Harmonising Definitions of Multiple Long Terms Conditions for Inflammation Research: A Co-Production Approach

Harmonising Definitions of Multiple Long Terms Conditions for Inflammation Research: A Co-Production Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Harmonising Definitions of Multiple Long Terms Conditions for Inflammation Research: A Co-Production Approach Daniel Asfaw, Charlotte Davies, Elena Kulinskaya, Christopher Fox, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7773449/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in BMC Research Notes → Version 1 posted 13 You are reading this latest preprint version Abstract Background Inflammation is implicated in many chronic diseases, but its role in driving multiple long-term conditions (MLTCs) remains unclear. The InflAIM programme aims to explore this potential link. Existing MLTC codelists are valuable but have not been designed for inflammation-focused research and often lack harmonisation across coding systems. Objective To develop a transparent, co-produced MLTC framework with harmonised codelists for epidemiological research, focusing on inflammatory conditions within the InflAIM programme. Methods Using a systematic, co-produced approach, we combined literature review, clinical consensus, and patient input. From eight sources, 363 candidate conditions were identified, refined to 60 validated MLTCs. Patients informed terminology and diagnostic experiences. Codelists were drawn from CALIBER (43), HDR UK Phenotype Library (4), MULTIPLY (8), with 7 developed de novo. Harmonisation covered Read v2, SNOMED CT, and Medcodes. Quality assurance excluded 16.5% of codes and resolved 92 overlaps. Results We produced harmonised codelists for 60 MLTCs, comprising 7,426 validated MedCodes. Clinical consensus identified 17 conditions with potential inflammatory components. Additional lists were developed for autoimmune and infectious diseases to support inflammation-focused analyses. Conclusion This framework extends existing resources to meet the needs of inflammation-focused research, offering a transparent, harmonised, and patient-informed foundation for the InflAIM programme. Multiple Long-Term Conditions Multimorbidity Co-production Patient and Public Involvement Medical Coding Electronic Health Records Inflammation Figures Figure 1 Figure 2 Introduction The UK’s ageing population is living longer with complex health needs: two-thirds of adults aged 65 or older have at least two long-term conditions (MLTCs) [ 1 – 4 ]. Globally about one-third have at least two long-term[ 1 ]. Policy efforts, including NICE (National Institute for Health and Care Excellence) guidelines [ 5 ] and NIHR’s (National Institute for Health and Care Research) strategic focus on MLTCs [ 6 ], are limited by the lack of standardised definitions. Existing frameworks vary in condition selection, coding, and disease groupings [ 7 – 9 ], limiting comparability across studies. The InflAIM programme investigates whether inflammation contributes to MLTC development, particularly among socioeconomically disadvantaged populations [ 10 , 11 ]. Examining this with large-scale electronic health record (EHR) data is important but methodologically challenging. First, most MLTC codelists do not distinguish inflammatory from non-inflammatory conditions—for example, osteoarthritis and rheumatoid arthritis are often grouped together—limiting their value for inflammation-focused research. Second, differences in coding systems across datasets complicate harmonisation and reproducibility. Third, few frameworks incorporate lived experience, despite evidence that patients describe symptoms differently from clinical labels [ 2 , 3 ]. To address these challenges, we developed a co-produced MLTC framework with three key innovations: A targeted focus on identifying and prioritising inflammatory conditions within MLTC definitions; The development of harmonised codelists across three major UK primary care coding systems; and A methodology that integrates patient experience with clinical and coding expertise. Methods We employed a two-part methodology: (1) co-produced condition selection using an iterative, consensus-based process, and (2) the development of harmonised, interoperable codelists across UK EHR systems. Condition selection Stage 1: Literature Review We conducted a pragmatic literature review to identify key sources informing disease inclusion criteria for defining MLTCs. Starting with the seminal paper by Barnett et al [2], we conducted forward citation searching, supplemented by keyword searches across PubMed, Google Scholar, Scopus, and Web of Science. An overview of outcomes of the search strategy is shown in Figure 1. This process identified eight key sources: Barnett et al.[2], Ho et al. [3] MacRae et al.[4], GEMINI study [5], Dodds et al.[6], Lyall et al. [7], Head et al. [8], and Beaney et al.[9]. We systematically compiled all conditions identified across these sources. Conditions appearing in at least two of the eight sources comprised our provisional list of 363 conditions. Stage 2: Clinical Expert Review A panel of three clinical experts—representing primary care, public health, secondary care, and psychiatry—reviewed the candidate conditions over several rounds. Decisions were guided by clinical experience and discussion, with disagreements resolved through consensus across the group. The final resulting refined list of MLTCs were categorised as chronic physical, mental, or infectious diseases. A key consideration was how to define “chronic,” as published definitions range from diseases with a duration of greater than 3 to 12 months [9, 10], Rather than applying a strict threshold, the panel used clinical judgement to determine whether each condition was appropriately considered long-term. Conditions were excluded if they were acute, hereditary, or congenital, associated with childhood (under 18), or pregnancy related. Long COVID was also excluded, as it is typically coded as a standalone entity encompassing multiple complications, making it difficult to classify within a single-condition framework. Stage 3: Patient and Public Involvement (PPIE) Our PPIE group comprised 6–8 individuals with lived experience of MLTCs or of caring for someone with them. The group was intentionally diverse, including people from different ethnic and socioeconomic backgrounds and seldom-heard communities. Participants engaged in three phases: initial sessions using online whiteboards exploring what MLTCs meant to them; reviews comparing the clinical condition list with their own; and mind-mapping exercises to integrate lived and clinical perspectives. These activities revealed key differences in language and symptom framing, helping ensure the final framework aligned with both patient experience and clinical understanding. Stages 4 and 5: Updated literature and final clinical review Following the initial review stages, we re-examined the literature and coding sources to ensure completeness and incorporate new or overlooked conditions. Updates from the PPIE process were also considered. The clinical panel then undertook a final round of structured review and adjudication to refine the list, resolve outstanding disagreements, and confirm consensus on the final set of conditions. Codelist development Harmonised Codelist Development Following condition selection, we developed comprehensive phenotyping algorithms for the selected condition set across three coding systems relevant to UK primary care research: Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT, UK Clinical Extension & International Edition), Read Code Version 2, and CPRD (Clinical Practice Research Datalink) Aurum Medcodeids. These systems reflect the range of EHRs and research datasets included in the InflAIM programme. Hierarchical Algorithm Development We implemented a hierarchical approach to developing phenotyping algorithms, prioritising sources by methodological rigor, validation status, and research adoption. This ensured consistent quality and maximised semantic interoperability with epidemiological literature. Our primary source was the CALIBER (CArdiovascular disease research using LInked Bespoke studies and Electronic health Records) algorithms (Kuan et al.[23]), chosen for their validated definitions and reproducibility across multiple studies. As CALIBER was developed using the CPRD GOLD database, we adapted codelists for CPRD Aurum using the mapping approach from Head et al.[19]. Secondary sources included the HDR UK Phenotype Library, recognised for expert clinical validation, and the MULTIPLY (MULTI-national consortia to study Polygenic risk scores by Large-scale health sYstems Initiative), which provides consensus-based phenotypes with transparent methods and primary care relevance [11]. Semi-Automated Development Process For conditions lacking published definitions, we implemented a systematic semi-automated development process. This began with ICD-10 concept expansion using the WHO API (World Health Organization, Application Programming Interface) (2019), followed by cross-mapping using NHS TRUD (Technology Reference data Update Distribution) files and the CPRD Aurum Medical Dictionary. Prioritising sensitivity in the initial stage, we applied automated filtering across five sequential categories: procedure-related, administrative and non-diagnostic, acute event, congenital/hereditary, and finally "not otherwise specified" or "unspecified" code exclusions. Where a code appeared across multiple condition lists, we used clinical adjudication to assign it appropriately. Supplementary Codelist Development Supplementary analyses followed two steps. First, the clinical panel identified a subset of the primary condition list as having significant inflammatory pathophysiology, based on expert consensus. Second, for autoimmune and infectious diseases, we systematically sourced published codelists (e.g. Lyall et al.[18], Watson et al.[25], Janbek et al.[26]). These were harmonised across coding systems using the CPRD Aurum Medical Dictionary and the same methods as for primary codelists. Final lists were clinically curated and de-duplicated to exclude overlaps with the primary MLTC set. Results Condition selection outcome The literature review identified 363 candidate conditions across eight key sources. Through a four-stage co-production process—including clinical expert review, patient and public involvement, and final adjudication—the list was refined to 60 conditions (Table 1), each agreed by unanimous clinical consensus as relevant to the definition of MLTC for the InflAIM programme (Figure 2). From the list of 60 diseases, we labelled a condition as ‘inflammatory’ if it involved active, immune-driven tissue inflammation. For disease groupings, inclusion as inflammatory was based on whether key subtypes within the group clearly met this criterion. Autoimmune and infectious diseases were considered separately. Insights from the PPIE group influenced