EXPRESS: State of the Art: Alternative Overlap Syndrome - Asthma and Obstructive Sleep Apnea.

In the general population, Bronchial Asthma (BA) and Obstructive Sleep Apnea (OSA) are amongst the most prevalent chronic respiratory disorders. Significant epidemiologic connections and complex pathogenetic pathways link these disorders via complex interactions at genetic, epigenetic and environmental levels. The coexistence of BA and OSA in an individual likely represents a distinct syndrome, i.e., a collection of clinical manifestations attributable to several mechanisms and pathobiological signatures. To avoid terminological confusion, this association has been named alternative overlap syndrome (vs overlap syndrome represented by the chronic obstructive pulmonary disease - OSA association).This comprehensive review summarizes the complex, often bidirectional links between the constituents of the alternative overlap syndrome. Cross-sectional, population or clinic-based studies are unlikely to elucidate causality or directionality in these relationships. Even longitudinal epidemiological evaluations in BA cohorts developing over time OSA, or OSA cohorts developing BA during follow-up cannot exclude time factors or causal influence of other known or unknown mediators. As such, a lot of pathophysiological interactions described here have suggestive evidence, biological plausibility, potential or actual directionality. By showcasing existing evidence and current knowledge gaps, the hope is that deliberate, focused and collaborative efforts in the near-future will be geared towards opportunities to shine light on the unknowns, and accelerate discovery in this field of health, clinical care, education, research and scholarly endeavors.

Contribution of Obstructive Sleep Apnea to Asthmatic Airway Inflammation and Impact of Its Treatment on the Course of Asthma.

Asthma and obstructive sleep apnea (OSA) are very common respiratory disorders in the general population. Beyond their high prevalence, shared risk factors, and genetic linkages, bidirectional relationships between asthma and OSA exist, each disorder affecting the other's presence and severity. The author reviews here some of the salient links between constituents of the alternative overlap syndrome, that is, OSA comorbid with asthma, with an emphasis on the effects of OSA or its treatment on inflammation in asthma. In the directional relationship from OSA toward asthma, beyond direct influences, multiple factors and comorbidities seem to contribute.

Deployment-related Cigarette Smoking Behaviors and Pulmonary Function Among U.S. Veterans.

INTRODUCTION: The effects of smoking on lung function among post-9/11 Veterans deployed to environments with high levels of ambient particulate matter are incompletely understood. MATERIALS AND METHODS: We analyzed interim data (04/2018-03/2020) from the Veterans Affairs (VA) Cooperative Studies Program #595, "Service and Health Among Deployed Veterans". Veterans with ≥1 land-based deployments enrolled at 1 of 6 regional Veterans Affairs sites completed questionnaires and spirometry. Multivariable linear regression models assessed associations between cigarette smoking (cumulative, deployment-related and non-deployment-related) with pulmonary function. RESULTS: Among 1,836 participants (mean age 40.7 ± 9.6, 88.6% male), 44.8% (n = 822) were ever-smokers (mean age 39.5 ± 9.5; 91.2% male). Among ever-smokers, 86% (n = 710) initiated smoking before deployment, while 11% (n = 90) initiated smoking during deployment(s). Smoking intensity was 50% greater during deployment than other periods (0.75 versus 0.50 packs-per-day; P < .05), and those with multiple deployments (40.4%) were more likely to smoke during deployment relative to those with single deployments (82% versus 74%). Total cumulative pack-years (median [IQR] = 3.8 [1, 10]) was inversely associated with post-bronchodilator FEV1%-predicted (-0.82; [95% CI] = [-1.25, -0.50] %-predicted per 4 pack-years) and FEV1/FVC%-predicted (-0.54; [95% CI] = [-0.78, -0.43] %-predicted per 4 pack-years). Deployment-related pack-years demonstrated similar point estimates of associations with FEV1%-predicted (-0.61; [95% CI] = [-2.28, 1.09]) and FEV1/FVC%-predicted (-1.09; [95% CI] = [-2.52, 0.50]) as non-deployment-related pack-years (-0.83; [95% CI] = [-1.26, -0.50] for FEV1%-predicted; -0.52; [95% CI] = [-0.73, -0.36] for FEV1/FVC%-predicted). CONCLUSIONS: Although cumulative pack-years smoking was modest in this cohort, an inverse association with pulmonary function was detectable. Deployment-related pack-years had a similar association with pulmonary function compared to non-deployment-related pack-years.

