The Secondary Use of Longitudinal Critical Care Data

Aims To examine the strengths and limitations of a novel United Kingdom (UK) critical care data resource that repurposes routinely collected physiological data for research. Exemplar clinical research studies will be developed to explore the unique longitudinal nature of the resource. Objectives - To evaluate the suitability of the National Institute for Health Research (NIHR) Critical Care theme of the Health Informatics Collaborative (CCHIC) data model as a representation of the Electronic Health Record (EHR) for secondary research use. - To conduct a data quality evaluation of data stored within the CC-HIC research database. - To use the CC-HIC research database to conduct two clinical research studies that make use of the longitudinal data supported by the CC-HIC: - The association between cumulative exposure to excess oxygen and outcomes in the critically ill. - The association between different morphologies of longitudinal physiology—in particular organ dysfunction—and outcomes in sepsis. The CC-HIC The EHR is now routinely used for the delivery of patient care throughout the United Kingdom (UK). This has presented the opportunity to learn from a large volume of routinely collected data. The CC-HIC data model represents 255 distinct clinical concepts including demographics, outcomes and granular longitudinal physiology. This model is used to harmonise EHR data of 12 contributing Intensive Care Units (ICUs). This thesis evaluates the suitability of the CC-HIC data model in this role and the quality of data within. While representing an important first step in this field, the CC-HIC data model lacks the necessary normalisation and semantic expressivity to excel in this role. The quality of the CC-HIC research database was variable between contributing sites. High levels of missing data, missing meta-data, non-standardised units and temporal drop out of submitted data are amongst the most challenging features to tackle. It is the principal finding of this thesis that the CC-HIC should transition towards implementing internationally agreed standards for interoperability. Exemplar Clinical Studies Two exemplar studies are presented, each designed to make use of the longitudinal data made available by the CC-HIC and address domains that are both contemporaneous and of importance to the critical care community. Exposure to Excess Oxygen Longitudinal data from the CC-HIC cohort were used to explore the association between the cumulative exposure to excess oxygen and outcomes in the critically ill. A small (likely less than 1% absolute risk reduction) dose-independent association was found between exposure to excess oxygen and mortality. The lack of dosedependency challenges a causal interpretation of these findings. Physiological Morphologies in Sepsis The joint modelling paradigm was applied to explore the different longitudinal profiles of organ failure in sepsis, while accounting for informative censoring from patient death. The rate of change of organ failure was found to play a more significan't role in outcomes than the absolute value of organ failure at a given moment. This has important implications for how the critical care community views the evolution of physiology in sepsis. DECOVID The Decoding COVID-19 (DECOVID) project is presented as future work. DECOVID is a collaborative data sharing project that pools clinical data from two large NHS trusts in England. Many of the lessons learnt from the prior work with the CC-HIC fed into the development of the DECOVID data model and its quality evaluation

Construction de modèles de données relationnels temporalisés guidée par les ontologies

Au sein d’une organisation, de même qu’entre des organisations, il y a plusieurs intervenants qui doivent prendre des décisions en fonction de la vision qu’ils se font de l’organisation concernée, de son environnement et des interactions entre les deux. Dans la plupart des cas, les données sont fragmentées en plusieurs sources non coordonnées ce qui complique, notamment, le fait de retracer leur évolution chronologique. Ces différentes sources sont hétérogènes par leur structure, par la sémantique des données qu’elles contiennent, par les technologies informatiques qui les manipulent et par les règles de gouvernance qui les contrôlent. Dans ce contexte, un système de santé apprenant (Learning Health System) a pour objectif d’unifier les soins de santé, la recherche biomédicale et le transfert des connaissances, en offrant des outils et des services pour améliorer la collaboration entre les intervenants ; l’optique sous-jacente à cette collaboration étant de fournir à un individu de meilleurs services qui soient personnalisés. Les méthodes classiques de construction de modèle de données sont fondées sur des règles de pratique souvent peu précises, ad hoc, non automatisables. L’extraction des données d’intérêt implique donc d’importantes mobilisations de ressources humaines. De ce fait, la conciliation et l’agrégation des sources sont sans cesse à recommencer parce que les besoins ne sont pas tous connus à l’avance, qu’ils varient au gré de l’évolution des processus et que les données sont souvent incomplètes. Pour obtenir l’interopérabilité, il est nécessaire d’élaborer une méthode automatisée de construction de modèle de données qui maintient conjointement les données brutes des sources et leur sémantique. Cette thèse présente une méthode qui permet, une fois qu’un modèle de connaissance est choisi, la construction d’un modèle de données selon des critères fondamentaux issus d’un modèle ontologique et d’un modèle relationnel temporel basé sur la logique des intervalles. De plus, la méthode est semi- automatisée par un prototype, OntoRelα. D’une part, l’utilisation des ontologies pour définir la sémantique des données est un moyen intéressant pour assurer une meilleure interopérabilité sémantique étant donné que l’ontologie permet d’exprimer de façon exploitable automatiquement différents axiomes logiques qui permettent la description de données et de leurs liens. D’autre part, l’utilisation d’un modèle relationnel temporalisé permet l’uniformisation de la structure du modèle de données, l’intégration des contraintes temporelles ainsi que l’intégration des contraintes du domaine qui proviennent des ontologies.Within an organization, many stakeholders must make decisions based on their vision of the organization, its environment, and the interactions between these two. In most cases, the data are fragmented in several uncoordinated sources, making it difficult, in particular, to trace their chronological evolution. These different sources are heterogeneous in their structure, in the semantics of the data they contain, in the computer technologies that manipulate them, and in the governance rules that control them. In this context, a Learning Health System aims to unify health care, biomedical research and knowledge transfer by providing tools and services to enhance collaboration among stakeholders in the health system to provide better and personalized services to the patient. The implementation of such a system requires a common data model with semantics, structure, and consistent temporal traceability that ensures data integrity. Traditional data model design methods are based on vague, non-automatable best practice rules where the extraction of data of interest requires the involvement of very important human resources. The reconciliation and the aggregation of sources are constantly starting over again because not all needs are known in advance and vary with the evolution of processes and data are often incomplete. To obtain an interoperable data model, an automated construction method that jointly maintains the source raw data and their semantics is required. This thesis presents a method that build a data model according to fundamental criteria derived from an ontological model, a relational model and a temporal model based on the logic of intervals. In addition, the method is semi-automated by an OntoRelα prototype. On the one hand, the use of ontologies to define the semantics of data is an interesting way to ensure a better semantic interoperability since it automatically expresses different logical axioms allowing the description of data and their links. On the other hand, the use of a temporal relational model allows the standardization of data model structure and the integration of temporal constraints as well as the integration of domain constraints defines in the ontologies