Area under the forced expiratory flow-volume loop in spirometry indicates severe hyperinflation in COPD patients

Curve-Modelling and Machine Learning for a Better COPD Diagnosis

Background

Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD).

Methods

This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD.

Results

A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value.

Conclusion

Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.

Lung Ultrasound Assessment of Lung Hyperinflation in Patients with Stable COPD: An Effective Diagnostic Tool

Purpose

To evaluate the degree of lung hyperinflation (LH) in patients with stable chronic obstructive pulmonary disease (COPD) by lung ultrasound score (LUS) and assess its value.

Patients and Methods

We conducted a study of 149 patients with stable COPD and 100 healthy controls recruited by the Second Affiliated Hospital of Fujian Medical University. The pleural sliding displacement (PSD) was measured, the sliding of the pleura in different areas was observed, and LUS was calculated from both of them. The diaphragm excursion (DE), residual capacity (RV), total lung capacity (TLC), inspiratory capacity (IC) and functional residual capacity (FRC) were measured. We described the correlation between ultrasound indicators and pulmonary function indicators reflecting LH. Multiple linear regression analysis was used. The ROC curves of LUS and DE were drawn to evaluate their diagnostic efficacy, and De Long method was used for comparison.

Results

(1) The LUS of patients with stable COPD were positively correlated with RV, TLC, RV/TLC and FRC and negatively correlated with IC and IC/TLC (r1=0.72, r2=0.41, r3=0.72, r4=0.70, r5=-0.56, r6=-0.65, P < 0.001). The correlation was stronger than that between DE at maximal deep inspiration and the corresponding pulmonary function indices (r1=-0.41, r2=-0.26, r3=-0.40, r4=-0.43, r5=0.30, r6=0.37, P < 0.001). (2) Multiple linear regression analysis showed that LUS were significantly correlated with IC/TLC and RV/TLC. (3) With IC/TLC<25% and RV/TLC>60% as the diagnostic criterion of severe LH, the areas under the ROC curves of LUS and DE at maximal deep inspiration for diagnosing severe LH were 0.914 and 0.385, 0.845 and 0.543, respectively (P < 0.001).

Conclusion

The lung ultrasound score is an important parameter for evaluating LH. LUS is better than DE at maximal deep inspiration for diagnosing severe LH and is expected to become an effective auxiliary tool for evaluating LH.

Hyperinflation and reduced diffusing capacity predict prognosis in SCLC: value of extended pre-therapeutic lung function testing

BACKGROUND

Small cell lung cancer (SCLC) is characterized by aggressive growth and poor prognosis. Although SCLC affects nearly exclusively heavy smokers and leads to frequent respiratory symptoms, the impact of pre-therapeutic lung function testing in SCLC is sparely investigated until now. Therefore, we sought to examine whether we could find prognostic markers in pre-therapeutic lung function testing of SCLC patients.

PATIENTS AND METHODS

We retrospectively analysed a cohort of 205 patients with the diagnosis of SCLC between 2010 and 2018. Pre-therapeutic values of spirometry, body plethysmography and measurement of diffusing capacity was extracted from patients' charts. Comparisons between groups were performed using the Mann-Whitney U-test or by chi-square tests as appropriate. Kaplan-Meier analyses and COX-regression models were performed to correlate lung function parameters with patients' outcome.

RESULTS

Airway obstruction itself, or the diagnosis chronic obstructive pulmonary disease (COPD) based on GOLD definitions did not correlate with survival in SCLC patients. Hyperinflation measured by increased residual volume and residual volume to total lung capacity ratio (log-rank p < 0.001) and reduced diffusing capacity (log-rank p = 0.007) were associated with reduced survival. Furthermore, patients with hyperinflation as well as impairments in gas exchange representing an emphysematic phenotype had the worst outcome (log-rank p < 0.001).

CONCLUSION

We recommend including body plethysmography and measurement of diffusing capacity in the pre-therapeutic assessment of SCLC patients. Our findings suggest that reduction of hyperinflation may lead to better outcome in SCLC patients. Thus, in addition to effective tumour therapy, adequate therapy of the comorbidity of COPD should also be provided. In particular, measures to reduce hyperinflation by means of dual bronchodilation as well as respiratory physiotherapy should be further assessed in this setting.

Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD

BACKGROUND

Parameters from maximal expiratory flow-volume curves (MEFVC) have been linked to CT-based parameters of COPD. However, the association between MEFVC shape and phenotypes like emphysema, small airways disease (SAD) and bronchial wall thickening (BWT) has not been investigated.

