Development of a Composite Health Index in Children with Cystic Fibrosis: A Pipeline for Data Processing, Machine Learning, and Model Implementation using Electronic Health Records

Cystic Fibrosis (CF) is a heterogeneous multi-faceted genetic condition that primarily affects the lungs and digestive system. For children and young people living with CF, timely management is necessary to prevent the establishment of severe disease. Modern data capture through electronic health records (EHR) have created an opportunity to use machine learning algorithms to classify subgroups of disease to understand health status and prognosis. The overall aim of this thesis was to develop a composite health index in children with CF. An iterative approach to unsupervised cluster analysis was developed to identify homogeneous clusters of children with CF in a pre-existing encounter-based CF database from Toronto Canada. An external validation of the model was carried out in a historical CF dataset from Great Ormond Street Hospital (GOSH) in London UK. The clusters were also re-created and validated using EHR data from GOSH when it first became accessible in 2021. The interpretability and sensitivity of the GOSH EHR model was explored. Lastly, a scoping review was carried out to investigate common barriers to implementation of prognostic machine learning algorithms in paediatric respiratory care. A cluster model was identified that detailed four clusters associated with time to future hospitalisation, pulmonary exacerbation, and lung function. The clusters were also associated with different disease related variables such as comorbidities, anthropometrics, microbiology infections, and treatment history. An app was developed to display individualised cluster assignment, which will be a useful way to interpret the cluster model clinically. The review of prognostic machine learning algorithms identified a lack of reproducibility and validations as the major limitation to model reporting that impair clinical translation. EHR systems facilitate point-of-care access of individualised data and integrated machine learning models. However, there is a gap in translation to clinical implementation of machine learning models. With appropriate regulatory frameworks the health index developed for children with CF could be implemented in CF care

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

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

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

BACKGROUND: Cystic Fibrosis (CF) is commonly characterised by thick respiratory mucous. From diagnosis, people with CF are prescribed daily physiotherapy, including airway clearance treatments (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 quality and quantity of ACTs and lung function in children and young people with CF. METHODS: Project Fizzyo, a longitudinal observational cohort study, used remote-monitoring electronic pressure sensors with 4 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 ACTs (135 individuals) and 21 069 days without treatments (141 individuals) were analysed. Average age of participants was 10.2 years (sd=2.9). Conformant ACTs (21%) had significantly higher FEV1 (mean effect size 0.23, 95%CI [0.19, 0.27]) 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]). CONCLUSION: ACTs are beneficial when done 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