several aspects of condition selection. They highlighted distinctions between clinical diagnoses and lived symptom experience—particularly in mental health and fluctuating conditions—prompting closer scrutiny of coding practices. PPIE members described gout as chronic yet presenting as acute flares with serious consequences for mobility, quality of life, and mental health. Similarly, inflammatory bowel disease (IBD) was described as episodic, with remission periods punctuated by debilitating flares. While these fluctuations may not always appear in clinical records, participants consistently framed them as central to the lived burden of disease. The group noted that treatments can reshape experience, introducing side effects or new susceptibilities not always captured in coding. This input supported retention of conditions that may be inconsistently coded but consistently described as impactful. Participants further stressed that some conditions affect multiple organ systems, prompting scrutiny of how conditions were grouped for analysis. The final list included conditions from a wide range of clinical domains: cardiovascular, metabolic, endocrine, musculoskeletal, respiratory, neurological, mental health, gastrointestinal, renal, dermatological, haematological, oncological, and infectious diseases. Of the final MLTC list, 17 conditions were identified by clinical consensus as having significant inflammatory pathophysiology, including rheumatoid arthritis, inflammatory bowel disease and asthma (see Table 1, inflammatory conditions indicated by *). To further support InflAIM’s focus on inflammation, we identified a comprehensive list of autoimmune and infectious disease conditions through separate literature and clinical reviews for targeted phenotyping. Codelist Development Phenotyping algorithms were successfully developed for all 60 conditions using a hierarchical approach to sourcing, based on methodological rigour, validation status, and research adoption. CALIBER provided codelists for 43 conditions (71.6%), forming the majority of the final set. HDR UK Phenotype Library contributed 4 (6.6%), including ischaemic heart disease and tuberculosis. MULTIPLY Initiative provided 8 (13.3%), including systemic connective tissue disorders. Codelists for seven conditions (11.6%) were developed de novo through a semi-automated process, for conditions such as hypoparathyroidism and in situ neoplasms. Of the conditions sourced above, two (3.2%) required hybrid approaches combining multiple sources. The initial compilation included 8,898 Medcodeids across three UK coding systems. Following automated and manual filtering to improve diagnostic precision, 1,472 codes (16.5%) were excluded, resulting in 7,426 validated codes. These were harmonised across SNOMED CT, Read Version 2, and CPRD Aurum Medcodeids (Figure 2). Automated quality checks flagged 92 SNOMED CT concepts and 160 Read/Medcodeids that mapped to multiple conditions. These were adjudicated by clinical experts, with codes either reassigned to a single most appropriate condition or retained across multiple lists where justified by clinical overlap (Additional file, Table 1) . The taxonomic structure of the clinical codelists for 60 long-term conditions (LTCs) is visualized as a radial dendrogram in the Additional file, Figure 1. Supplementary codelists were then developed for 228 autoimmune and 2,107 infectious diseases, based on a systematic sourcing and harmonisation process . The autoimmune codelist included 489 Read codes and 341 SNOMED CT codes. The infectious disease list comprised 2,107 Read codes and 1,390 SNOMED CT codes (see Additional files, Tables 2 and 3). Discussion This study presents a systematically developed MLTC framework, tailored to support inflammation-focused research. Through a four-stage co-production process, we integrated evidence from the literature, clinical consensus, and public involvement to define a robust condition set and harmonised codelists. This addresses limitations in prior frameworks, which often lack transparency, patient input, or interchangeability across coding systems. Our approach was shaped by the aims of the InflAIM programme, exploring inflammatory mechanisms in multimorbidity. This influenced condition selection—prioritising those with inflammatory pathophysiology—and the decision to develop supplementary autoimmune and infectious disease codelists. Choices around granularity were guided by biological relevance; for example, grouping spinal degenerative conditions while treating inflammatory arthropathies as distinct. Patient and public contributors played a key role in refining the framework. Their insights prompted scrutiny of mental health and fluctuating conditions and highlighted mismatches between clinical codes and lived experience. This ensured the framework reflects not only diagnostic logic but also real-world complexity. Codelists were developed via a hierarchical strategy prioritising validated algorithms across three UK coding systems. Where definitions were absent, we derived new algorithms using a semi-automated pipeline with clinical adjudication. Automated quality checks improved specificity and resolved overlaps. Primarily built for CPRD Aurum, the codelists can be applied to other EHR datasets (e.g., UK Biobank) using established mapping tables, enhancing reproducibility. Limitations Our literature review focused on key sources, not exhaustive coverage. Clinical review involved judgement calls on chronicity and condition grouping. Despite rigorous harmonisation, some coding inconsistencies may persist. The PPIE process was qualitative, and while influential, its impact on final decisions is not easily quantifiable. Nonetheless, this work offers a flexible, transparent, and interoperable MLTC identification framework with broad applicability to other domains. By explicitly incorporating inflammation as a focal mechanism, and combining clinical evidence with patient perspectives, the approach strengthens the validity and relevance of MLTC research. Abbreviations MLTC: Multiple Long-Term Conditions NICE: National Institute for Health and Care Excellence NIHR: National Institute for Health and Care Research EHR: Electronic Health Records PPIE: Patient and Public Involvement and Engagement CPRD: Clinical Practice Research Datalink IBD: Inflammatory Bowel Disease ICD-10: International Classification of Diseases, 10th Revision SNOMED CT: Systematized Nomenclature of Medicine Clinical Terms CALIBER: CArdiovascular disease research using LInked Bespoke studies and Electronic health Records AI MULTIPLY Initiative: MULTI-national consortia to study Polygenic risk scores by Large-scale health sYstems TRUD: Technology Reference data Update Distribution HDR UK: Health Data Research UK WHO: World Health Organisation NHS: National Health Service API: Application Programming Interface Declarations Ethics approval and consent to participate – PPIE involvement and engagement in the InflAIM programme was approved by the University of Chester, Faculty of Health, Medicine and Society, School of Nursing and Midwifery’s Research Ethics Sub-Committee in May 2025. (REC Reference: FRC0425-954). PPIE Participants were informed about the aims and process of the programme at recruitment, provided with a programme handbook, and advised that they could withdraw from the programme at any time. Participants also provided written or verbal consent for the outcomes of the meetings to be shared for academic dissemination and other relevant purposes. Consent for publication - Not applicable Competing interests The authors declare that they have no competing interests. Funding This work was supported by NIHR grant NIHR205461as part of the InflAIM programme. Author Contribution Conceptualisation: D.A., C.D., A.J.M.Data curation: D.A.Methodology: D.A., C.D., E.K., B.A.H.J., C.F., J.F., J.G., A.M.Literature review: D.A., C.D.Validation of clinical conditions: A.J.M., C.F., J.F.PPIE: J.G., C.D.Writing – original draft preparation: D.A., C.D.Writing – review and editing: D.A., C.D., E.K., B.A.H.J., C.F., J.F., J.G., A.J.M.All authors read and approved the final manuscript. Acknowledgement We thank the PPIE group members for their invaluable contributions to this research and the clinical experts who provided consensus review. We acknowledge the whole InflAIM team for their support and advice. Data Availability The complete validated codelist repository are available at [https://github.com/Inflaim-Study](https:/github.com/Inflaim-Study) . All codelists are provided in multiple formats compatible with major UK electronic health record systems. References Chowdhury, S.R., et al., Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. EClinicalMedicine, 2023. 57 : p. 101860. Barnett, K., et al., Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet, 2012. 380 (9836): p. 37–43. Ho, I.S.S., et al., Measuring multimorbidity in research: Delphi consensus study. BMJ Med, 2022. 1 (1): p. e000247. MacRae, C., et al., Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service. BMC Medicine, 2023. 21 (1): p. 309. GEMINI Multimorbidity, GEMINI: Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions . 2025. Dodds, R.M., et al., Simple approaches to characterising multiple long-term conditions (multimorbidity) and rates of emergency hospital admission: Findings from 495,465 UK Biobank participants. J Intern Med, 2023. 293 (1): p. 100–109. Lyall, L.M., et al., The associations between self-reported depression, self-reported chronic inflammatory conditions and cognitive abilities in UK Biobank. Eur Psychiatry, 2019. 60 : p. 63–70. Head, A., et al., Inequalities in incident and prevalent multimorbidity in England, 2004–19: a population-based, descriptive study. The Lancet Healthy Longevity, 2021. 2 (8): p. e489–e497. Beaney, T., et al., Effect of timeframes to define long term conditions and sociodemographic factors on prevalence of multimorbidity using disease code frequency in primary care electronic health records: retrospective study. BMJ Med, 2024. 3 (1): p. e000474. Goodman, R.A., et al., Defining and measuring chronic conditions: imperatives for research, policy, program, and practice. Prev Chronic Dis, 2013. 10 : p. E66. Kuan, V., et al., A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit Health, 2019. 