RESEARCH QUESTION

We analyzed if the shape of MEFVC can be linked to CT-determined emphysema, SAD and BWT in a large cohort of COPDGene participants.

STUDY DESIGN AND METHODS

In the COPDGene cohort, we used principal component analysis (PCA) to extract patterns from MEFVC shape and performed multiple linear regression to assess the association of these patterns with CT parameters over the COPD spectrum, in mild and moderate-severe COPD.

RESULTS

Over the entire spectrum, in mild and moderate-severe COPD, principal components of MEFVC were important predictors for the continuous CT parameters. Their contribution to the prediction of emphysema diminished when classical pulmonary function test parameters were added. For SAD, the components remained very strong predictors. The adjusted R² was higher in moderate-severe COPD, while in mild COPD, the adjusted R² for all CT outcomes was low; 0.28 for emphysema, 0.21 for SAD and 0.19 for BWT.

INTERPRETATION

The shape of the maximal expiratory flow-volume curve as analyzed with PCA is not an appropriate screening tool for early disease phenotypes identified by CT scan. However, it contributes to assessing emphysema and SAD in moderate-severe COPD.

Use of flow volume curve to evaluate large airway obstruction

The flow volume loop (FVL) is a graphic display of airflow against lung volumes at different levels obtained during the maximum inspiratory and expiratory maneuver. It is a simple and reproducible method of lung function assessment. A narrative review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. PubMed, EMBASE, Ovid MEDLINE and CINAHL databases were queried and reviewed for studies pertinent to the various FVLs abnormalities and their mechanisms from January 2020 to December 2020. We used the following search terms; flow-volume loop, upper airway obstruction, Obstructive airway disease, and spirometry.  Assessing the shape of the flow-volume loop is particularly helpful in diagnosing and localizing upper airway obstruction. They are also helpful in identifying bronchodilator response to treatment. Characteristic FVLs is also seen in patients with obstructive or restrictive lung disorders. Spirometry should be interpreted using the absolute values for flows and volumes as well as the flow volume and volume time curves.

Area under flow-volume loop may predict severe exacerbation in COPD patients with high grade of dyspnea

OBJECTIVE

Exacerbations in patients with COPD may still be unpredictable, although the general risk factors have been well defined. We aimed to determine the role of a novel parameter, area under flow-volume loop, in predicting severe exacerbations.

METHODS

In this single-centre retrospective cohort study, 81 COPD patients over 40 years of age with high grade of dyspnea (having a CAT score of ≥10) and a history of ≥1 moderate exacerbation in the previous year were included. Area under flow-volume curve (AreaFE%) was obtained from pulmonary function test graph and calculated from Matlab programme. Univariate and multivariate logistic regression analyses were performed to determine independent risk factors of the severe exacerbation.

RESULTS

Patients with severe exacerbation (n = 70, 86.4 %) were older. They had lower FEV1%, FVC%, 6MWD, AreaFE% and higher CAT score than patients without exacerbation. After performing multivariate analysis, high CAT score and low AreaFE% value were found to be independent risk factors for severe exacerbation (OR: 1.12, 95 % CI: 1.065-1.724; p = 0.01 and OR: 1.18, 95 % CI: 0.732-0.974; p = 0.02).

CONCLUSIONS

We found that a low AreaFE% value was an independent risk factor in addition to a high CAT score and these both have an excellent discriminative ability in predicting the risk of severe exacerbation.

Pulmonary function testing in COPD: looking beyond the curtain of FEV1

Chronic obstructive pulmonary disease (COPD) management remains challenging due to the high heterogeneity of clinical symptoms and the complex pathophysiological basis of the disease. Airflow limitation, diagnosed by spirometry, remains the cornerstone of the diagnosis. However, the calculation of the forced expiratory volume in the first second (FEV1) alone, has limitations in uncovering the underlying complexity of the disease. Incorporating additional pulmonary function tests (PFTs) in the everyday clinical evaluation of COPD patients, like resting volume, capacity and airway resistance measurements, diffusion capacity measurements, forced oscillation technique, field and cardiopulmonary exercise testing and muscle strength evaluation, may prove essential in tailoring medical management to meet the needs of such a heterogeneous patient population. We aimed to provide a comprehensive overview of the available PFTs, which can be incorporated into the primary care physician's practice to enhance the efficiency of COPD management.

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 (FEF_25, 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.

An Alternative Spirometric Measurement. Area under the Expiratory Flow-Volume Curve

Rationale: Interpretation of spirometry is influenced by inherent limitations and by the normal or predicted reference values used. For example, traditional spirometric parameters such as “distal” airflows do not provide sufficient differentiating capacity, especially for mixed patterns or small airway disease.

