Bronchodilator responsiveness in wheezy infants and toddlers is not associated with asthma risk factors

Background There are limited data assessing bronchodilator responsiveness (BDR) in infants and toddlers with recurrent wheezing, and factors associated with a positive response. Objectives In a multicenter study of children ≤ 36 months old, we assessed the prevalence of and factors associated with BDR among infants/toddlers with recurrent episodes of wheezing. Methods Forced expiratory flows and volumes using the raised‐volume rapid thoracic compression method were measured in 76 infants/toddlers [mean (SD) age 16.8 (7.6) months] with recurrent wheezing before and after administration of albuterol. Prior history of hospitalization or emergency department treatment for wheezing, use of inhaled or systemic corticosteroids, physician treatment of eczema, environmental tobacco smoke exposure, and family history of asthma or allergic rhinitis were ascertained. Results Using the published upper limit of normal for post bronchodilator change (FEV 0.5  ≥ 13% and/or FEF 25–75  ≥ 24%) in healthy infants, 24% (n = 18) of children in our study exhibited BDR. The BDR response was not associated with any clinical factor other than body size. Dichotomizing subjects into responders (defined by published limits of normal) or by quartile to identify children with the greatest change from baseline (4th quartile vs. other) did not identify any other factor associated with BDR. Conclusions Approximately one quarter of infants/toddlers with recurrent wheezing exhibited BDR at their clinical baseline. However, BDR in wheezy infants/toddlers was not associated with established clinical asthma risk factors. Pediatr Pulmonol. 2012; 47:421–428. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91214/1/21567_ftp.pd

Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies

Data Availability: The data that support the findings of this study are available from Great Ormond Street Hospital, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Great Ormond Street Hospital.Copyright © The Author(s) 2023. Background: Clinical outcomes are normally captured less frequently than data from remote technologies, leaving a disparity in volumes of data from these different sources. To align these data, flexible polynomial regression was investigated to estimate personalised trends for a continuous outcome over time. Methods: Using electronic health records, flexible polynomial regression models inclusive of a 1st up to a 4th order were calculated to predict forced expiratory volume in 1 s (FEV1) over time in children with cystic fibrosis. The model with the lowest AIC for each individual was selected as the best fit. The optimal parameters for using flexible polynomials were investigated by comparing the measured FEV1 values to the values given by the individualised polynomial. Results: There were 8,549 FEV1 measurements from 267 individuals. For individuals with > 15 measurements (n = 178), the polynomial predictions worked well; however, with < 15 measurements (n = 89), the polynomial models were conditional on the number of measurements and time between measurements. The method was validated using BMI in the same population of children. Conclusion: Flexible polynomials can be used to extrapolate clinical outcome measures at frequent time intervals to align with daily data captured through remote technologies.UCL, GOSH and Toronto SickKids studentship. GD is supported by a Future Leaders Fellowship from UK Research & Innovation (UKRI), Grant reference: MR/T041285. All research at Great Ormond Street Hospital NHS Foundation Trust and UCL Great Ormond Street Institute of Child Health is made possible by the NIHR Great Ormond Street Hospital Biomedical Research Centre

Unsupervised phenotypic clustering for determining clinical status in children with cystic fibrosis

BACKGROUND: Cystic Fibrosis (CF) is a multisystem disease in which assessing disease severity based on lung function alone may not be appropriate. The aim of the study was to develop a comprehensive machine-learning algorithm to assess clinical status independent of lung function in children. METHODS: A comprehensive prospectively collected clinical database (Toronto, Canada) was used to apply unsupervised cluster analysis. The defined clusters were then compared by current and future lung function, risk of future hospitalisation, and risk of future pulmonary exacerbation (PEx) treated with oral antibiotics. A K-Nearest Neighbours (KNN) algorithm was used to prospectively assign clusters. The methods were validated in a paediatric clinical CF dataset from Great Ormond Street Hospital (GOSH). RESULTS: The optimal cluster model identified four (A-D) phenotypic clusters based on 12 200 encounters from 530 individuals. Two clusters (A,B) consistent with mild disease were identified with high FEV1, and low risk of both hospitalisation and PEx treated with oral antibiotics. Two clusters (C,D) consistent with severe disease were also identified with low FEV1. Cluster D had the shortest time to both hospitalisation and PEx treated with oral antibiotics. The outcomes were consistent in 3124 encounters from 171 children at GOSH. The KNN cluster allocation error rate was low, at 2.5% (Toronto), and 3.5% (GOSH). CONCLUSION: Machine learning derived phenotypic clusters can predict disease severity independent of lung function and could be used in conjunction with functional measures to predict future disease trajectories in CF patients

