Exploration of quality criteria for the detection of peak inspiratory flow under different resistance conditions.

BACKGROUND: At present, robust quality criteria and methods for the assessment of Peak inspiratory flow meter performance are lacking. OBJECTIVE: A standard flow-volume simulator for quality control analyses of an inhalation assessment device was utilized with different simulated resistance levels in order to propose a quality testing method and associated standard for this device type. METHODS: A standard flow-volume simulator was utilized to assess the performance of an In-Check DIAL® (Device I) and an intelligent inhalation assessment device (Device P) at a fixed volume and flow rate. Indices used to evaluate these two instruments included repeatability, accuracy, linearity, and impedance. RESULTS: Both devices exhibited good repeatability (<± 3 L/min). The difference between test results and standard simulator values for Device P was less than ± 5 L/min at resistance level R1 but higher than ± 5 L/min at resistance levels R2-5, while Device I were greater than 5 L/min at all resistance levels. The relative error for Device P was <± 10% at resistance levels R1, R2, and R4, but > 10% at resistance levels R3 and R5. The relative error values for Device I at all five resistance levels were > 10%. Device P passed the linearity test at the R2 resistance level, while Device I partially passed the linearity test at all five resistance levels. CONCLUSION: Standard monitoring methods and standards provide a valuable approach to the more reliable clinical assessment and application of these instruments.

Performance testing for different peak expiratory flow meters.

BACKGROUND: Peak expiratory flow meters (PEFMs) have emerged as primary tools used for diagnosing and monitoring a range of respiratory diseases including asthma and chronic obstructive pulmonary disease, and the performance of these meters will thus impact disease evaluation. OBJECTIVE: The aim of this study was therefore to assess the technical performance of mechanical and electronic PEFMs commonly used in clinical practice. METHODS: The accuracy, repeatability, airflow resistance, frequency response, and linearity of five electronic and seven mechanical PEFMs were measured using a standard flow/volume simulator in accordance with nine A-waveforms and three B-waveforms defined in ISO 23747:2015 issued by the International Standards Organization (ISO). RESULTS: The accuracy, repeatability, linearity, airflow resistance, and frequency response pass rates for these 12 different PEFM brands were 41.67%, 75.00%, 50.00%, 75.00%, and 25.00%, respectively. Just 16.67% (2/12) of the tested PEFMs met all evaluated criteria, whereas the remaining PEFMs partially met these criteria. There were no significant differences between the two tested PEFM types in the low flow rate waveform test (P> 0.05), although there were significant differences in the medium and high flow rate waveform test (P< 0.05). In addition, the overall PEFMs test had poor accuracy and good repeatability, although most of the repeatability errors occurred in the BTPS state. CONCLUSION: PEFMs commonly used in clinical settings exhibit variable technical performance, and relevant departments need to strengthen PEFM quality control and management in China.

Quality inspection and result analysis of the spirometer calibration cylinder.

BACKGROUND: To understand the accuracy of volume calibration syringes used in China and compare the difference between new and old volume calibration syringes, technical testing was performed on volume calibration syringes in clinical lung function instruments. MATERIALS AND METHODS: A standard validator device (Model 1180, Hans Rudolph, USA) was used to perform leak testing and volume accuracy testing for calibration syringes. Sixteen volume calibration syringes from 8 brands (CareFusion in Germany, Vyaire in Germany, Yaeger in Germany, Vitalograph in the United Kingdom, MGC Diagnostics in the United States, U-Breath in Zhejiang, China, Wendi in Ningbo, Zhejiang, and Boya in Ningbo, China) were tested. RESULTS: A total of 75% (12/16) of the volume calibration syringes passed the pressure decay leak test, 69% (11/16) of the volume calibration syringes passed the volume accuracy and repeatability test, and 56% (9/16) passed both tests; there was no significant difference in the total passing of the new and old volume calibration syringe quality tests (P > 0.05). CONCLUSIONS: A standard validator device should be used for both leakage tests and volume accuracy and repeatability tests to ensure the reliability of volume calibration syringes. It is suggested that the quality verification of volume calibration syringes should be regularly conducted to ensure the accuracy of the pulmonary function tests.

Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve.

BACKGROUND: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians. METHODS: Spirometry records were reviewed to detect the possible condition of airway stenosis. Then they were confirmed by the gold standard (e.g., computed tomography, endoscopy, or clinic diagnosis of upper airway obstruction). Images and indices derived from flow-volume curves were used for training and testing the model. Clinicians determined cases using spirometry records from the test set. The deep learning model evaluated the same data. RESULTS: Of 45,831 patients' spirometry records, 564 subjects with curves suggesting upper airway obstruction, after verified by the gold standard, 351 patients were confirmed. These cases and another 200 cases without airway stenosis were used as the training and testing sets. 432 clinicians evaluated 20 cases of each of the three patterns and 20 no airway stenosis cases (n = 80). They assigned an accuracy of 41.2% (±15.4) (interquartile range: 27.5-52.5%), with poor agreements (κ = 0.12). For the same cases, the model generated a correct detection of 81.3% (p < 0.0001). CONCLUSIONS: Deep learning could detect upper airway obstruction patterns from other classic patterns of ventilatory defects with high accuracy, whereas clinicians presented marked errors and variabilities. The model may serve as a support tool to enhance clinicians' correct diagnosis of upper airway obstruction using spirometry.

Deep learning for spirometry quality assurance with spirometric indices and curves.

BACKGROUND: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitive deep learning-based model aiming at assisting high-quality spirometry assurance. METHODS: Spirometry PDF files retrieved from one hospital between October 2017 and October 2020 were labeled according to ATS/ERS 2019 criteria and divided into training and internal test sets. Additional files from three hospitals were used for external testing. A deep learning-based model was constructed and assessed to determine acceptability, usability, and quality rating for FEV1 and FVC. System warning messages and patient instructions were also generated for general practitioners (GPs). RESULTS: A total of 16,502 files were labeled. Of these, 4592 curves were assigned to the internal test set, the remaining constituted the training set. In the internal test set, the model generated 95.1%, 92.4%, and 94.3% accuracy for FEV1 acceptability, usability, and rating. The accuracy for FVC acceptability, usability, and rating were 93.6%, 94.3%, and 92.2%. With the assistance of the model, the performance of GPs in terms of monthly percentages of good quality (A, B, or C grades) tests for FEV1 and FVC was higher by ~ 21% and ~ 36%, respectively. CONCLUSION: The proposed model assisted GPs in spirometry quality assurance, resulting in enhancing the performance of GPs in quality control of spirometry.

Technical performance analysis of different types of spirometers.

BACKGROUND: The spirometer is an important element in lung function examinations, and its accuracy is directly related to the accuracy of the results of these examinations and to the diagnosis and treatment of diseases. Our aim was to conduct a performance analysis of the detection techniques of differential pressure and ultrasonic portable spirometers commonly used in China. METHODS: A standard flow/volume simulator was used to analyze the performance (accuracy, repeatability, linearity, impedance, and so on) of portable spirometers, 4 imported and 6 domestic, based on 13 curves generated by different air sources in the ISO 26782:2009 standard. A Bland-Altman diagram was used to evaluate the consistency between the values measured by the spirometers and the simulator. RESULTS: The pass rates for accuracy, repeatability, linearity, and impedance for the 10 different portable spirometers were 50%, 100%, 70%, and 70%, respectively. Only 30% (3/10) of the spirometers-2 domestic and 1 imported-met all standards of quality and performance evaluation, while the rest were partially up to standard. In the consistency evaluation, only 3 spirometers were within both the consistency standard range and the acceptability range. CONCLUSION: The quality and performance of different types of portable spirometers commonly used in the clinic differ. The use of a standard flow/volume simulator is helpful for the standard evaluation of the technical performance of spirometers.

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.