Correlation of digital flow peak with spirometry in children with and without asthma

Maia, Paula; Anna, Maria de Fátima Bazhuni Pombo Sant; Parente, Ana Alice Amaral Ibiapina; Aurílio, Rafaela Baroni; Silva, Beatriz Albino Servilha; Luiz, Ronir Raggio; ́Anna, Clemax Couto Sant

doi:10.1080/02770903.2022.2045308

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Perhaps, this suggest that ePFMs have comparable findings to spirometers, as shown by studies that compared ePFMs and spirometers. 16,17 Compared with Nigerian studies where PEF was measured with mini-Wright mPFM, our PEF was higher than 212 L/min reported by Agaba et al 9 among Nigerian children Jos plateau (Northern Nigeria) and 216 L/min reported by Adeniyi 8 in Abuja (North-central Nigeria), but lower than 257 L/min reported by Mohammed et al 37 in Kano (Northern Nigeria). These variations may reflect device types, varying inclusion/exclusion criteria, methodological variations or true geographical differences.…”

Section: Mean Pef and Fev 1 Values Of Nigerian Childrencontrasting

confidence: 59%

“…2,3,14,15 Thus, electronic or digital peak flow meters (ePFMs) which measure both PEF and FEV 1 and have inbuilt quality checks may provide better diagnostic precision than the more ubiquitous mPFMs, especially in the absence of the more expensive spirometers. [16][17][18][19] However, the use of ePFMs may be limited because REs of PEF and FEV 1 specifically measured with ePFM are not available; those measured with mPFMs or spirometers may not be appropriate for ePFMs since device-type is an independent determinant of the fit of REs to a given populations, with consequent risk of mis-classification, mis-diagnosis and over-/under-treatment. 20,21 To determine the REs most appropriate for clinical use of ePFM for LF measurement among Nigerian school-age children (6-11 years), we compared ePFM-measured PEF and FEV 1 with local and foreign equations, including the multi-ethnic Global Lung Function Initiative (GLI) equations comprising four racespecific equations (GLI-African-American , GLI-Caucasian ,GLI-Southeast-Asian , GLI-Northeast-Asian ) and two race-neutral/-composite equations-GLI-Others/mixed and a newGLI-Global now recommended by the American Thoracic Society (ATS) for global bench-marking of LF.…”

Section: Introductionmentioning

confidence: 99%

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TITLE: Reference lung function of Nigerian school-age children measured with electronic peak flow meter: comparison with local and Global-Lung-Initiative equations

Ubuane,

Akinola,

Ajiboye

et al. 2023

Preprint

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Introduction. Spirometric reference equations (REs) assists diagnosing, monitoring and prognosticating respiratory diseases. However, spirometers are scarce in respiratory care in low-resource settings, hampering optimal care. Cheaper, simpler-to-use electronic peak-flow-meters (ePFM ) which measure both peak expiratory flow (PEF) and forced expiratory volume in one-second (FEV 1 ) may serve alternative utility. However, their use requires REsfitted to local populations. We compare ePFM-measured PEF and FEV 1 of Nigerian children with published local/foreign REs, including the Global-Lung-Function-Initiative (GLI) equations.Methods. PEF and FEV 1 of healthy pupils aged 6-11 years in Lagos-Nigeria was cross-sectionally measured with ePFM (Asma-1, Vitalograph, UK). PEF was compared to published local and foreign equations using Bland-and-Altman analysis. Goodness-of-fit of FEV 1 to GLI-equations was defined as mean (SD) predicted z-scores < 0.5 (1.0), 5% z-scores < -1.64 (% lower-limit-of-normal, LLN), 5% > 1.64 (% upper-limit-of-normal, ULN).Results. 766 children [mean (SD) age: 8.9 (1.6) years; 53.1% girls] achieved mean (SD) PEF and FEV 1 of 235.8 (52.0) L/min and 1.40 (0.34) L; higher in boys (p < 0.001), and strongly correlated with height (r=0.73-0.86), lower-limb-length (r=0.67-081), weight (r=0.62-0.76), age (r=0.60-0.66), upper-segmentvolume (r=0.58-0.69), upper-segment-length (r = 0.54-0.61) and chest-circumference (r=0.52-0.66). PEF agreed poorly with local and foreign equations, with wide limits-of-agreement.GLI-African-American equations fitted our FEV 1 [mean (SD; %LLN, %ULN)-girls: -0.17 (1.10; 9.1%, 5.4%), boys: 0.03 (1.11; 7.2%, 7.5%)]; GLI-global and GLI-othersdid not fit. Conclusion:Our FEV 1 fitted GLI-AA, albeit less so for girls. The most appropriate GLI FEV 1 equations for our population require bench-marking with clinical outcomes.

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Section: Mean Pef and Fev 1 Values Of Nigerian Childrencontrasting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

TITLE: Reference lung function of Nigerian school-age children measured with electronic peak flow meter: comparison with local and Global-Lung-Initiative equations

Ubuane,

Akinola,

Ajiboye

et al. 2023

Preprint

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show abstract

“…Several studies also described updates in pulmonary function testing. Researchers performed a cross-sectional study to compare newer digital peak flow meters (DPM) to conventional spirometry 12 . Specifically, the authors compared forced expiratory volumes in the first second (FEV1) and peak expiratory flow measurement (PEF) for 125 children without asthma and 196 children with asthma.…”

Section: New Methods For Diagnostic Testingmentioning

confidence: 99%

Pediatric Pulmonology 2023 Year in Review: Asthma

Espaillat,

Stephenson,

Loughlin

2024

Preprint

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Caring for children with asthma continues to be an essential responsibility of the pediatric pulmonologist. In this Pediatric Pulmonology Year in Review, we summarize the significant publications focused on pediatric asthma that have been published over the last year. We will discuss new findings that assist in the diagnosis and evaluation of pediatric asthma, highlight risk factors and social determinants that impact successful care, and discuss new interventions and treatment modalities for acute exacerbations and maintenance control. We hope this review can provide new insights and guidance for implementation into clinical practice within the field of pediatric asthma.

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Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Koul

Bawa²,

Kumar³

2022

Arch Computat Methods Eng

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Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

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Correlation of digital flow peak with spirometry in children with and without asthma

Cited by 4 publications

References 23 publications

TITLE: Reference lung function of Nigerian school-age children measured with electronic peak flow meter: comparison with local and Global-Lung-Initiative equations

TITLE: Reference lung function of Nigerian school-age children measured with electronic peak flow meter: comparison with local and Global-Lung-Initiative equations

Pediatric Pulmonology 2023 Year in Review: Asthma

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

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