Using dynamics of forced expiration to identify COPD where conventional criteria for the FEV1/FVC ratio do not match

lnc-IL7R Expression Reflects Physiological Pulmonary Function and Its Aberration Is a Putative Indicator of COPD

Despite rapidly evolving pathobiological mechanistic demystification, coupled with advances in diagnostic and therapeutic modalities, chronic obstructive pulmonary disease (COPD) remains a major healthcare and clinical challenge, globally. Further compounded by the dearth of available curative anti-COPD therapy, it is posited that this challenge may not be dissociated from the current lack of actionable COPD pathognomonic molecular biomarkers. There is accruing evidence of the involvement of protracted ‘smoldering’ inflammation, repeated lung injury, and accelerated lung aging in enhanced predisposition to or progression of COPD. The relatively novel uncharacterized human long noncoding RNA lnc-IL7R (otherwise called LOC100506406) is increasingly designated a negative modulator of inflammation and regulator of cellular stress responses; however, its role in pulmonary physiology and COPD pathogenesis remains largely unclear and underexplored. Our previous work suggested that upregulated lnc-IL7R expression attenuates inflammation following the activation of the toll-like receptor (TLR)-dependent innate immune system, and that the upregulated lnc-IL7R is anti-correlated with concomitant high PM2.5, PM10, and SO2 levels, which is pathognomonic for exacerbated/aggravated COPD in Taiwan. In the present study, our quantitative analysis of lnc-IL7R expression in our COPD cohort (n = 125) showed that the lnc-IL7R level was significantly correlated with physiological pulmonary function and exhibited COPD-based stratification implications (area under the curve, AUC = 0.86, p < 0.001). We found that the lnc-IL7R level correctly identified patients with COPD (sensitivity = 0.83, specificity = 0.83), precisely discriminated those without emphysematous phenotype (sensitivity = 0.48, specificity = 0.89), and its differential expression reflected disease course based on its correlation with the COPD GOLD stage (r = −0.59, p < 0.001), %LAA-950insp (r = −0.30, p = 0.002), total LAA (r = −0.35, p < 0.001), FEV1(%) (r = 0.52, p < 0.001), FVC (%) (r = 0.45, p < 0.001), and post-bronchodilator FEV1/FVC (r = 0.41, p < 0.001). Consistent with other data, our bioinformatics-aided dose−response plot showed that the probability of COPD decreased as lnc-IL7R expression increased, thus, corroborating our posited anti-COPD therapeutic potential of lnc-IL7R. In conclusion, reduced lnc-IL7R expression not only is associated with inflammation in the airway epithelial cells but is indicative of impaired pulmonary function, pathognomonic of COPD, and predictive of an exacerbated/ aggravated COPD phenotype. These data provide new mechanistic insights into the ailing lung and COPD progression, as well as suggest a novel actionable molecular factor that may be exploited as an efficacious therapeutic strategy in patients with COPD.

Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease

OBJECTIVES

Identifying acute exacerbations in chronic obstructive pulmonary disease (AECOPDs) is of utmost importance for reducing the associated mortality and financial burden. In this research, the authors aimed to develop identification models for AECOPDs and to compare the relative performance of different modeling paradigms to find the best model for this task.

METHODS

Data were extracted from electronic medical records (EMRs) of patients with chronic obstructive pulmonary disease who admitted to the China-Japan Friendship Hospital between February 2011 and March 2017. Five machine learning algorithms (random forest, support vector machine, logistic regression, K-nearest neighbor and naïve Bayes) were used to develop the AECOPDs identification models. Feature selection was performed to find an optimal feature subset. 10-folds cross-validation was used to find the best hyperparameters for each model. The following metrics:  area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate the performance of these models.

RESULTS

A total of 303 EMRs (AECOPDs patients:135; None AECOPDs patients: 168) were included in the study. The SVM model obtained the best performance (sensitivity: 0.80, specificity: 0.83, positive predictive value:0.81, negative predictive value:0.85 and area under the receiver operating characteristic curve: 0.90) after performing feature selection.

CONCLUSIONS

Our research confirms that the proposed model based on the support vector machine is a powerful tool to identify AECOPDs patients, and it is promising to provide decision support for clinicians when they are struggling to give a confirmed clinical diagnosis.

