Exploring the relationship between age and health conditions using electronic health records: from single diseases to multimorbidities

Background Two enormous challenges facing healthcare systems are ageing and multimorbidity. Clinicians, policymakers, healthcare providers and researchers need to know “who gets which diseases when” in order to effectively prevent, detect and manage multiple conditions. Identification of ageing-related diseases (ARDs) is a starting point for research into common biological pathways in ageing. Examining multimorbidity clusters can facilitate a shift from the single-disease paradigm that pervades medical research and practice to models which reflect the reality of the patient population. Aim To examine how age influences an individual’s likelihood of developing single and multiple health conditions over the lifecourse. Methods and Outputs I used primary care and hospital admission electronic health records (EHRs) of 3,872,451 individuals from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care (HES-APC) dataset in England from 1 April 2010 to 31 March 2015. In collaboration with Professor Aroon Hingorani, Dr Osman Bhatti, Dr Shanaz Husain, Dr Shailen Sutaria, Professor Dorothea Nitsch, Mrs Melanie Hingorani, Dr Constantinos Parisinos, Dr Tom Lumbers and Dr Reecha Sofat, I derived the case definitions for 308 clinically important health conditions, by harmonising Read, ICD-10 and OPCS-4 codes across primary and secondary care records in England. I calculated the age-specific incidence rate, period prevalence and median age at first recorded diagnosis for these conditions and described the 50 most common diseases in each decade of life. I developed a protocol for identifying ARDs using machine-learning and actuarial techniques. Finally, I identified highly correlated multimorbidity clusters and created a tool to visualise comorbidity clusters using a network approach. Conclusions I have developed case definitions (with a panel of clinicians) and calculated disease frequency estimates for 308 clinically important health conditions in the NHS in England. I have described patterns of ageing and multimorbidity using these case definitions, and produced an online app for interrogating comorbidities for an index condition. This work facilitates future research into ageing pathways and multimorbidity

Pharmacovigilance Decision Support : The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology

The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms.Doctor of Philosoph

Measuring multimorbidity using Australian linked administrative health data sources

The growing number of individuals living with multimorbidity – the presence of two or more chronic conditions – is a challenge facing many healthcare systems internationally. Multimorbidity has been hailed a priority for research and practice, but Australian studies of multimorbidity are impeded by the lack of national primary care data, data silos, researcher access to data, and limited information contained within the data that are available. This thesis demonstrates how data linkage can be used to enhance the understanding of multimorbidity and its outcomes via a series of studies using Australian linked data sources, including claims-based, cohort study and clinical registry datasets for residents of NSW, Australia's most populous state. Thesis studies found variations in the recording of common health conditions between hospitals, under ascertainment of multimorbidity in administrative data, and differences in the estimates of multimorbidity dependent on the data used. Thesis studies also showed we can enhance our understanding of multimorbidity by exploring related concepts of patient risk and complexity. Within administrative hospital inpatient data, one-third of hospitalised patients had both multimorbidity and elevated risks of frailty – and these patients had worse outcomes than those with one or neither factor. The addition of clinical registry data also improved risk adjustment for hospital readmission performance indicators for total knee and hip replacement over and above models including multimorbidity measured using administrative hospital inpatient data. The research presented here highlights the benefits of the use of linked data in Australian multimorbidity research in three ways. Firstly, it underlines the need for incorporation of chronic disease information from multiple databases, including self-reported, inpatient, and claims-based data to accurately capture the extent of chronic disease and to identify people with multimorbidity. Secondly, it emphasises the need to examine complexities in the interplay between drivers of adverse outcomes – including multimorbidity, frailty and clinical assessment of a patient's overall health – in identifying patients with increased risk of complications and informing future hospital resource planning. And thirdly, it demonstrates the value of integrating new data sources, such as clinical registries with linked administrative data for improving risk-adjustment of hospital performance measures

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

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe