Developing a Regional Understanding of Primary Biliary Cholangitis using a Novel Clinical Registry with Linked and Real-World Data
Bibliographic record
Abstract
IntroductionLiver disease is currently the 5th biggest cause of mortality in England and Wales. TheUK liver disease crisis has been captured and extensively analysed by the Lancetcommission group in 2014 (and subsequent versions). This landmark publication hasproduced a blueprint for addressing the burden of liver disease in the UK. The scopeof the report does not only cover common liver diseases, but also rarer causes ofhepatic pathology often called orphan liver diseases. It is estimated that there are 54million people living with a rare disease in Europe and North America. There is also awider call to consider integrated care for all patients with liver disease thougheffective chronic disease management.In this thesis, I argue that rare diseases are also chronic diseases and should be viewedthrough the prism of the chronic care model (CCM) which has been successfully usedhistorically for commoner conditions such as diabetes and chronic obstructivepulmonary disease (COPD). The low prevalence of rare liver diseases leads to paucityof data both from clinical trials as well as the real world. The European UnionCommittee of Experts on Rare Diseases (EUCERD) was set up with the purpose ofencouraging the exchange of relevant experience, policies, and practices in rarediseases among member states. It became the prelude to the European ReferenceNetworks (ERNs) which were set up at a later stage to underpin the provision of robustgovernance and policy in data collection and registration in rare diseases within theEuropean Union (EU). Despite this framework, a comprehensive blueprint of how to create an effective and contemporary registry for rare liver diseases does not exist todate, despite there being approximately 20 non-cancer rare hepatic conditions.In this project, I used primary biliary cholangitis (PBC) as an example of a rare liverdisease. My aim was to initially understand whether there is much evidence in theliterature on the use of CCM for rare liver diseases, and subsequently, develop a modelfor creating registries for rare liver disease. Using this model, I next set out to build aregional registry for PBC using real world regional data in the county of Surrey, UK.Thereafter, I sought to use the registry to examine whether it had yielded meaningfulclinical outputs for patients with PBC in the county of Surrey.MethodsBefore I set up the regional registry for PBC in Surrey, I sought to identify Europeannon-cancer registries for patients with rare liver diseases. Using identified literatureand data from those registries, I was able to develop my own model for an aspirationaldata registry for rare liver diseases. I used this model as a theoretical cornerstone tobuild the PBC registry, which also allowed data linkage from primary, secondary, andtertiary care. Following the necessary applications and approvals from the HealthResearch Authority, data were collected from primary, secondary, and tertiary care.For the primary care data, I used the database of the Research and Surveillance Centre(RSC) of the Royal College of General Practitioners (RCGP), which holds data on morethan 1.2 million patients in England. I focused on GP surgeries that have consented todata collection in the county of Surrey. Relevant data were captured for the registry.Moreover, I also used the primary care data to explore whether I could develop anovel ontology to search for patients with PBC in primary care datasets. I collectedreal-world secondary care data from three regional NHS hospital Trusts includingRoyal Surrey NHS Foundation Trust (RSNHSFT), Ashford and St Peter’s hospital (ASPH)and East Surrey hospital (ESH). Following the necessary application, tertiary centredata were obtained from patients in the county of Surrey who had received a diagnosisof PBC. Once the data were mined and uploaded onto the registry, they were analysedusing Statistical Package for the Social Sciences (SPSS) v28.ResultsMy initial literature review using a systematic approach identified that there waslimited use of the chronic care model in patients with rare liver disease. There wereonly 6, 11, 1, 13, 2 and 0 studies discussing individual components of the CCM forAutoimmune Hepatitis (AIH), PBC, Primary Sclerosing Cholangitis (PSC), Wilsonsdisease (WD), Alpha-1 Antitrypsin Deficiency (A1AD) and Lysosomal Acid Lipasedeficiency (LALd) respectively. I did not identify any studies using the full CCM for anyhepatic orphan liver diseases. One of the components of the CCM is the use of clinicalinformation systems and registries. There was very little identified literature on theuse of disease registries for rare liver disease. A separate literature review was carriedout to appreciate the cardinal components and crucial elements of establishedregistries for rare liver disease. The review identified 37 European registries, whichwere analysed and led to the development of a novel registry design blueprint. Usinginformation from the design of these registries I developed a blueprint for thedevelopment of a patient registry for rare liver diseases consisting of 9 stages underthree phases: the theoretical, technical and maintenance phases. I used this model tobuild a regional PBC registry.I searched 218,099 primary care records and developed a novel clinical ontology toidentify patients with PBC. Using this ontology, I identified 58 patients with likely PBCand 2317 patients with probable PBC. There were 32 new cases of PBC. These datawere linked to secondary care data and in total, the registry held regional real-worlddata for 403 patients with PBC. The ratio of male: female was found to beapproximately 1:10 and the average age at diagnosis was 59. Most patients wereWhite Caucasian (93%). Fatigue, itching, and arthralgia were the commonestpresenting symptoms. Using reference criteria to assess response, I found higherresponse rates in underdosed patients. Similarly, underdosing patients appears to alsoyield better response rates when the Barcelona, Paris-2 and Ehim criteria were usedto assess response. On the contrary, overdosing patients confers better responserates when using the Paris-I, Toronto, Rotterdam and Momah Lindon criteria.DiscussionIt is believed that rare diseases may affect as much as 6-8% of the EU population acrossits 28 member states, and yet there is little published consideration for theseconditions to be viewed through models of chronic care. One of the novel outputs ofthis work is the development of a toolkit for designing registries for rare liver disease(and not only). Therefore, this work complements the efforts of loco-regional,national, and international groups seeking to establish robust systems for datacollection and analysis for orphan liver diseases. Another novel output of this thesis isthe development of a PBC ontology, which was used to search primary care records.Using this tool, I was able to identify (i) established cases of PBC not known tolocal/regional secondary care providers and (ii) de novo PBC cases, not previouslyidentified either in primary or in secondary care. There are many PBC probable caseswhose data merit further careful evaluation, and it is possible that many of these casesare true PBC cases. The Surrey PBC registry is probably the largest real-world PBCdatabase, which I have utilised to describe in detail, demographics, geoepidemiology,referral timelines, clinical management, pharmacotherapy, natural history of disease,and survival outcomes of patient with PBC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".