Title: Designing a Paediatric Sepsis Prediction Tool using Machine Learning
Funder: Department of Health and Social Care

Abstract: BACKGROUND: Sepsis in paediatrics is a global threat and a major contributor to childhood mortality and morbidity. Recognition of sepsis was acknowledged as a national, and also a top research priority. There are limited studies reporting the utilization of paediatric sepsis prediction tools, none of which have been subjected to robust research trials to allow generalisability. To date there are no validated paediatric sepsis prediction tools in the UK. Models developed utilizing electronic health records (EHR) using machine learning (ML) algorithms, demonstrated high precision to detect neonatal sepsis and paediatric severe sepsis, and predict clinical outcomes and disposition in children in the emergency department (ED). AIM: The overarching aim of the study is to design a robust, accurate prediction tool for identifying the outcomes of sepsis in children, using machine learning methods. PRIMARY OBJECTIVE: Derive clinical characteristics predictive of sepsis outcomes using ML methods. SECONDARY OBJECTIVE: Create a ML algorithm to predict sepsis outcomes METHODS: The study designed as a retrospective, observational database analysis, will curate a 2-year dataset of routinely collected EHRs of children less than 16 years, attending the paediatric ED at the St Thomas' Hospital. PRELIMINARY WORK: A sample dataset of 1199 patients with infection (fever) was curated for six variables (age, heart rate, respiratory rate, oxygen saturation, systolic blood pressure, AVPU scale). We trained three classifiers - a logistic regression, a random forest and XGBoost model to predict admission. We split the data into a 75:25 ratio for training and testing respectively. We trained each model with the training data and evaluated the AUROC score on each data split. The performance of these models (training vs test AUROC) were logistic regression 0.74 (0.73), random forest 0.9 (0.77) and XGBoost Classifier 1.0 (0.74). Each model performed better than random guess (AUROC > 0.5) and had a much higher accuracy. We demonstrated predictive power in the six variables. Incorporating more clinical variables may enhance the accuracy. The reasonable performance of this model demonstrated this is a viable approach and the overfitting justifies the need for further data collection. STRATEGY: 1) Develop the infrastructure and platforms for the extraction, storage and processing of a wider range of EHR variables, including laboratory and microbiology results, 2) Create ML prediction model using classical statistical and machine learning methods, combining novel predictors of sepsis with criteria from known scoring systems of paediatric sepsis 3) Train, test and improve machine learning model. SIGNIFICANCE: Our study will pioneer the application of machine learning for the first time in paediatric emergency care in the UK. Further, it will initiate important research using big data and artificial intelligence in paediatrics and other specialties. The project fosters collaboration of clinical experts, data analysts and data scientists. The ML algorithm developed during the study will undergo a Phase 2 multi-centre prospective evaluation and refinement to demonstrate its clinical effectiveness, and its broader therapeutic and economic impact. The final prediction tool will have the potential to save children s lives and improve outcomes from sepsis in children though nationwide adoption.

Lay abstract: Background Sepsis is a life-threatening and preventable condition that kills children worldwide. More than 4% of all hospitalized patients less than 18 years, and about 8% of patients admitted to paediatric intensive care units have sepsis. Mortality for children with sepsis ranges from 4%, to as high as 50%. The National Institute for Health and Care (NICE) guideline which forms the foundation for paediatric sepsis recognition in the UK, acknowledges lack of adequate research evidence regarding its accuracy. Application of NICE guidelines in paediatric emergency departments (ED) is estimated to increase intravenous antibiotic usage by ten to forty-fold. This leads to poor experience for patients and unnecessary, expensive treatments. To date, there are no robust sepsis prediction or screening tools in the UK. Predictive models developed from electronic health records (EHR) using artificial intelligence (AI) demonstrated high precision to detect sepsis in children and the new-born, and predict clinical outcomes in children attending ED. Aims 1) The overarching aim of this study is to design a robust, accurate prediction tool for sepsis in children. 2) Software integrating this prediction tool will be developed in the future. These softwares will have the potential to save children s lives and improve outcomes of sepsis, by supporting clinical decision. Design & methods Phase 1 of the main programme, being proposed for funding, has been designed as a retrospective, observational study. We will develop a prediction tool by applying AI methods to a 2-year dataset of routinely collected EHRs of children attending at the St Thomas' Hospital paediatric ED. Future programme of research The main programme will be delivered over three Phases in the future. Phase 2: Prospective, multi-centre evaluation of the prediction tool developed in Phase 1; refinement and incorporation into software packages Phase 3: Pilot study to determine the safety and performance; improvement of preliminary software packages. Phase 4: Integration of finessed software at multiple centres to support clinical decision for early identification of sepsis in children. Patient and public involvement Members of the Young Persons Advisory Group (YPAG) have contributed to the study design and protocol writing. The YPAG will be involved throughout the course of the study. The group will attend meetings and feedback on the ethical use of data. They will help design posters and the study website. Dissemination The YPAG will contribute towards dissemination of the findings through engagement events and in-house conferences, posts and blogs through the study website and social media, accounting for their experiences and showcasing the study results. Following completion of the study, publications will be submitted to high-impact journals as open access. The YPAG will be encouraged to contribute to the writing of any manuscripts.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: INFLAMMATORY AND IMMUNE SYSTEM;INFECTION