Glucocorticoid therapy in respiratory illness: bench to bedside.

Each year, hundreds of millions of individuals are affected by respiratory disease leading to approximately 4 million deaths. Most respiratory pathologies involve substantially dysregulated immune processes that either fail to resolve the underlying process or actively exacerbate the disease. Therefore, clinicians have long considered immune-modulating corticosteroids (CSs), particularly glucocorticoids (GCs), as a critical tool for management of a wide spectrum of respiratory conditions. However, the complex interplay between effectiveness, risks and side effects can lead to different results, depending on the disease in consideration. In this comprehensive review, we present a summary of the bench and the bedside evidence regarding GC treatment in a spectrum of respiratory illnesses. We first describe here the experimental evidence of GC effects in the distal airways and/or parenchyma, both in vitro and in disease-specific animal studies, then we evaluate the recent clinical evidence regarding GC treatment in over 20 respiratory pathologies. Overall, CS remain a critical tool in the management of respiratory illness, but their benefits are dependent on the underlying pathology and should be weighed against patient-specific risks.

Sphinganine is associated with 24-h MAP in the non-sleepy with OSA.

INTRODUCTION: Excessive daytime sleepiness is a debilitating symptom of obstructive sleep apnea (OSA) linked to cardiovascular disease, and metabolomic mechanisms underlying this relationship remain unknown. We examine whether metabolites from inflammatory and oxidative stress-related pathways that were identified in our prior work could be involved in connecting the two phenomena. METHODS: This study included 57 sleepy (Epworth Sleepiness Scale (ESS) ≥ 10) and 37 non-sleepy (ESS < 10) participants newly diagnosed and untreated for OSA that completed an overnight in-lab or at home sleep study who were recruited from the Emory Mechanisms of Sleepiness Symptoms Study (EMOSS). Differences in fasting blood samples of metabolites were explored in participants with sleepiness versus those without and multiple linear regression models were utilized to examine the association between metabolites and mean arterial pressure (MAP). RESULTS: The 24-h MAP was higher in sleepy 92.8 mmHg (8.4) as compared to non-sleepy 88.8 mmHg (8.1) individuals (P = 0.03). Although targeted metabolites were not significantly associated with MAP, when we stratified by sleepiness group, we found that sphinganine is significantly associated with MAP (Estimate = 8.7, SE = 3.7, P = 0.045) in non-sleepy patients when controlling for age, BMI, smoking status, and apnea-hypopnea index (AHI). CONCLUSION: This is the first study to evaluate the relationship of inflammation and oxidative stress related metabolites in sleepy versus non-sleepy participants with newly diagnosed OSA and their association with 24-h MAP. Our study suggests that Sphinganine is associated with 24 hour MAP in the non-sleepy participants with OSA.

Area under the expiratory flow-volume curve: normative values in the National Health and Nutrition Survey (NHANES) study.

The area under the expiratory flow-volume (AEX-FV) loop has been evaluated before as a spirometric tool for assessing respiratory functional impairment. We computed the AEX-FV curves in spirometry tests performed on 20,313 participants in the National Health and Nutrition Examination Survey (NHANES) study.We analyzed 108,939 spirometry tests performed between 2007 and 2012 (5964 children; 14,349 adults). In these tests, we computed the three areas from existing NHANES raw data on instantaneous expiratory flows measured at 0.01 s intervals.Mean best-trial measurements for AEX-FV were 3.4 in boys, 2.8 in girls, 11.8 in men and 7.7 L2/s in women. We characterized indices of central tendency and dispersion of the measurements (eg, means and fifth percentiles-lower limits of normal) by age group (children vs adults), gender, race or ethnicity group and effort grading. Simple regression equations using logarithmic transformations of the above areas and using age, gender and height as inputs provided good predictive ability for the variable AEX-FV.Regular, digital spirometry could and should make available to clinicians and researchers the area under the curves for flow versus volume graph, providing additional tools in our armamentarium to evaluate ventilatory impairments and patterns, and possibly respiratory disability.