1 (2): p. e63–e77. Table Table 1 – MLTC conditions, ICD-10 chapter and Inflammatory indicator ICD-10 MLTC Condition A15-A19 Tuberculosis* A69 Other spirochaetal infections* B18-B19 Chronic viral hepatitis* B20-B24 HIV disease C00-C80, C97, D37-D44, D47, D48 Malignant neoplasms C81-C96 Malignant neoplasms, stated or presumed to be primary, of lymphoid, haematopoietic and related tissue D00-D09 In situ neoplasms D50 Iron deficiency anaemia E00-E07 Disorders of thyroid gland E10-E14 Diabetes E20 Hypoparathyroidism E21 Hyperparathyroidism E27 Addison's disease F00-F03 Dementia F10-F19 Drug or alcohol misuse F20-F29 Schizophrenia F30-F39 Mood disorders F40-F48 Neurotic, stress-related and somatoform disorders F50 Eating disorders G20-G21 Parkinson disease G35, H46 Multiple sclerosis (and optic neuritis)* G40, F80.3 Epilepsy G43 Migraine G47 Sleep disorders G61-G64 Polyneuropathies and other disorders of the peripheral nervous system H25 Cataract H33 Retinal detachments and breaks H35 Other retinal disorders (Macular degeneration) H40-H42 Glaucoma H53-H54 Visual disturbances and blindness H81.0 Meniere disease H90-H91 Conductive and sensorineural hearing loss I05-I09 Chronic rheumatic heart diseases* I10-I15 Hypertensive diseases I20-I25 Ischaemic heart diseases I30-I52 Other forms of heart disease I60-I69 Cerebrovascular Disease I70-I79 Disease of arteries, arterioles and capillaries I80-I89 Diseases of veins, lymphatic vessels and lymph nodes, note elsewhere classified J30-J32 Rhinitis and Sinusitis* J40-J47 Chronic lower respiratory diseases* J84 Other interstitial pulmonary diseases* K21, K27, K29 Diseases of oesophagus, stomach and duodenum K50-K52 Noninfective enteritis and colitis* K57 Diverticular disease of the intestine* K58 Irritable Bowel Syndrome K70-K77 Diseases of the liver K80-K87 Disorders of gallbladder, biliary tract and pancreas L20-L30 Dermatitis and eczema* L40 Psoriasis* M05-M14 Inflammatory polyarthropathies* M15-M19 Arthrosis M30-M36 Systemic connective tissue disorders* M45 Ankylosing spondylitis* M47, M51 Spinal degenerative disease M80-M85 Disorders of bone density and structure N18-N19 Chronic kidney disease N40-N42 Disorders of the prostate N70-N77 Inflammatory diseases of the female pelvic organs* N80 Endometriosis* *Inflammatory condition Additional Declarations No competing interests reported. Supplementary Files AdditionalFileTable1Overlappingconditions.xlsx AdditionalFileTable2AutoimmuneCodelist.xlsx AdditionalFileTable3InfectionsCodelist.xlsx AdditionalFileFigure1.docx Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in BMC Research Notes → Version 1 posted Editorial decision: Revision requested 10 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers invited by journal 15 Oct, 2025 Editor invited by journal 14 Oct, 2025 Editor assigned by journal 14 Oct, 2025 Submission checks completed at journal 08 Oct, 2025 First submitted to journal 08 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7773449","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":535412080,"identity":"97909ef3-4d97-4d1c-a397-f78285872c18","order_by":0,"name":"Daniel Asfaw","email":"","orcid":"","institution":"University of East Anglia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Asfaw","suffix":""},{"id":535412081,"identity":"f340a426-8dea-453c-bd0a-9a3df2db412a","order_by":1,"name":"Charlotte Davies","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie2RMUsDMRTHXyncFMz6DqGf4UHg6CB3XyVHoF0OFArSQeSg0G7OAb+EUnCOFDpFXDM4CELnwIEoFLWtcjjcVd0c8uMl5BF+vH8IQCDwH8HNevw80q54fcX2KBIIayUu/6Zsd/ODwi8nKy/H63MO3euKnRxlwqlbD2cpkDWNCj4shZaWMC6j0SGjQX7jBgphqYDuykaFUArIp4RkGMWaFjJxxSZmZIDum4MRDqudkhkmXjW9Z0IfVy/wtk8pvqYAS9CT6VxhEWFnalqDoStGIK2I9SI67XtSubarpJ9fKBa3PJ/r4Rz8uMf5bDJ3cp1mfKaenH9OewdWNier6X5vZPtHBgKBQOAXfAB/oVepKtfMDAAAAABJRU5ErkJggg==","orcid":"","institution":"University of East Anglia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Davies","suffix":""},{"id":535412082,"identity":"109d37d9-d51a-417b-93c7-351f1f823682","order_by":2,"name":"Elena Kulinskaya","email":"","orcid":"","institution":"University of East Anglia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Kulinskaya","suffix":""},{"id":535412083,"identity":"37c0d5af-5d9e-4aaa-b9d5-ec4918c918fa","order_by":3,"name":"Christopher Fox","email":"","orcid":"","institution":"University of Exeter","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Fox","suffix":""},{"id":535412084,"identity":"c81cfa1a-8cec-4132-a366-a7dcc8af7a4e","order_by":4,"name":"Benedict Alan Harries Jones","email":"","orcid":"","institution":"Evergreen Life","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Benedict","middleName":"Alan Harries","lastName":"Jones","suffix":""},{"id":535412085,"identity":"28193f2f-c888-4f2e-adbc-ab32723d2f22","order_by":5,"name":"Jasmyn Gooding","email":"","orcid":"","institution":"University of East Anglia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jasmyn","middleName":"","lastName":"Gooding","suffix":""},{"id":535412086,"identity":"c0e6270e-4c5e-4ce2-864e-6c1469a6e1a5","order_by":6,"name":"John Ford","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Ford","suffix":""},{"id":535412087,"identity":"8d8a40c6-5e94-4f3a-b679-9d4f73649afe","order_by":7,"name":"Alexander J MacGregor","email":"","orcid":"","institution":"University of East Anglia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"J","lastName":"MacGregor","suffix":""}],"badges":[],"createdAt":"2025-10-03 12:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7773449/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7773449/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13104-025-07626-0","type":"published","date":"2026-01-13T16:30:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94681346,"identity":"c3b7fcc0-1d7e-4456-9644-453b9c555a3c","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3791771,"visible":true,"origin":"","legend":"","description":"","filename":"HarmonisingDefinitionsofMultipleLongTermConditionsforInflammationResearchAsfawetal2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/69ecd519df9597a641942b94.docx"},{"id":94681328,"identity":"f2ae1214-8f9d-4eef-b27c-97f2174bc8f3","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9753,"visible":true,"origin":"","legend":"","description":"","filename":"499b1d0f2ed44abb8a376a5cb66c5573.json","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/fdc18748813b53f7bdbeb230.json"},{"id":94728385,"identity":"3c6abfd8-64c1-4480-95e3-d7b80a3b8a43","added_by":"auto","created_at":"2025-10-30 07:03:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3460416,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/e78778f5b10a61ea94bcd1c6.docx"},{"id":94681330,"identity":"96bc5581-1179-42c8-9b9c-1e4f02d71008","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25834,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTable1Overlappingconditions.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/b8a5504e0dd1fb82e6aaa0f6.xlsx"},{"id":94728553,"identity":"48b814bf-ced9-4ce8-9f8b-8b16abca3004","added_by":"auto","created_at":"2025-10-30 07:04:01","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52241,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTable2AutoimmuneCodelist.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/f30cbcf0663ade6e1b0e4f9a.xlsx"},{"id":94728723,"identity":"14269099-407f-469f-a390-40b44af028dc","added_by":"auto","created_at":"2025-10-30 07:04:13","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200557,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTable3InfectionsCodelist.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/b6ed2d6c65250bfe803a2646.xlsx"},{"id":94681332,"identity":"7e306739-3707-4bfe-8d42-c56cec0f8938","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61852,"visible":true,"origin":"","legend":"","description":"","filename":"499b1d0f2ed44abb8a376a5cb66c55731enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/8991d603f0d4f1f3d14d03ba.xml"},{"id":94681343,"identity":"a8715afb-e5fc-4ceb-87df-62827a2f354d","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1126355,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/a763718dde8f5a06a1243e2c.jpeg"},{"id":94728119,"identity":"369cdbce-cd98-4005-9544-4d543db70d56","added_by":"auto","created_at":"2025-10-30 07:03:06","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2574149,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/da1ee7c29e0a4e53fe1017b5.jpeg"},{"id":94728093,"identity":"bf824d1f-5c7e-480d-a168-5f78e459bcf2","added_by":"auto","created_at":"2025-10-30 07:03:04","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":514400,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/57012ccebf2f9401933ce124.png"},{"id":94681341,"identity":"6ff49cb6-e244-44f0-b228-0aa9b2715822","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1920553,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/2c22c714c47914b3735c55a6.png"},{"id":94728445,"identity":"0b87135a-3c31-4d27-9c0b-6041740ea377","added_by":"auto","created_at":"2025-10-30 07:03:49","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59068,"visible":true,"origin":"","legend":"","description":"","filename":"499b1d0f2ed44abb8a376a5cb66c55731structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/590803034d31d5457dc158d0.xml"},{"id":94681339,"identity":"2f8d7ae8-2544-4f36-99dc-58b3819eaa35","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67222,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/c03976d0737950f0c9b153b5.html"},{"id":94681329,"identity":"0ba5d1b7-24ae-4917-adb6-43df744ed037","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1126355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of inclusion/exclusion*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e* Flow diagram showing the process of identifying and refining multiple long-term conditions (MLTCs). Candidate conditions were sourced from Barnett \u003cem\u003eet al.\u003c/em\u003e (2012), McRae \u003cem\u003eet al.\u003c/em\u003e (2023), Ho \u003cem\u003eet al.\u003c/em\u003e (2022), Cooper \u003cem\u003eet al.\u003c/em\u003e (2024), Lyall \u003cem\u003eet al.\u003c/em\u003e (2019), and the GEMINI codelist (2023). Following clinical review (n = 363), conditions were aggregated to ICD-10 subchapter level (n = 307), filtered for inclusion if listed in ≥2 sources and validated through clinical review (n = 103), reviewed by the PPIE core group (n = 62), and subjected to final coding refinements. This process provided a final list of 60 MLTCs. \u003cem\u003e(n) = number of long-term conditions reported.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/f934ee2d2a519e24ecf7805c.jpeg"},{"id":94681335,"identity":"e15aec4d-15b9-40b5-ab1f-88bfab54688d","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2574149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow for the development and validation of 60 harmonised phenotyping algorithms.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e* Workflow for development and validation of harmonised phenotyping algorithms. The diagram illustrates the hierarchical sourcing strategy, integration of published and de novo algorithms, and the multi-stage filtering and adjudication processes that produced the final validated codelists across three UK primary care coding systems. Counts shown in each block indicate the number of unique codes (concepts/terms) present at that stage within the respective coding system.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/ee7e3b1f0a2d12f96f472e7d.jpeg"},{"id":100617739,"identity":"1d903205-f6c2-4976-b898-e6d16b18b948","added_by":"auto","created_at":"2026-01-19 17:55:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4441328,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/e5046dab-88a4-4069-b58e-8a6f6cb9709f.pdf"},{"id":94681327,"identity":"96e45e65-ad53-4226-9341-3c591fa767ad","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25834,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTable1Overlappingconditions.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/11962a828b7bebe53bf681a6.xlsx"},{"id":94728876,"identity":"3368349a-48d1-4158-b880-9e1c58e1e3c5","added_by":"auto","created_at":"2025-10-30 07:04:19","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":52241,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTable2AutoimmuneCodelist.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/cd2b391ffe2602a583e5f1fc.xlsx"},{"id":94728585,"identity":"9fd2d02e-c3d2-4992-ad13-e4472c46aa28","added_by":"auto","created_at":"2025-10-30 07:04:02","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":200557,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTable3InfectionsCodelist.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/05c72bdba4ac9922a966944f.xlsx"},{"id":94681345,"identity":"4c0163fc-d1df-4a07-99d9-8eba0e76701b","added_by":"auto","created_at":"2025-10-29 14:51:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3460416,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7773449/v1/5ecf7517a39e4e62ec64adad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Harmonising Definitions of Multiple Long Terms Conditions for Inflammation Research: A Co-Production Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe UK\u0026rsquo;s ageing population is living longer with complex health needs: two-thirds of adults aged 65 or older have at least two long-term conditions (MLTCs) [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Globally about one-third have at least two long-term[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Policy efforts, including NICE (National Institute for Health and Care Excellence) guidelines [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and NIHR\u0026rsquo;s (National Institute for Health and Care Research) strategic focus on MLTCs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], are limited by the lack of standardised definitions. Existing frameworks vary in condition selection, coding, and disease groupings [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], limiting comparability across studies.\u003c/p\u003e\u003cp\u003eThe InflAIM programme investigates whether inflammation contributes to MLTC development, particularly among socioeconomically disadvantaged populations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Examining this with large-scale electronic health record (EHR) data is important but methodologically challenging. First, most MLTC codelists do not distinguish inflammatory from non-inflammatory conditions\u0026mdash;for example, osteoarthritis and rheumatoid arthritis are often grouped together\u0026mdash;limiting their value for inflammation-focused research. Second, differences in coding systems across datasets complicate harmonisation and reproducibility. Third, few frameworks incorporate lived experience, despite evidence that patients describe symptoms differently from clinical labels [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo address these challenges, we developed a co-produced MLTC framework with three key innovations:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eA targeted focus on identifying and prioritising inflammatory conditions within MLTC definitions;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe development of harmonised codelists across three major UK primary care coding systems; and\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eA methodology that integrates patient experience with clinical and coding expertise.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe employed a two-part methodology: (1) co-produced condition selection using an iterative, consensus-based process, and (2) the development of harmonised, interoperable codelists across UK EHR systems.\u003c/p\u003e\n\u003ch3\u003eCondition selection\u003c/h3\u003e\n\u003ch4\u003e\u003cstrong\u003eStage 1: Literature Review\u003c/strong\u003e\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eWe conducted a pragmatic literature review to identify key sources informing disease inclusion criteria for defining MLTCs. Starting with the seminal paper by Barnett et al [2], we conducted forward citation searching, supplemented by keyword searches across PubMed, Google Scholar, Scopus, and Web of Science. \u0026nbsp;An overview of outcomes of the search strategy is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eThis process identified eight key sources: Barnett et al.[2], Ho et al. [3] MacRae et al.[4], GEMINI study [5], Dodds et al.[6], Lyall et al. [7], Head et al. [8], and Beaney et al.[9]. We systematically compiled all conditions identified across these sources. Conditions appearing in at least two of the eight sources comprised our provisional list of 363 conditions.