Spirometric indices of early airflow impairment in individuals at risk of developing COPD: Spirometry beyond FEV1/FVC

Spirometry is the current gold standard for diagnosing and monitoring the progression of Chronic Obstructive Pulmonary Disease (COPD). However, many current and former smokers who do not meet established spirometric criteria for the diagnosis of this disease have symptoms and clinical courses similar to those with diagnosed COPD. Large longitudinal observational studies following individuals at risk of developing COPD offer us additional insight into spirometric patterns of disease development and progression. Analysis of forced expiratory maneuver changes over time may allow us to better understand early changes predictive of progressive disease. This review discusses the theoretical ability of spirometry to capture fine pathophysiologic changes in early airway disease, highlights the shortcomings of current diagnostic criteria, and reviews existing evidence for spirometric measures which may be used to better detect early airflow impairment.

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential

PURPOSE OF REVIEW

The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases.

RECENT FINDINGS

Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies.

SUMMARY

Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.

Small airways disease: time for a revisit?

It is increasingly acknowledged that delays in the diagnosis of chronic inflammatory lung conditions have hampered our understanding of pathogenesis and thus our ability to design efficacious therapies. This is particularly true for COPD, where most patients are diagnosed with moderate-to-severe airflow obstruction and little is known about the inflammatory processes present in early disease. There is great interest in developing screening tests that can identify those most at risk of developing COPD before airflow obstruction has developed for the purpose of research and clinical care. Landmark pathology studies have suggested that damage to the small airways precedes the development of airflow obstruction and emphysema and, thus, presents an opportunity to identify those at risk of COPD. However, despite a number of physiological tests being available to assess small airways function, none have been adopted into routine care in COPD. The reasons that tests of small airways have not been utilized widely include variability in test results and a lack of validated reference ranges from which to compare results for some methodologies. Furthermore, population studies have not consistently demonstrated their ability to diagnose disease. However, the landscape may be changing. As the equipment that delivers tests of small airways become more widely available, reference ranges are emerging and newer methodologies specifically seek to address variability and difficulty in test performance. Moreover, there is evidence that while tests of small airways may not be helpful across the full range of established disease severity, there may be specific groups (particularly those with early disease) where they might be informative. In this review, commonly utilized tests of small airways are critically appraised to highlight why these tests may be important, how they can be used and what knowledge gaps remain for their use in COPD.

The relationship between smoking and knee osteoarthritis in the Osteoarthritis Initiative

OBJECTIVE

To estimate the extent that smoking history is associated with symptoms and disease progression among individuals with radiographically confirmed knee Osteoarthritis (OA).

METHOD

Both cross-sectional (baseline) and longitudinal studies employed data from the Osteoarthritis Initiative (OAI) (n = 2250 participants). Smoking history was assessed at baseline with 44% current or former smokers. The Western Ontario and McMaster Universities Arthritis Index (WOMAC) was used to measure knee pain, stiffness, and physical function. Disease progression was measured using joint space width (JSW). We used adjusted multivariable linear models to examine the relationship between smoking status and exposure in pack years (PY) with symptoms and JSW at baseline. Changes in symptoms and JSW over time were further assessed.

RESULTS

In cross-sectional analyses, compared to never-smokers high PY (≥15 PY) was associated with slightly greater pain (beta 0.36, 95% CI: 0.01-0.71) and stiffness (beta 0.20, 95% CI: 0.03-0.37); and low PY (<15 PY) was associated with better JSW (beta 0.15, 95% CI: 0.02-0.28). Current smoking was associated with greater pain (beta 0.59, 95% CI: 0.04-1.15) compared to never-smokers. These associations were not confirmed in the longitudinal study. Longitudinally, no associations were found between high or low PY or baseline smoking status with changes in symptoms (at 72 months) or JSW (at 48 months).

CONCLUSION

Cross-sectional findings are likely due residual confounding. The more robust longitudinal analysis found no associations between smoking status and symptoms or JSW. Long-term smoking provides no benefits to knee OA patients while exposing them to other well-documented serious health risks.