Browsing by Subject "spirometry"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
    (Frontiers in physiology, 2021-01) Giri, Paresh C; Chowdhury, Anand M; Bedoya, Armando; Chen, Hengji; Lee, Hyun Suk; Lee, Patty; Henriquez, Craig; MacIntyre, Neil R; Huang, Yuh-Chin T
    Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert's pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual's clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.
  • Thumbnail Image
    ItemOpen Access
    Association of quality-of-care indicators with asthma outcomes: A retrospective observational study for asthma care in Singapore
    (Annals of the Academy of Medicine Singapore, 2023-10-01) Lam, Sean Shao Wei; Chen, Jingwei; Wu, Jun Tian; Lee, Chun Fan; Ragavendran, Narayanan; Ong, Marcus Eng Hock; Tan, Ngiap Chuan; Loo, Chian Min; Matchar, David Bruce; Koh, Mariko Siyue
    Introduction: Asthma guidelines have advocated for the use of quality-of-care indicators (QCIs) in asthma management. To improve asthma care, it is important to identify effective QCIs that are actionable. This study aimed to evaluate the effect of the presence of 3 QCIs: asthma education, Asthma Control Test (ACT) and spirometry testing on the time to severe exacerbation (TTSE). Method: Data collected from the SingHealth COPD and Asthma Data Mart (SCDM), including asthma patients managed in 9 SingHealth polyclinics and Singapore General Hospital from January 2015 to December 2020, were analysed. Patients receiving Global Initiative for Asthma (GINA) Steps 3–5 treatment, with at least 1 QCI recorded, and at least 1 severe exacerbation within 1 year before the first QCI record, were included. Data were analysed using multivariate Cox regression and quasi-Poisson regression models. Results: A total of 3849 patients in the registry fulfilled the criteria. Patients with records of asthma education or ACT assessment have a lower adjusted hazard ratio (HR) for TTSE (adjusted HR=0.88, P=0.023; adjusted HR=0.83, P<0.001). Adjusted HR associated with spirometry is higher (adjusted HR=1.22, P=0.026). No QCI was significantly associated with emergency department (ED)/inpatient visits. Only asthma education and ACT showed a decrease in the number of exacerbations for multivariate analysis (asthma education estimate:-0.181, P<0.001; ACT estimate:-0.169, P<0.001). No QCI was significant for the number of exacerbations associated with ED/inpatient visits. Conclusion: Our study suggests that the performance of asthma education and ACT was associated with increased TTSE and decreased number of exacerbations, underscoring the importance of ensuring quality care in clinical practice.
  • Thumbnail Image
    ItemOpen Access
    Development and Evaluation of a Small Airway Disease Index Derived From Modeling the Late-Expiratory Flattening of the Flow-Volume Loop.
    (Frontiers in physiology, 2022-01) Chen, Hengji; Joshi, Sangeeta; Oberle, Amber J; Wong, An-Kwok; Shaz, David; Thapamagar, Suman; Tan, Laren; Anholm, James D; Giri, Paresh C; Henriquez, Craig; Huang, Yuh-Chin T
    Excessive decrease in the flow of the late expiratory portion of a flow volume loop (FVL) or "flattening", reflects small airway dysfunction. The assessment of the flattening is currently determined by visual inspection by the pulmonary function test (PFT) interpreters and is highly variable. In this study, we developed an objective measure to quantify the flattening. We downloaded 172 PFT reports in PDF format from the electronic medical records and digitized and extracted the expiratory portion of the FVL. We located point A (the point of the peak expiratory flow), point B (the point corresponding to 75% of the expiratory vital capacity), and point C (the end of the expiratory portion of the FVL intersecting with the x-axis). We did a linear fitting to the A-B segment and the B-C segment. We calculated: 1) the AB-BC angle (∠ABC), 2) BC-x-axis angle (∠BCX), and 3) the log ratio of the BC slope over the vertical distance between point A and x-axis [log (BC/A-x)]. We asked an expert pulmonologist to assess the FVLs and separated the 172 PFTs into the flattening and the non-flattening groups. We defined the cutoff value as the mean minus one standard deviation using data from the non-flattening group. ∠ABC had the best concordance rate of 80.2% with a cutoff value of 149.7°. We then asked eight pulmonologists to evaluate the flattening with and without ∠ABC in another 168 PFTs. The Fleiss' kappa was 0.320 (lower and upper confidence intervals [CIs]: 0.293 and 0.348 respectively) without ∠ABC and increased to 0.522 (lower and upper CIs: 0.494 and 0.550) with ∠ABC. There were 147 CT scans performed within 6 months of the 172 PFTs. Twenty-six of 55 PFTs (47.3%) with ∠ABC <149.7° had CT scans showing small airway disease patterns while 44 of 92 PFTs (47.8%) with ∠ABC ≥149.7° had no CT evidence of small airway disease. We concluded that ∠ABC improved the inter-rater agreement on the presence of the late expiratory flattening in FVL. It could be a useful addition to the assessment of small airway disease in the PFT interpretation algorithm and reporting.