only focuses on the relief of symptoms, and the reversal of lung function deterioration and improvement in patient's quality of life are very limited. Consequently, development of novel effective therapeutic strategies for COPD is urgently needed. Stem cells were known to differentiate into a variety of cell types and used to regenerate lung parenchyma and airway structures. Stem cell therapy is a promising therapeutic strategy that has the potential to restore the lung function and improve the quality of life in patients with COPD. This review summarizes the current state of knowledge regarding the clinical research on the treatment of COPD with mesenchymal stem cells (MSCs) and aims to update the understanding of the role of MSCs in COPD treatment, which may be helpful for developing effective therapeutic strategies in clinical settings.

Common Mental Disorders, Functional Limitation and Diet Quality Trends and Related Factors among COPD Patients in Spain, 2006-2017: Evidence from Spanish National Health Surveys

Certain conditions such as common mental disorders (CMDs), functional limitation (FL) and poor diet quality may affect the lives of individuals who suffer from chronic obstructive pulmonary disease (COPD). This study sought to examine time trends in the prevalence of CMDs, FL and diet quality among male and female COPD patients living in Spain from 2006 to 2017 and to identify which factors were related to CMDs, FL and a poor/improvable diet quality in these patients. We performed a cross-sectional study among COPD patients aged ≥ 40 years old using data from the Spanish National Health Surveys conducted in 2006, 2011 and 2017, identifying a total of 2572 COPD patients. Binary logistic regressions were performed to determine the characteristics related to CMDs, FL and poor/improvable diet quality. Over the years of the study, the prevalence of FL among female COPD patients increased (p for trend <0.001). In addition, CMDs were associated to body mass index (BMI), educational level, physical activity, smoking status, occupation, chronic conditions and alcohol consumption; FL was related to age, living with a partner, educational level, physical activity and chronic conditions; and poor/improvable diet quality was associated to age, smoking status, BMI and physical activity.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Chrysophanol protects human bronchial epithelial cells from cigarette smoke extract (CSE)-induced apoptosis

OBJECTIVE

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease characterized by the persistent airflow obstruction. Chrysophanol, an anthraquinone derivative isolated from the rhizomes of Rheum palmatum, has been reported to be protective for some inflammatory diseases. The present report aimed to dissect its effect on cigarette smoke extract (CSE)-induced apoptosis in 16HBECs, a human bronchial epithelial cell line.

METHODS

CCK8 cell viability assay was conducted to evaluate the protective effect of chrysophanol on 16HBECs after CSE induction. Western blot analysis, Annexin V/PI staining and TUNEL assay were conducted to test the effect of chrysophanol on 16HBECs apoptosis induced by CSE. Then the western blot assay measured associated molecular pathways to dissect the mechanisms underlying protective effect of chrysophanol on 16HBECs.

RESULTS

Chrysophanol protects 16HBECs against CSE-induced apoptosis in a dose dependent manner. Specifically, pre-treatment of 16HBECs with 20 mmol/l of chrysophanol, reduced CSE-induced apoptosis by almost 10%. Mechanistically, chrysophanol manifested high potency to attenuate CSE-induced expression of apoptotic markers, Bax and cleaved caspase 3. In particular, chrysophanol not only represses CSE-induced oxidative stress by inhibiting CYP1A1 expression, but also suppresses CSE-induced ER stress by inhibiting pPERK, ATF4 and ATF6 expression.

CONCLUSION

Chrysophanol showed protective effect on CSE-induced epithelial injuries in cell line 16HBECs. And our data support that chrysophanol could be employed to reduce the toxicity of cigarette smoke in bronchial epithelial cells, which may have the potential to decrease the risk for developing COPD in smoking subjects.

Scutellarein inhibits BLM-mediated pulmonary fibrosis by affecting fibroblast differentiation, proliferation, and apoptosis

Introduction

Idiopathic pulmonary fibrosis (IPF) is a progressive and irreversible interstitial pulmonary disease that has a poor prognosis. Scutellarein, which is extracted from the traditional Chinese medicine Erigeron breviscapus, is used to treat a variety of diseases; however, the use of scutellarein for the treatment of pulmonary fibrosis and the related mechanisms of action have not been fully explored.

Methods

This study was conducted using a well-established mouse model of pulmonary fibrosis induced by bleomycin (BLM). The antifibrotic effects of scutellarein on histopathologic manifestations and fibrotic marker expression levels were examined. The effects of scutellarein on fibroblast differentiation, proliferation, and apoptosis and on related signaling pathways were next investigated to demonstrate the underlying mechanisms.

Results

In the present study, we found that scutellarein alleviated BLM-induced pulmonary fibrosis, as indicated by histopathologic manifestations and the expression levels of fibrotic markers. Further data demonstrated that the ability of fibroblasts to differentiate into myofibroblasts was attenuated in scutellarein-treated mice model. In addition, we obtained in vitro evidence that scutellarein inhibited fibroblast-to-myofibroblast differentiation by repressing TGF-β/Smad signaling, inhibited cellular proliferation by repressing PI3K/Akt signaling, and increased apoptosis of fibroblasts by affecting Bax/Bcl2 signaling.

Discussion

In general, scutellarein might exert therapeutic effects on pulmonary fibrosis by altering the differentiation, proliferation, and apoptosis of fibroblasts. Although scutellarein has been demonstrated to be safe in mice, further studies are required to investigate the efficacy of scutellarein in patients with IPF.