GLI equations improve interpretation of FEV1 decline among patients with cystic fibrosis

No abstract for this research letter

Real-world effectiveness of airway clearance techniques in children with cystic fibrosis

Data Availability Statement - The study protocol is published open access. De-identified participant data are hosted in a secure DRE through GOSH DRIVE (www.goshdrive.com). Access to the data, data dictionary and informed consent forms through the DRE is available with permission from the corresponding author.Background Cystic fibrosis (CF) is commonly characterised by thick respiratory mucus. From diagnosis, people with CF are prescribed daily physiotherapy, including airway clearance techniques (ACTs). ACTs consume a large proportion of treatment time, yet the efficacy and effectiveness of ACTs are poorly understood. This study aimed to evaluate associations between the quality and quantity of ACTs and lung function in children and young people with CF. Methods Project Fizzyo, a longitudinal observational cohort study in the UK, used remote monitoring with electronic pressure sensors attached to four different commercial ACT devices to record real-time, breath-by-breath pressure data during usual ACTs undertaken at home over 16 months in 145 children. ACTs were categorised either as conformant or not with current ACT recommendations based on breath pressure and length measurements, or as missed treatments if not recorded. Daily, weekly and monthly associations between ACT category and lung function were investigated using linear mixed effects regression models adjusting for clinical confounders. Results After exclusions, 45 224 ACT treatments (135 individuals) and 21 069 days without treatments (141 individuals) were analysed. The mean±SD age of participants was 10.2±2.9 years. Conformant ACTs (21%) had significantly higher forced expiratory volume in 1 s (FEV1) (mean effect size 0.23 (95% CI 0.19–0.27) FEV1 % pred per treatment) than non-conformant (79%) or missed treatments. There was no benefit from non-conformant or missed treatments and no significant difference in FEV1 between them (mean effect size 0.02 (95% CI −0.01–0.05) FEV1 % pred per treatment). Conclusions ACTs are beneficial when performed as recommended, but most people use techniques that do not improve lung function. Work is needed to monitor and improve ACT quality and to increase the proportion of people doing effective airway clearance at home.UK Research and Innovation MR/T041285/1 Rosetrees Trust M712 Cystic Fibrosis Trust CEA010 Great Ormond Street Hospital for Children Higher Education Funding Council for England KEI2017–01–04 Hospital for Sick Children University College London Partners Awar

Peripheral blood marker of residual acute leukemia after hematopoietic cell transplantation using multi-plex digital droplet PCR

BACKGROUND Relapse remains the primary cause of death after hematopoietic cell transplantation (HCT) for acute leukemia. The ability to identify minimal/measurable residual disease (MRD) via the blood could identify patients earlier when immunologic interventions may be more successful. We evaluated a new test that could quantify blood tumor mRNA as leukemia MRD surveillance using droplet digital PCR (ddPCR). METHODS The multiplex ddPCR assay was developed using tumor cell lines positive for the tumor associated antigens (TAA: WT1, PRAME, BIRC5), with homeostatic ABL1. On IRB-approved protocols, RNA was isolated from mononuclear cells from acute leukemia patients after HCT (n = 31 subjects; n = 91 specimens) and healthy donors (n = 20). ddPCR simultaneously quantitated mRNA expression of WT1, PRAME, BIRC5, and ABL1 and the TAA/ABL1 blood ratio was measured in patients with and without active leukemia after HCT. RESULTS Tumor cell lines confirmed quantitation of TAAs. In patients with active acute leukemia after HCT (MRD+ or relapse; n=19), the blood levels of WT1/ABL1, PRAME/ABL1, and BIRC5/ABL1 exceeded healthy donors (p<0.0001, p=0.0286, and p=0.0064 respectively). Active disease status was associated with TAA positivity (1+ TAA vs 0 TAA) with an odds ratio=10.67, (p=0.0070, 95% confidence interval 1.91 - 59.62). The area under the curve is 0.7544. Changes in ddPCR correlated with disease response captured on standard of care tests, accurately denoting positive or negative disease burden in 15/16 (95%). Of patients with MRD+ or relapsed leukemia after HCT, 84% were positive for at least one TAA/ABL1 in the peripheral blood. In summary, we have developed a new method for blood MRD monitoring of leukemia after HCT and present preliminary data that the TAA/ABL1 ratio may may serve as a novel surrogate biomarker for relapse of acute leukemia after HCT