Objectives: We assessed the utility of an alternative spirometric parameter (area under the expiratory flow–volume curve [AEX]) in differentiating between normal, obstruction, restriction, and mixed patterns, as well as in severity stratification of traditional functional impairments.

Methods: We analyzed 15,308 spirometry tests in subjects who had same-day lung volume assessments in a pulmonary function laboratory. Using Global Lung Initiative predicted values and standard criteria for pulmonary function impairment, we assessed the diagnostic performance of AEX in best-split partition and artificial neural network models.

Results: The average square root AEX values were 3.32, 1.81, 2.30, and 1.64 L⋅s^−0.5 in normal, obstruction, restriction, and mixed patterns, respectively. As such, in combination with traditional spirometric measurements, the square root of AEX differentiated well between normal, obstruction, restriction, and mixed defects. Using forced expiratory volume in 1 second (FEV₁), forced vital capacity (FVC), and FEV₁/FVC z-scores plus the square root of AEX in a machine learning algorithm, diagnostic categorization of ventilatory impairments was accomplished with very low rates of misclassification (<9%). Especially for mixed ventilatory patterns, the neural network model performed best in improving the rates of diagnostic misclassification.

Conclusions: Using a novel approach to lung function assessment in combination with traditional spirometric measurements, AEX differentiates well between normal, obstruction, restriction and mixed impairments, potentially obviating the need for more complex lung volume-based determinations.

Diffusing capacity in chronic obstructive pulmonary disease assessment: A meta-analysis

To achieve a multidimensional evaluation of chronic obstructive pulmonary disease (COPD) patients, the spirometry measures are supplemented by assessment of symptoms, risk of exacerbations, and CT imaging. However, the measurement of diffusing capacity of the lung for carbon monoxide (DLCO) is not included in most common used models of COPD assessment. Here, we conducted a meta-analysis to evaluate the role of DLCO in COPD assessment.The studies were identified by searching the terms "diffusing capacity" OR "diffusing capacity for carbon monoxide" or "DLCO" AND "COPD" AND "assessment" in Pubmed, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Scopus, and Web of Science databases. The mean difference of DLCO % predict was assessed in COPD patient with different severity (according to GOLD stage and GOLD group), between COPD patients with or without with frequent exacerbation, between survivors and non-survivors, between emphysema dominant and non-emphysema dominant COPD patients, and between COPD patients with or without pulmonary hypertension.43 studies were included in the meta-analysis. DLCO % predicted was significantly lower in COPD patients with more severe airflow limitation (stage II/IV), more symptoms (group B/D), and high exacerbation risk (group C/D). Lower DLCO % predicted was also found in exacerbation patients and non-survivors. Low DLCO % predicted was related to emphysema dominant phenotype, and COPD patients with PH.The current meta-analysis suggested that DLCO % predicted might be an important measurement for COPD patients in terms of severity, exacerbation risk, mortality, emphysema domination, and presence of pulmonary hypertension. As diffusion capacity reflects pulmonary ventilation and perfusion at the same time, the predictive value of DLCO or DLCO combined with other criteria worth further exploration.

Prediction of air trapping or pulmonary hyperinflation by forced spirometry in COPD patients: results from COSYCONET

Background

Air trapping and lung hyperinflation are major determinants of prognosis and response to therapy in chronic obstructive pulmonary disease (COPD). They are often determined by body plethysmography, which has limited availability, and so the question arises as to what extent they can be estimated via spirometry.

Methods

We used data from visits 1–5 of the COPD cohort COSYCONET. Predictive parameters were derived from visit 1 data, while visit 2–5 data was used to assess reproducibility. Pooled data then yielded prediction models including sex, age, height, and body mass index as covariates. Hyperinflation was defined as ratio of residual volume (RV) to total lung capacity (TLC) above the upper limit of normal. (ClinicalTrials.gov identifier: NCT01245933).

Results

Visit 1 data from 1988 patients (Global Initiative for Chronic Obstructive Lung Disease grades 1–4, n=187, 847, 766, 188, respectively) were available for analysis (n=1231 males, 757 females; mean±sd age 65.1±8.4 years; forced expiratory volume in 1 s (FEV₁) 53.1±18.4 % predicted (% pred); forced vital capacity (FVC) 78.8±18.8 % pred; RV/TLC 0.547±0.107). In total, 7157 datasets were analysed. Among measures of hyperinflation, RV/TLC showed the closest relationship to FEV₁ % pred and FVC % pred, which were sufficient for prediction. Their relationship to RV/TLC could be depicted in nomograms. Even when neglecting covariates, hyperinflation was predicted by FEV₁ % pred, FVC % pred or their combination with an area under the curve of 0.870, 0.864 and 0.889, respectively.

