Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Frequency. Clinical Parameters Are Still Better Than Biomarkers

Prognostic value of the post-exercise heart rate recovery and BHDE-index in chronic obstructive pulmonary disease

BACKGROUND

The BODE index, consisting of body mass index (B), airflow obstruction (O), dyspnea score (D), and exercise capacity (E), can predict outcomes in COPD. However, when spirometry was restricted to prevent cross-infection such as COVID-19 pandemic, a modified index would be needed. Because cardiovascular dysfunction is associated with poor clinical outcomes in COPD, we conducted a novel BHDE-index by replacing spirometry with post-exercise heart rate recovery (HRR, H) and evaluated its predictive performance in this observational study.

METHODS

From January 2019 to December 2019, enrolled patients were analyzed as a derivation cohort for the setup of the model. This model was verified in another group of patients generated between January 2020 and December 2020, as the validation cohort. The post exercise HRR was defined as the difference of heart rate immediately after and 1 min after test cessation.

RESULTS

A total of 447 patients with COPD were enrolled. Patients with abnormal HRR were older, with more severe airway obstruction, severe airway symptoms, faster resting heart rate, shorter 6-min walk distance and higher frequency of severe acute exacerbation in previous one year. The prediction performance of the BHDE-index for one-year severe COPD exacerbation was similar to that of the BODE-index in both the derivation and validation groups [area under the receiver operating characteristic curve (AUROC) 0.76 vs. 0.75, p = 0.369; AUROC 0.74 vs. 0.79, p = 0.05]. The prediction performance for 1 year mortality was also similar between BHDE-index and BODE-index in both cohorts [AUROC 0.80 vs. 0.77, p = 0.564; 0.76 vs. 0.70, p = 0.234]. Univariate and multivariate analyses also showed that the BHDE-index was an independent and important predictor of annual severe COPD exacerbation in the derivation and validation cohorts.

CONCLUSIONS

The BHDE-index is a good and easy-to-perform prediction model for the risk of severe acute exacerbation and 1-year mortality in COPD wherever spirometry results are unavailable.

Developmental endothelial locus-1 as a potential biomarker for the incidence of acute exacerbation in patients with chronic obstructive pulmonary disease

BACKGROUND

Despite the high disease burden of chronic obstructive pulmonary disease (COPD) and risk of acute COPD exacerbation, few COPD biomarkers are available. As developmental endothelial locus-1 (DEL-1) has been proposed to possess beneficial effects, including anti-inflammatory effects, we hypothesized that DEL-1 could be a blood biomarker for COPD.

OBJECTIVE

To elucidate the role of plasma DEL-1 as a biomarker of COPD in terms of pathogenesis and for predicting acute exacerbation.

METHODS

Cigarette smoke extract (CSE) or saline was intratracheally administered to wild-type (WT) and DEL-1 knockout (KO) C57BL/6 mice. Subsequently, lung sections were obtained to quantify the degree of emphysema using the mean linear intercept (MLI). Additionally, plasma DEL-1 levels were compared between COPD and non-COPD participants recruited in ongoing prospective cohorts. Using negative binomial regression analysis, the association between the plasma DEL-1 level and subsequent acute exacerbation risk was evaluated in patients with COPD.

RESULTS

In the in vivo study, DEL-1 KO induced emphysema (KO saline vs. WT saline; P = 0.003) and augmented CSE-induced emphysema (KO CSE vs. WT CSE; P < 0.001) in 29 mice. Among 537 participants, patients with COPD presented plasma log (DEL-1) levels lower than non-COPD participants (P = 0.04), especially non-COPD never smokers (P = 0.019). During 1.2 ± 0.3 years, patients with COPD in the lowest quartile of Log(DEL-1) demonstrated an increased risk of subsequent acute exacerbation, compared with those in the highest quartile of Log(DEL-1) (adjusted incidence rate ratio, 3.64; 95% confidence interval, 1.03-12.9).

CONCLUSION

Low DEL-1 levels are associated with COPD development and increased risk of subsequent COPD acute exacerbation. DEL-1 can be a useful biomarker in patients with COPD.

Evolving Concepts in Chronic Obstructive Pulmonary Disease Blood-Based Biomarkers

In recent years, there has been a great deal of interest in the identification and validation of blood-based biomarkers for clinical use in chronic obstructive pulmonary disease (COPD). We now have panels of blood biomarkers that potentially hold great promise as they show statistically significant associations with COPD, but biomarkers for the diagnosis of COPD remain elusive. In fact, they are yet to demonstrate sufficient accuracy to be accepted in clinical use, and many are not specific to COPD but more related to inflammation (e.g. interleukin-6) or associated with other chronic diseases such as diabetes (e.g. soluble receptor for advanced glycation endproducts [sRAGE]). Although no single blood-based biomarker has demonstrated clinical utility for either the diagnosis or progression of COPD, it has been suggested that combinations of individual markers may provide important diagnostic or prognostic information; however, the interpretation of COPD biomarker results still requires thought and many questions remain unanswered.

