Using reference equations to standardise incremental shuttle walk test performance in children and young people with chronic conditions and facilitate the evaluation of exercise capacity and disease severity.

AIMS: The aim was to evaluate whether standardised exercise performance during the incremental shuttle walk test (ISWT) can be used to assess disease severity in children and young people (CYP) with chronic conditions, through (1) identifying the most appropriate paediatric normative reference equation for the ISWT, (2) assessing how well CYP with haemophilia and cystic fibrosis (CF) perform against the values predicted by the best fit reference equation and (3) evaluating the association between standardised ISWT performance and disease severity. METHODS: A cross-sectional analysis was carried out using existing data from two independent studies (2018-2019) at paediatric hospitals in London,UK. CYP with haemophilia (n=35) and CF (n=134) aged 5-18 years were included. Published reference equations for standardising ISWT were evaluated through a comparison of populations, and Bland-Altman analysis was used to assess the level of agreement between distances predicted by each equation. Associations between ISWT and disease severity were assessed with linear regression. RESULTS: Three relevant reference equations were identified for the ISWT that standardised performance based on age, sex and body mass index (Vardhan, Lanza, Pinho). A systematic proportional bias of standardised ISWT was observed in all equations, most pronounced with Vardhan and Lanza; the male Pinho equation was identified as most appropriate. On average, CYP with CF and haemophilia performed worse than predicted by the Pihno equation, although the range was wide. Standardised ISWT, and not ISWT distance alone, was significantly associated with forced expiratory volume in 1 s in CYP with CF. Standardised ISWT in CYP with haemophilia was slightly associated with haemophilia joint health score, but this was not significant. CONCLUSIONS: ISWT performance may be useful in a clinic to identify those with worsening disease, but only when performance is standardised against a healthy reference population. The development of validated global reference equations is necessary for more robust assessment.

The impact of genetic background during laboratory evolution of Pseudomonas aeruginosa in a cystic fibrosis-like environment.

Genetic background has the potential to influence both the tempo and trajectory of adaptive change: Different genotypes of a given species may adopt varied solutions to the same environmental challenge, or they may approach the same solution at different rates. Laboratory selection has been widely used to experimentally examine the evolutionary consequences of variation in genetic background, although largely using genotypes differing by only a few mutations. Here, we leverage natural variation in the bacterium Pseudomonas aeruginosa to investigate whether different adaptive solutions are accessible from distant points of departure on an adaptive landscape. We evolved 17 diverse genotypes in a laboratory medium that partially mimics the lung sputum of cystic fibrosis patients, and we measured changes in 10 phenotypes as well as in fitness. Using phylogenetically informed analyses, we found that genetic background impacted the tempo, but not the trajectory, of phenotypic evolution: Different starting genotypes converged toward similar phenotypes, but at varying rates. Our findings add to a growing body of evidence supporting widespread diminishing return epistasis during adaptation. The importance of genetic background toward the trajectory of adaptation remains inconsistent across experimental systems and conditions.

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

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.

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

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.

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

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.

Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.

Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published between 2013 and 2021 were evaluated for features of a good clinical prediction model using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines.Most of the studies were in asthma (80%), with few in cystic fibrosis (12%), bronchiolitis (4%) and childhood wheeze (4%). There were inconsistencies in model reporting and studies were limited by a lack of validation, and absence of equations or code for replication. Clinician involvement during ML model development is essential and diversity, equity and inclusion should be assessed at each step of the ML pipeline to ensure algorithms do not promote or amplify health disparities among marginalised groups. As ML prediction studies become more frequent, it is important that models are rigorously developed using published guidelines and take account of regulatory frameworks which depend on model complexity, patient safety, accountability and liability.

Official ERS technical standard: Global Lung Function Initiative reference values for static lung volumes in individuals of European ancestry.

BACKGROUND: Measurement of lung volumes across the life course is critical to the diagnosis and management of lung disease. The aim of the study was to use the Global Lung Function Initiative methodology to develop all-age multi-ethnic reference equations for lung volume indices determined using body plethysmography and gas dilution techniques. METHODS: Static lung volume data from body plethysmography and gas dilution techniques from individual, healthy participants were collated. Reference equations were derived using the LMS (lambda-mu-sigma) method and the generalised additive models of location shape and scale programme in R. The impact of measurement technique, equipment type and being overweight or obese on the derived lung volume reference ranges was assessed. RESULTS: Data from 17 centres were submitted and reference equations were derived from 7190 observations from participants of European ancestry between the ages of 5 and 80 years. Data from non-European ancestry populations were insufficient to develop multi-ethnic equations. Measurements of functional residual capacity (FRC) collected using plethysmography and dilution techniques showed physiologically insignificant differences and were combined. Sex-specific reference equations including height and age were developed for total lung capacity (TLC), FRC, residual volume (RV), inspiratory capacity, vital capacity, expiratory reserve volume and RV/TLC. The derived equations were similar to previously published equations for FRC and TLC, with closer agreement during childhood and adolescence than in adulthood. CONCLUSIONS: Global Lung Function Initiative reference equations for lung volumes provide a generalisable standard for reporting and interpretation of lung volumes measurements in individuals of European ancestry.

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

BACKGROUND: Cystic fibrosis (CF) is a multisystem disease in which the assessment of 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 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 and B) consistent with mild disease were identified with high forced expiratory volume in 1 s (FEV1), and low risk of both hospitalisation and pulmonary exacerbation treated with oral antibiotics. Two clusters (C and D) consistent with severe disease were also identified with low FEV1. Cluster D had the shortest time to both hospitalisation and pulmonary exacerbation 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.

Protocol for Project Fizzyo, an analytic longitudinal observational cohort study of physiotherapy for children and young people with cystic fibrosis, with interrupted time-series design.

INTRODUCTION: Daily physiotherapy is believed to mitigate the progression of cystic fibrosis (CF) lung disease. However, physiotherapy airway clearance techniques (ACTs) are burdensome and the evidence guiding practice remains weak. This paper describes the protocol for Project Fizzyo, which uses innovative technology and analysis methods to remotely capture longitudinal daily data from physiotherapy treatments to measure adherence and prospectively evaluate associations with clinical outcomes. METHODS AND ANALYSIS: A cohort of 145 children and young people with CF aged 6-16 years were recruited. Each participant will record their usual physiotherapy sessions daily for 16 months, using remote monitoring sensors: (1) a bespoke ACT sensor, inserted into their usual ACT device and (2) a Fitbit Alta HR activity tracker. Real-time breath pressure during ACTs, and heart rate and daily step counts (Fitbit) are synced using specific software applications. An interrupted time-series design will facilitate evaluation of ACT interventions (feedback and ACT-driven gaming). Baseline, mid and endpoint assessments of spirometry, exercise capacity and quality of life and longitudinal clinical record data will also be collected.This large dataset will be analysed in R using big data analytics approaches. Distinct ACT and physical activity adherence profiles will be identified, using cluster analysis to define groups of individuals based on measured characteristics and any relationships to clinical profiles assessed. Changes in adherence to physiotherapy over time or in relation to ACT interventions will be quantified and evaluated in relation to clinical outcomes. ETHICS AND DISSEMINATION: Ethical approval for this study (IRAS: 228625) was granted by the London-Brighton and Sussex NREC (18/LO/1038). Findings will be disseminated via peer-reviewed publications, at conferences and via CF clinical networks. The statistical code will be published in the Fizzyo GitHub repository and the dataset stored in the Great Ormond Street Hospital Digital Research Environment. TRIAL REGISTRATION NUMBER: ISRCTN51624752; Pre-results.

Paediatric reproducibility limits for the forced expiratory volume in 1 s.

BACKGROUND: Current reproducibility standards for spirometry were derived using a small adult dataset and may not be optimal for interpretation of repeated measurements of lung function in children. OBJECTIVE: To define reproducibility limits for forced expiratory volume in 1 s (FEV1) change that represent the normal within-subject between-visit variability in healthy children and evaluate these limits as a tool to monitor children with cystic fibrosis (CF). METHODS: Repeated FEV1 measurements (3 months to 5 years apart) from healthy children from the Global Lung Function Initiative data repository were used to derive a conditional change score. Spirometry and clinical data from a CF clinical database was used to verify utility in clinical practice. RESULTS: A reproducibility change score was derived from 47 938 FEV1 measures from 7885 healthy children 6-18 years of age. The simple algorithm, which is conditional on the initial measurement, also accounts for age and time interval between measurements. The change score limits of reproducibility were much narrower than currently used cut-offs. Specifically, changes, considered as improvements using either a 12% or 10% relative change from baseline, are too wide for children. In CF, there was overall agreement between different approaches, with the distinct advantage that the change score was not biased by regression to the mean. CONCLUSIONS: Compared with current approaches to interpretation of repeated lung function measurements, the proposed change score was less biased and provides a simple alternative to reduce misinterpretation.