Conclusions

The degree of air trapping/hyperinflation in terms of RV/TLC can be estimated in a simple manner from forced spirometry, with an accuracy sufficient for inferring the presence of hyperinflation. This may be useful for clinical settings, where body plethysmography is not available.

This proposed method allows estimation of hyperinflation in COPD by spirometry, obviating the need for body plethysmography or further techniques. Results are depicted in easily applicable nomograms that can be used in clinical practice.https://bit.ly/3c0tUNL

Determining emphysema in adult patients with COPD-bronchiectasis overlap using a novel spirometric parameter: area under the forced expiratory flow-volume loop

BACKGROUND

Defining the optimal therapeutic approach in patients with chronic obstructive pulmonary disease (COPD) bronchiectasis overlap (CBO) is challenging. The presence of emphysema suggests that COPD is the primary problem and it impacts therapeutic decision making.

RESEARCH DESIGN AND METHODS

We hypothesized that the AreaFE% performance will be reliable in diagnosing the presence of emphysema such that serial CT scanning may not be needed. In this retrospective chart review study, we included 113 CBO patients (52 having emphysema, 61 not having emphysema). We compared these two groups according to conventional spirometric parameters and AreaFE% values.

RESULTS

54% of all patients were female and mean age was 58 years.FEV1%, FEV1/FVC and AreaFE% were found to be significantly lower in patients with emphysema. 12% is the cutoff value for AreaFE% in determining emphysema with 73% sensitivity,75% specificity, and 72% diagnostic accuracy (AUC: 0.82) and it provides superior estimation than conventional parameters.

CONCLUSIONS

We found that AreaFE% is more suitable for determining the presence of emphysema than conventional spirometric parameters in CBO patients. This novel parameter may be helpful instead of scanning thorax CT to indicate the presence of emphysema and manage treatment in the follow-up of CBO patients.

Validation of a method to assess emphysema severity by spirometry in the COPDGene study

Background

Standard spirometry cannot identify the predominant mechanism underlying airflow obstruction in COPD, namely emphysema or airway disease. We aimed at validating a previously developed methodology to detect emphysema by mathematical analysis of the maximal expiratory flow-volume (MEFV) curve in standard spirometry.

Methods

From the COPDGene population we selected those 5930 subjects with MEFV curve and inspiratory-expiratory CT obtained on the same day. The MEFV curve descending limb was fit real-time using forced vital capacity (FVC), peak expiratory flow, and forced expiratory flows at 25, 50 and 75% of FVC to derive an emphysema severity index (ESI), expressed as a continuous positive numeric parameter ranging from 0 to 10. According to inspiratory CT percent lung attenuation area below − 950 HU we defined three emphysema severity subgroups (%LAA_-950insp < 6, 6–14, ≥14). By co-registration of inspiratory-expiratory CT we quantified persistent (%pLDA) and functional (%fLDA) low-density areas as CT metrics of emphysema and airway disease, respectively.

Results

ESI differentiated CT emphysema severity subgroups increasing in parallel with GOLD stages (p < .001), but with high variability within each stage. ESI had significantly higher correlations (p < .001) with emphysema than with airway disease CT metrics, explaining 67% of %pLDA variability. Conversely, standard spirometric variables (FEV₁, FEV₁/FVC) had significantly lower correlations than ESI with emphysema CT metrics and did not differentiate between emphysema and airways CT metrics.

Conclusions

ESI adds to standard spirometry the power to discriminate whether emphysema is the predominant mechanism of airway obstruction. ESI methodology has been validated in the large multiethnic population of smokers of the COPDGene study and therefore it could be applied for clinical and research purposes in the general population of smokers, using a readily available online website.

Area Under the Expiratory Flow-Volume Curve (AEX): Assessing Bronchodilator Responsiveness

Background

Area under expiratory flow–volume curve (AEX) is a useful spirometric tool in stratifying respiratory impairment. The AEX approximations based on isovolumic flows can be used with reasonable accuracy when AEX is unavailable. We assessed here pre- to post-bronchodilator (BD) variability of AEX₄ as a functional assessment tool for lung disorders.

Methods

The BD response was assessed in 4330 subjects by changes in FEV₁, FVC, and AEX₄, which were derived from FVC, peak expiratory flow, and forced expiratory flow at 25%, 50%, and 75% FVC. Newly proposed BD response categories (negative, minimal, mild, moderate and marked) have been investigated in addition to standard criteria.

Results

Using standard BD criteria, 24% of subjects had a positive response. Using the new BD response categories, only 23% of subjects had a negative response; 45% minimal, 18% mild, 9% moderate, and 5% had a marked BD response. Mean percent change of the square root AEX₄ was 0.3% and 14.3% in the standard BD-negative and BD-positive response groups, respectively. In the new BD response categories of negative, minimal, mild, moderate, and marked, mean percent change of square root AEX₄ was − 8.2%, 2.9%, 9.2%, 15.0%, and 24.8%, respectively.

Conclusions

Mean pre- to post-BD variability of AEX₄ was < 6% and stratified well between newly proposed categories of BD response (negative, minimal, mild, moderate and marked). We suggest that AEX₄ (AEX) could become a useful measurement for stratifying dysfunction in obstructive lung disease and invite further investigation into indications for using bronchodilator agents or disease-modifying, anti-inflammatory therapies.

Assessing small airway disease in GLI versus NHANES III based spirometry using area under the expiratory flow-volume curve

Background

Spirometry interpretation is influenced by the predictive equations defining lower limit of normal (LLN), while ‘distal’ expiratory flows such as forced expiratory flow at 50% FVC (FEF₅₀) are important functional parameters for diagnosing small airway disease (SAD). Area under expiratory flow-volume curve (AEX) or its approximations have been proposed as supplemental spirometric assessment tools. We compare here the performance of AEX in differentiating between normal, obstruction, restriction, mixed defects and SAD, as defined by Global Lung Initiative (GLI) or National Health and Nutrition Examination Survey (NHANES) III reference values, and using various predictive equations for FEF₅₀.

Methods

We analysed 15 308 spirometry-lung volume tests. Using GLI versus NHANES III LLNs, and diagnosing SAD by the eight most common equation sets for forced expiratory flow at 50% of vital capacity lower limits of normal (FEF_{50 LLN}), we assessed the degree of diagnostic concordance and the ability of AEX to differentiate between various definition-dependent patterns.

Results

Concordance rates between NHANES III and GLI-based classifications were 93.7%, 78.6%, 86.8%, 88.0%, 93.8% and 98.8% in those without, with mild, moderate, moderately severe, severe and very severe obstruction, respectively (agreement coefficient 0.81 (0.80–0.82)). The prevalence of SAD was 0.6%–6.9% of the cohort, depending on the definition used. The AEX differentiated well between normal, obstruction, restriction, mixed pattern and SAD, as defined by most equations.

Conclusions

If the SAD diagnosis is established by using mean FEF_{50 LLN} or a set number of predictive equations, AEX is able to differentiate well between various spirometric patterns. Using the most common predictive equations (NHANES III and GLI), the diagnostic concordance for functional type and obstruction severity is high.

Area under the expiratory flow-volume curve (AEX): actual versus approximated values

Previous work has shown that area under the expiratory flow-volume curve (AEX) performs well in diagnosing and stratifying respiratory physiologic impairment, thereby lessening the need to measure lung volumes. Extending this prior work, the current study assesses the accuracy and utility of several geometric approximations of AEX based on standard instantaneous flows. These approximations can be used in spirometry interpretation when actual AEX measurements are not available. We analysed 15 308 spirometry tests performed on subjects who underwent same-day lung volume assessments in the Pulmonary Function Laboratory. Diagnostic performance of four AEX approximations (AEX_1-4) was compared with that of actual AEX. All four computations included forced vital capacity (FVC) and various instantaneous flows: AEX₁ was derived from peak expiratoryflow (PEF); AEX₂ from PEF and forced expiratoryflow at 50% FVC (FEF₅₀); AEX₃ from FVC, PEF, FEF at 25% FVC (FEF₂₅) and at 75% FVC (FEF₇₅), while AEX₄ was computed from all four flows, PEF, FEF₂₅, FEF₅₀ and FEF₇₅ Mean AEX, AEX₁, AEX₂, AEX₃ and AEX₄ were 6.6, 8.3, 6.7, 6.3 and 6.1 L²/s, respectively. All four approximations had strong correlations with AEX, that is, 0.95-0.99. Differences were the smallest for AEX-AEX₄, with a mean of 0.52 (95% CI 0.51 to 0.54) and a SD of 0.75 (95% CI 0.74 to 0.76) L²/s. In the absence of AEX and in addition to the usual spirometric variables used for assessing functional impairments, parameters such as AEX₄ can provide reasonable approximations of AEX and become useful new tools in future interpretative strategies.

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.