A meta-analysis of diagnostic test performance of peripheral arterial tonometry studies.

STUDY OBJECTIVES: The objective of this meta-analysis was to analyze agreement in apnea-hypopnea index (AHI) determination between peripheral arterial tonometry (PAT) and polysomnography (PSG) studies. METHODS: Mean AHI bias and standard deviation extracted from Bland-Altman plots reported in studies were pooled in a meta-analysis, which was then used to calculate percentage errors of limit agreement in AHI determination by PAT using PSG AHI as the reference. Individual participant data (where reported in studies) were used to compute Cohen's kappa to assess agreement between PSG and PAT on sleep apnea severity and for computing the sensitivity and specificity of PAT at different AHI thresholds using PSG AHI as the reference. RESULTS: From 17 studies and 1,318 participants (all underwent simultaneous PSG and use of the WatchPAT device), a pooled mean AHI bias of 0.30 (standard error [SE], 0.74) and a WatchPAT AHI percentage error of 230% was calculated. The meta-analysis of Cohen's kappa for agreement between PSG and WatchPAT studies for classifying patients with no sleep apnea, mild, moderate, or severe sleep apnea severity was 0.45 (SE, 0.06), 0.29 (SE, 0.05), 0.25 (SE, 0.07), and 0.64 (SE, 0.05), respectively. At AHI thresholds of 5, 15 and 30 events/h, WatchPAT studies showed pooled sensitivities and specificities of 94.11% and 43.47%, 92.21% and 72.39%, and 74.11% and 87.10%, respectively. Likelihood ratios were not significant at any AHI threshold. CONCLUSIONS: The results of this meta-analysis suggest clinically significant discordance between WatchPAT and PSG measurements of AHI, significant sleep apnea severity misclassification by PAT studies, and poor diagnostic test performance. CITATION: Iftikhar IH, Finch CE, Shah AS, Augunstein CA, Ioachimescu OC. A meta-analysis of diagnostic test performance of peripheral arterial tonometry studies. J Clin Sleep Med. 2022;18(4):1093-1102.

Area under the expiratory flow-volume curve: predicted values by regression and deep learning methods and recommendations for clinical practice.

BACKGROUND: In spirometry, the area under expiratory flow-volume curve (AEX-FV) was found to perform well in diagnosing and stratifying physiologic impairments, potentially lessening the need for complex lung volume testing. Expanding on prior work, this study assesses the accuracy and the utility of several models of estimating AEX-FV based on forced vital capacity (FVC) and several instantaneous flows. These models could be incorporated in regular spirometry reports, especially when actual AEX-FV measurements are not available. METHODS: We analysed 4845 normal spirometry tests, performed on 3634 non-smoking subjects without known respiratory disease or complaints. Estimated AEX-FV was computed based on FVC and several flows: peak expiratory flow, isovolumic forced expiratory flow at 25%, 50% and 75% of FVC (FEF25, FEF50 and FEF75, respectively). The estimations were based on simple regression with and without interactions, by optimised regression models and by a deep learning algorithm that predicted the response surface of AEX-FV without interference from any predictor collinearities or normality assumption violations. RESULTS: Median/IQR of actual square root of AEX-FV was 3.8/3.1-4.5 L2/s. The per cent of variance (R2) explained by the models selected was very high (>0.990), the effect of collinearities was negligible and the use of deep learning algorithms likely unnecessary for regular or routine pulmonary function testing laboratory usage. CONCLUSIONS: In the absence of actual AEX-FV, a simple regression model without interactions between predictors or use of optimisation techniques can provide a reasonable estimation for clinical practice, thus making AEX-FV an easily available additional tool for interpreting spirometry.

Assessing bronchodilator response by changes in per cent predicted forced expiratory volume in one second.

