Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

Mondal, Pritish; Yirinec, Alison; Midya, Vishal; Sankoorikal, Binu John; Smink, Gayle; Khokhar, Arshjot; Abu-Hasan, Mutasim; Bascom, Rebecca

doi:10.1002/ppul.24382

Cited by 21 publications

(19 citation statements)

References 34 publications

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“…100 Although it's not an adult-based study, a group of researchers from Pennsylvania found that oscillometry was more feasible than spirometry in children with Sickle-Cell disease, because it can be performed on children of young age, and it has greater sensibility to bronchodilatationresponse. 101…”

Section: Neuromuscular Diseasesmentioning

confidence: 99%

<p>Lung Function Assessment by Impulse Oscillometry in Adults</p>

Porojan-Suppini¹,

Fira-Mlădinescu²,

Marc³

et al. 2020

TCRM

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Over the past decades, impulse oscillometry (IOS) has gained ground in the battery of pulmonary function tests. Performing the test requires minimal cooperation of the patient; therefore, it is a useful tool, especially in evaluating lung mechanics in children, elderly patients, and those who cannot perform spirometry. Oscillometry has also been used in both clinical and research departments. Studies were published mainly in asthma regarding detection of bronchodilator response and the therapeutic response to different drugs. Furthermore, it has been shown to be a sensitive technique to evaluate disease control. Other studied diseases were COPD, interstitial lung diseases, small airway disease, impairment of lung function due to exposure to occupational hazards or smoking, central airways obstruction, cystic fibrosis, monitoring lung mechanics during mechanical ventilation and sleep, neuromuscular diseases, lung transplant, and graft function. The aim of this review is to present the utility of oscillometry on the previously mentioned clinical fields.

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Section: Neuromuscular Diseasesmentioning

confidence: 99%

<p>Lung Function Assessment by Impulse Oscillometry in Adults</p>

Porojan-Suppini¹,

Fira-Mlădinescu²,

Marc³

et al. 2020

TCRM

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“…Machine learning (ML) is an algorithm-based novel modeling technique that has been introduced recently to select key behavior features (biomarkers) and predict risk levels [29]. ML methods are more precise and accurate in terms of prediction abilities compared with traditional statistical methods, because complex intervariable interactions are taken into account in ML only [30].…”

Section: Application Of Different ML Algorithmsmentioning

confidence: 99%

Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease

Spiga

Cicaloni

Fiorini³

et al. 2020

Orphanet J Rare Dis

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Background Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase (HGD) gene. One of the main obstacles in studying AKU, and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Quality of Life scores (QoL) are a reliable way to monitor patients’ clinical condition and health status. QoL scores allow to monitor the evolution of diseases and assess the suitability of treatments by taking into account patients’ symptoms, general health status and care satisfaction. However, more comprehensive tools to study a complex and multi-systemic disease like AKU are needed. In this study, a Machine Learning (ML) approach was implemented with the aim to perform a prediction of QoL scores based on clinical data deposited in the ApreciseKUre, an AKU- dedicated database. Method Data derived from 129 AKU patients have been firstly examined through a preliminary statistical analysis (Pearson correlation coefficient) to measure the linear correlation between 11 QoL scores. The variable importance in QoL scores prediction of 110 ApreciseKUre biomarkers has been then calculated using XGBoost, with K-nearest neighbours algorithm (k-NN) approach. Due to the limited number of data available, this model has been validated using surrogate data analysis. Results We identified a direct correlation of 6 (age, Serum Amyloid A, Chitotriosidase, Advanced Oxidation Protein Products, S-thiolated proteins and Body Mass Index) out of 110 biomarkers with the QoL health status, in particular with the KOOS (Knee injury and Osteoarthritis Outcome Score) symptoms (Relative Absolute Error (RAE) 0.25). The error distribution of surrogate-model (RAE 0.38) was unequivocally higher than the true-model one (RAE of 0.25), confirming the consistency of our dataset. Our data showed that inflammation, oxidative stress, amyloidosis and lifestyle of patients correlates with the QoL scores for physical status, while no correlation between the biomarkers and patients’ mental health was present (RAE 1.1). Conclusions This proof of principle study for rare diseases confirms the importance of database, allowing data management and analysis, which can be used to predict more effective treatments.

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“…In adults, IOS was found to be correlated with spirometry results but was not found to be correlated with asthma control 20 . Measurements of R5Hz and AX have been found to be more sensitive to bronchodilator response compared to FEV1 values in children 21 . Similarly, in our study asthma control was significantly worse in overweight/obese children but this was not reflected on the IOS measurements of resistence.…”

Section: Discussionmentioning

confidence: 98%

Impulse Oscillometric Evaluation of Airway Resistance in Overweight and Obese Children With Asthma

Caliskan

ksel

Yaşar

et al. 2020

Preprint

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Background: Change in airway resistance and inflammation are the two most commonly accused pathogenetic processes in the coexistence of asthma and obesity. The aim of this study was to compare airway resistance between normal weight and overweight/obese children using impulse oscillometry (IOS). Methods: We consecutively enrolled 3 to 10-year-old children with asthma who presented to our department between May and August 2018. Ones with a body mass index percentile at or above 85th percentile were grouped as overweight/obese. Sociodemographic characteristics such as age, sex and family history of allergy were recorded and Test for Respiratory and Asthma Control in Kids (TRACK) was filled in for all. Finally, impulse oscillometric (IOS) measurements were taken. Results: We enrolled 170 subjects; among which 114 (67.1%) were normal weight while 56 (33%) were overweight/obese. Sociodemographic characteristics were not significantly different between the two groups, but asthma control was significantly worse in the overweight/obese group (75.9±21.1 vs 85.9±17.8, p=0.001). Impulse oscillometric measurements of resistance and reactance were nor significantly different between the two groups. Conclusion: Asthma control in children who are overweight or obese are worse compared to the normal weight ones but, airway resistance and reactance measured by IOS at different frequencies are not significantly different between these two groups. This implies that airway resistance change may not the main pathogenetic mechanism underlying the uncontrolled asthma and obesity coexistence.

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Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

Cited by 21 publications

References 34 publications

<p>Lung Function Assessment by Impulse Oscillometry in Adults</p>

<p>Lung Function Assessment by Impulse Oscillometry in Adults</p>

Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease

Impulse Oscillometric Evaluation of Airway Resistance in Overweight and Obese Children With Asthma

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