Acute exacerbations of chronic obstructive pulmonary disease: in search of diagnostic biomarkers and treatable traits

Acute exacerbations of chronic obstructive pulmonary disease (COPD) are associated with a significant mortality, health and economic burden. Their diagnosis, assessment and management remain suboptimal and unchanged for decades. Recent clinical and translational studies revealed that the significant heterogeneity in mechanisms and outcomes of exacerbations could be resolved by grouping them etiologically. This is anticipated to lead to a better understanding of the biological processes that underlie each type of exacerbation and to allow the introduction of precision medicine interventions that could improve outcomes. This review summarises novel data on the diagnosis, phenotyping, targeted treatment and prevention of COPD exacerbations.

Biomarkers for chronic obstructive pulmonary disease diagnosis and progression: insights, disappointments and promise

PURPOSE OF REVIEW

This article reviews the status of biomarkers useful in the diagnosis and progression of chronic obstructive pulmonary disease (COPD). Biomarkers have been the focus of a great deal of COPD-related research in recent years, although useful markers in these specific arenas remain elusive.

RECENT FINDINGS

No biomarker other than lung function has been shown to be useful, to date, for the diagnosis of COPD. The best blood-based biomarkers for the progression of COPD may involve combinations of individual markers, such as CC16, fibrinogen and sRAGE. New imaging metrics, such as central airway collapse, pulmonary vascular changes and central airway branch variation, may be able to provide valuable prognostic and information, although these remain confined to research applications.

SUMMARY

Blood-based biomarkers for diagnosing and determining the progression of COPD remain disappointingly elusive. Although there have been some advances in nonblood-based markers, such as those from imaging, exhaled breath or physiologic assessment, these remain limited, for the most part, to research applications. Moving toward better markers that could be used in clinical application in the screening and diagnosis of COPD that could also provide prognostic information remains an important goal of research.

Machine Learning Algorithms Utilizing Functional Respiratory Imaging May Predict COPD Exacerbations

RATIONALE AND OBJECTIVES

Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this disease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression.

MATERIALS AND METHODS

A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data (unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms.

RESULTS

A total of 11 baseline FRI parameters could significantly distinguish ( p < 0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support Vector Machines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline.

CONCLUSION

This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient specific level in contrast to classical clinical parameters.

Predicting Acute Exacerbations in Chronic Obstructive Pulmonary Disease

BACKGROUND

With increasing health care costs that have outpaced those of other industries, payers of health care are moving from a fee-for-service payment model to one in which reimbursement is tied to outcomes. Chronic obstructive pulmonary disease (COPD) is a disease where this payment model has been implemented by some payers, and COPD exacerbations are a quality metric that is used. Under an outcomes-based payment model, it is important for health systems to be able to identify patients at risk for poor outcomes so that they can target interventions to improve outcomes.

OBJECTIVE

To develop and evaluate predictive models that could be used to identify patients at high risk for COPD exacerbations.

METHODS

This study was retrospective and observational and included COPD patients treated with a bronchodilator-based combination therapy. We used health insurance claims data to obtain demographics, enrollment information, comorbidities, medication use, and health care resource utilization for each patient over a 6-month baseline period. Exacerbations were examined over a 6-month outcome period and included inpatient (primary discharge diagnosis for COPD), outpatient, and emergency department (outpatient/emergency department visits with a COPD diagnosis plus an acute prescription for an antibiotic or corticosteroid within 5 days) exacerbations. The cohort was split into training (75%) and validation (25%) sets. Within the training cohort, stepwise logistic regression models were created to evaluate risk of exacerbations based on factors measured during the baseline period. Models were evaluated using sensitivity, specificity, and positive and negative predictive values. The base model included all confounding or effect modifier covariates. Several other models were explored using different sets of observations and variables to determine the best predictive model.

RESULTS

There were 478,772 patients included in the analytic sample, of which 40.5% had exacerbations during the outcome period. Patients with exacerbations had slightly more comorbidities, medication use, and health care resource utilization compared with patients without exacerbations. In the base model, sensitivity was 41.6% and specificity was 85.5%. Positive and negative predictive values were 66.2% and 68.2%, respectively. Other models that were evaluated resulted in similar test characteristics as the base model.

CONCLUSIONS

In this study, we were not able to predict COPD exacerbations with a high level of accuracy using health insurance claims data from COPD patients treated with bronchodilator-based combination therapy. Future studies should be done to explore predictive models for exacerbations.

DISCLOSURES

No outside funding supported this study. Samp is now employed by, and owns stock in, AbbVie. The other authors have nothing to disclose. Study concept and design were contributed by Joo and Pickard, along with the other authors. Samp and Lee performed the data analysis, with assistance from the other authors. Samp wrote the manuscript, which was revised by Schumock and Calip, along with the other authors.