Formation of regulatory modules by local sequence duplication

Turnover of regulatory sequence and function is an important part of molecular evolution. But what are the modes of sequence evolution leading to rapid formation and loss of regulatory sites? Here, we show that a large fraction of neighboring transcription factor binding sites in the fly genome have formed from a common sequence origin by local duplications. This mode of evolution is found to produce regulatory information: duplications can seed new sites in the neighborhood of existing sites. Duplicate seeds evolve subsequently by point mutations, often towards binding a different factor than their ancestral neighbor sites. These results are based on a statistical analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome, and a comparison set of intergenic regulatory sequence in Saccharomyces cerevisiae. In fly regulatory modules, pairs of binding sites show significantly enhanced sequence similarity up to distances of about 50 bp. We analyze these data in terms of an evolutionary model with two distinct modes of site formation: (i) evolution from independent sequence origin and (ii) divergent evolution following duplication of a common ancestor sequence. Our results suggest that pervasive formation of binding sites by local sequence duplications distinguishes the complex regulatory architecture of higher eukaryotes from the simpler architecture of unicellular organisms

Global asthma prevalence in adults: findings from the cross-sectional world health survey

<p>Abstract</p> <p>Background</p> <p>Asthma is a major cause of disability, health resource utilization and poor quality of life world-wide. We set out to generate estimates of the global burden of asthma in adults, which may inform the development of strategies to address this common disease.</p> <p>Methods</p> <p>The World Health Survey (WHS) was developed and implemented by the World Health Organization in 2002-2003. A total of 178,215 individuals from 70 countries aged 18 to 45 years responded to questions related to asthma and related symptoms. The prevalence of asthma was based on responses to questions relating to self-reported doctor diagnosed asthma, clinical/treated asthma, and wheezing in the last 12 months.</p> <p>Results</p> <p>The global prevalence rates of doctor diagnosed asthma, clinical/treated asthma and wheezing in adults were 4.3%, 4.5%, and 8.6% respectively, and varied by as much as 21-fold amongst the 70 countries. Australia reported the highest rate of doctor diagnosed, clinical/treated asthma, and wheezing (21.0%, 21.5%, and 27.4%). Amongst those with clinical/treated asthma, almost 24% were current smokers, half reported wheezing, and 20% had never been treated for asthma.</p> <p>Conclusions</p> <p>This study provides a global estimate of the burden of asthma in adults, and suggests that asthma continues to be a major public health concern worldwide. The high prevalence of smoking remains a major barrier to combating the global burden of asthma. While the highest prevalence rates were observed in resource-rich countries, resource-poor nations were also significantly affected, posing a barrier to development as it stretches further the demands of non-communicable diseases.</p

Quantity and quality of airway clearance in children and young people with cystic fibrosis

Supplementary materials are online at: https://www.sciencedirect.com/science/article/pii/S1569199322006865#sec0016 .Children and young people with CF (CYPwCF) get advice about using positive expiratory pressure (PEP) or oscillating PEP (OPEP) devices to clear sticky mucus from their lungs. However, little is known about the quantity (number of treatments, breaths, or sets) or quality (breath pressures and lengths) of these daily airway clearance techniques (ACTs) undertaken at home. This study used electronic pressure sensors to record real time breath-by-breath data from 145 CYPwCF (6–16y) during routine ACTs over 2 months. ACT quantity and quality were benchmarked against individual prescriptions and accepted recommendations for device use. In total 742,084 breaths from 9,081 treatments were recorded. Individual CYPwCF maintained consistent patterns of ACT quantity and quality over time. Overall, 60% of CYPwCF did at least half their prescribed treatments, while 27% did fewer than a quarter. About 77% of pre-teens did the right number of daily treatments compared with only 56% of teenagers. CYPwCF usually did the right number of breaths. ACT quality (recommended breath length and pressure) varied between participants and depended on device. Breath pressures, lengths and pressure-length relationships were significantly different between ACT devices. PEP devices encouraged longer breaths with lower pressures, while OPEP devices encouraged shorter breaths with higher pressures. More breaths per treatment were within advised ranges for both pressure and length using PEP (30–31%) than OPEP devices (1–3%). Objective measures of quantity and quality may help to optimise ACT device selection and support CYPwCF to do regular effective ACTs.Project Fizzyo was supported by the UCL Rosetrees Stoneygate prize (M712), a Cystic Fibrosis Trust Clinical Excellence and Innovation Award (CEA010), A UCL Partners award and the HEFCE Higher Education Innovation Fund (KEI2017–01–04). HD was funded by the CF Trust Youth Activity Unlimited SRC and an NIHR GOSH BRC internship. All work at UCL GOSICH is supported by the NIHR GOSH BRC

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies