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Enregistrement W6904935069 · doi:10.15126/thesis.901265

Developing a Regional Understanding of Primary Biliary Cholangitis using a Novel Clinical Registry with Linked and Real-World Data

2024· article· en· W6904935069 sur OpenAlexaboutno aff

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineMedicine
ThématiqueLiver Diseases and Immunity
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChronic liver diseaseBlueprintDiseaseEuropean unionLiver diseaseRare diseaseDisease registryEpidemiology

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,027
score de la tête « metaresearch » (Gemma)0,068
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,033
Score d'incertitude au seuil0,144

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0270,068
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0080,008
Études des sciences et des technologies0,0010,002
Communication savante0,0100,008
Science ouverte0,0020,008
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,432
Tête enseignante GPT0,420
Écart entre enseignants0,012 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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