Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition

Background Small plateau (SP) on the flow-volume curve was found in parts of patients with suspected asthma or upper airway abnormalities, but it lacks clear scientific proof. Therefore, we aimed to characterize its clinical features. Methods We involved patients by reviewing the bronchoprovocation test (BPT) and bronchodilator test (BDT) completed between October 2017 and October 2020 to assess the characteristics of the sign. Patients who underwent laryngoscopy were assigned to perform spirometry to analyze the relationship of the sign and upper airway abnormalities. SP-Network was developed to recognition of the sign using flow-volume curves. Results Of 13,661 BPTs and 8,168 BDTs completed, we labeled 2,123 (15.5%) and 219 (2.7%) patients with the sign, respectively. Among them, there were 1,782 (83.9%) with the negative-BPT and 194 (88.6%) with the negative-BDT. Patients with SP sign had higher median FVC and FEV1% predicted (both P < .0001). Of 48 patients (16 with and 32 without the sign) who performed laryngoscopy and spirometry, the rate of laryngoscopy-diagnosis upper airway abnormalities in patients with the sign (63%) was higher than those without the sign (31%) (P = 0.038). SP-Network achieved an accuracy of 95.2% in the task of automatic recognition of the sign. Conclusions SP sign is featured on the flow-volume curve and recognized by the SP-Network model. Patients with the sign are less likely to have airway hyperresponsiveness, automatic visualizing of this sign is helpful for primary care centers where BPT cannot available.

Exploring the 175-year history of spirometry and the vital lessons it can teach us today

175 years have elapsed since John Hutchinson introduced the world to his version of an apparatus that had been in development for nearly two centuries, the spirometer. Though he was not the first to build a device that sought to measure breathing and quantify the impact of disease and occupation on lung function, Hutchison coined the terms spirometer and vital capacity that are still in use today, securing his place in medical history. As Hutchinson envisioned, spirometry would become crucial to our growing knowledge of respiratory pathophysiology, from Tiffeneau and Pinelli's work on forced expiratory volumes, to Fry and Hyatt's description of the flow–volume curve. In the 20th century, standardization of spirometry further broadened its reach and prognostic potential. Today, spirometry is recognized as essential to respiratory disease diagnosis, management and research. However, controversy exists in some of its applications, uptake in primary care remains sub-optimal and there are concerns related to the way in which race is factored into interpretation. Moving forward, these failings must be addressed, and innovations like Internet-enabled portable spirometers may present novel opportunities. We must also consider the physiologic and practical limitations inherent to spirometry and further investigate complementary technologies such as respiratory oscillometry and other emerging technologies that assess lung function. Through an exploration of the storied history of spirometry, we can better contextualize its current landscape and appreciate the trends that have repeatedly arisen over time. This may help to improve our current use of spirometry and may allow us to anticipate the obstacles confronting emerging pulmonary function technologies.

Dynamic airway function during exercise in COPD assessed via impulse oscillometry before and after inhaled bronchodilators.

Introduction. Assessing airway function during exercise provides useful information regarding mechanical properties of the airways and extent of ventilatory limitation in COPD. The primary aim of this study was to use impulse oscillometry (IOS) to assess dynamic changes in airway impedance across a range of exercise intensities in GOLD 1-4 patients, before and after albuterol. A secondary aim was to assess reproducibility of IOS measures during exercise. Methods. Fifteen COPD patients (8 male; age=66±8 y; pre-bronchodilator FEV1=54.3±23.6%Pred) performed incremental cycle ergometry before and 90-min after inhaled albuterol. Pulmonary ventilation and gas exchange were measured continuously, and IOS-derived indices of airway impedance were measured every 2 min immediately preceding inspiratory capacity manoeuvres. Test-retest reproducibility of exercise IOS was assessed as mean difference between replicate tests in five healthy subjects (3 male). Results. At rest and during exercise, albuterol significantly increased airway reactance (X5), and decreased airway resistance (R5, R5-20), impedance (Z5), and end-expiratory lung volume (60±12 vs. 58±12%TLC, main effect p=0.003). At peak exercise, there were moderate-to-strong associations between IOS variables and IC, and between IOS and concavity in the expiratory limb of the flow-volume curve. Exercise IOS exhibited moderate reproducibility in healthy subjects which was strongest with R5 (mean diff. -0.01±0.05 kPa/L/s; ICC=0.68), R5-20 (mean diff. -0.004±0.028 kPa/L/s; ICC=0.65), and Z5 (mean diff. -0.006±0.021 kPa/L/s; ICC=0.69). Conclusions. Exercise evoked increases in airway resistance and decreases in reactance that were ameliorated by inhaled bronchodilators. The technique of exercise IOS may aid in the clinical assessment of dynamic airway function during exercise.

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

BackgroundIn 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.MethodsWe 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.ResultsMedian/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.ConclusionsIn 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.

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.