In pulmonary function testing by spirometry, bronchodilator responsiveness (BDR) evaluates the degree of volume and airflow improvement in response to an inhaled short-acting bronchodilator (BD). The traditional, binary categorization (present vs absent BDR) has multiple pitfalls and limitations. To overcome these limitations, a novel classification that defines five categories (negative, minimal, mild, moderate and marked BDR), and based on % and absolute changes in forced expiratory volume in 1 s (FEV1), has been recently developed and validated in patients with chronic obstructive pulmonary disease, and against multiple objective and subjective measurements. In this study, working on several large spirometry cohorts from two different institutions (n=31 598 tests), we redefined the novel BDR categories based on delta post-BD-pre-BD FEV1 % predicted values. Our newly proposed BDR partition is based on several distinct intervals for delta post-BD-pre-BD % predicted FEV1 using Global Lung Initiative predictive equations. In testing, training and validation cohorts, the model performed well in all BDR categories. In a validation set that included only normal baseline spirometries, the partition model had a higher rate of misclassification, possibly due to unrestricted BD use prior to baseline testing. A partition that uses delta % predicted FEV1 with the following intervals ≤0%, 0%-2%, 2%-4%, 4%-8% and >8% may be a valid and easy-to-use tool for assessing BDR in spirometry. We confirmed in our cohorts that these thresholds are characterized by low variance and that they are generally gender-independent and race-independent. Future validation in other cohorts and in other populations is needed.

Area under the expiratory flow-volume curve: predicted values by artificial neural networks.

Area under expiratory flow-volume curve (AEX) has been proposed recently to be a useful spirometric tool for assessing ventilatory patterns and impairment severity. We derive here normative reference values for AEX, based on age, gender, race, height and weight, and by using artificial neural network (ANN) algorithms. We analyzed 3567 normal spirometry tests with available AEX values, performed on subjects from two countries (United States and Spain). Regular linear or optimized regression and ANN models were built using traditional predictors of lung function. The ANN-based models outperformed the de novo regression-based equations for AEXpredicted and AEX z scores using race, gender, age, height and weight as predictor factors. We compared these reference values with previously developed equations for AEX (by gender and race), and found that the ANN models led to the most accurate predictions. When we compared the performance of ANN-based models in derivation/training, internal validation/testing, and external validation random groups, we found that the models based on pooling samples from various geographic areas outperformed the other models (in both central tendency and dispersion of the residuals, ameliorating any cohort effects). In a geographically diverse cohort of subjects with normal spirometry, we computed by both regression and ANN models several predicted equations and z scores for AEX, an alternative measurement of respiratory function. We found that the dynamic nature of the ANN allows for continuous improvement of the predictive models' performance, thus promising that the AEX could become an essential tool in assessing respiratory impairment.

Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea.

Outside sleep laboratory settings, peripheral arterial tonometry (PAT, eg, WatchPat) represents a validated modality for diagnosing obstructive sleep apnea (OSA). We have shown before that the accuracy of home sleep apnea testing by WatchPat 200 devices in diagnosing OSA is suboptimal (50%-70%). In order to improve its diagnostic performance, we built several models that predict the main functional parameter of polysomnography (PSG), Apnea Hypopnea Index (AHI). Participants were recruited in our Sleep Center and underwent concurrent in-laboratory PSG and PAT recordings. Statistical models were then developed to predict AHI by using robust functional parameters from PAT-based testing, in concert with available demographic and anthropometric data, and their performance was confirmed in a random validation subgroup of the cohort. Five hundred synchronous PSG and WatchPat sets were analyzed. Mean diagnostic accuracy of PAT was improved to 67%, 81% and 85% in mild, moderate-severe or no OSA, respectively, by several models that included participants' age, gender, neck circumference, body mass index and the number of 4% desaturations/hour. WatchPat had an overall accuracy of 85.7% and a positive predictive value of 87.3% in diagnosing OSA (by predicted AHI above 5). In this large cohort of patients with high pretest probability of OSA, we built several models based on 4% oxygen desaturations, neck circumference, body mass index and several other variables. These simple models can be used at the point-of-care, in order to improve the diagnostic accuracy of the PAT-based testing, thus ameliorating the high rates of misclassification for OSA presence or disease severity.