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eStage 2: Clinical Expert Review\u003c/strong\u003e\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA panel of three clinical experts\u0026mdash;representing primary care, public health, secondary care, and psychiatry\u0026mdash;reviewed the candidate conditions over several rounds. Decisions were guided by clinical experience and discussion, with disagreements resolved through consensus across the group. The final resulting refined list of MLTCs were categorised as chronic physical, mental, or infectious diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA key consideration was how to define \u0026ldquo;chronic,\u0026rdquo; as published definitions range from diseases with a duration of greater than 3 to 12 months [9, 10], Rather than applying a strict threshold, the panel used clinical judgement to determine whether each condition was appropriately considered long-term. Conditions were excluded if they were acute, hereditary, or congenital, associated with childhood (under 18), or pregnancy related. Long COVID was also excluded, as it is typically coded as a standalone entity encompassing multiple complications, making it difficult to classify within a single-condition framework.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eStage 3: Patient and Public Involvement (PPIE)\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eOur PPIE group comprised 6\u0026ndash;8 individuals with lived experience of MLTCs or of caring for someone with them. The group was intentionally diverse, including people from different ethnic and socioeconomic backgrounds and seldom-heard communities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants engaged in three phases: initial sessions using online whiteboards exploring what MLTCs meant to them; reviews comparing the clinical condition list with their own; and mind-mapping exercises to integrate lived and clinical perspectives. These activities revealed key differences in language and symptom framing, helping ensure the final framework aligned with both patient experience and clinical understanding.\u003c/p\u003e\n\u003ch4\u003eStages 4 and 5: Updated literature and final clinical review\u003c/h4\u003e\n\u003cp\u003eFollowing the initial review stages, we re-examined the literature and coding sources to ensure completeness and incorporate new or overlooked conditions. Updates from the PPIE process were also considered. The clinical panel then undertook a final round of structured review and adjudication to refine the list, resolve outstanding disagreements, and confirm consensus on the final set of conditions.\u003c/p\u003e\n\u003ch3\u003eCodelist development\u003c/h3\u003e\n\u003ch4\u003eHarmonised Codelist Development\u003c/h4\u003e\n\u003cp\u003eFollowing condition selection, we developed comprehensive phenotyping algorithms for the selected condition set across three coding systems relevant to UK primary care research: Systematized Nomenclature of Medicine \u0026ndash; Clinical Terms (SNOMED CT, UK Clinical Extension \u0026amp; International Edition), Read Code Version 2, and CPRD (Clinical Practice Research Datalink) Aurum Medcodeids. These systems reflect the range of EHRs and research datasets included in the InflAIM programme.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eHierarchical Algorithm Development\u003c/h4\u003e\n\u003cp\u003eWe implemented a hierarchical approach to developing phenotyping algorithms, prioritising sources by methodological rigor, validation status, and research adoption. This ensured consistent quality and maximised semantic interoperability with epidemiological literature. Our primary source was the CALIBER \u0026nbsp;(CArdiovascular disease research using LInked Bespoke studies and Electronic health Records) algorithms (Kuan et al.[23]), chosen for their validated definitions and reproducibility across multiple studies. As CALIBER was developed using the CPRD GOLD database, we adapted codelists for CPRD Aurum using the mapping approach from Head et al.[19]. Secondary sources included the HDR UK Phenotype Library, recognised for expert clinical validation, and the MULTIPLY (MULTI-national consortia to study Polygenic risk scores by Large-scale health sYstems Initiative), which provides consensus-based phenotypes with transparent methods and primary care relevance [11].\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eSemi-Automated Development Process\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eFor conditions lacking published definitions, we implemented a systematic semi-automated development process. This began with ICD-10 concept expansion using the WHO API (World Health Organization, Application Programming Interface) (2019), followed by cross-mapping using NHS TRUD (Technology Reference data Update Distribution) files and the CPRD Aurum Medical Dictionary. Prioritising sensitivity in the initial stage, we applied automated filtering across five sequential categories: procedure-related, administrative and non-diagnostic, acute event, congenital/hereditary, and finally \u0026quot;not otherwise specified\u0026quot; or \u0026quot;unspecified\u0026quot; code exclusions. Where a code appeared across multiple condition lists, we used clinical adjudication to assign it appropriately.\u003c/p\u003e\n\u003cp\u003eSupplementary Codelist Development\u003c/p\u003e\n\u003cp\u003eSupplementary analyses followed two steps. First, the clinical panel identified a subset of the primary condition list as having significant inflammatory pathophysiology, based on expert consensus. Second, for autoimmune and infectious diseases, we systematically sourced published codelists (e.g. Lyall et al.[18], Watson et al.[25], Janbek et al.[26]). These were harmonised across coding systems using the CPRD Aurum Medical Dictionary and the same methods as for primary codelists. Final lists were clinically curated and de-duplicated to exclude overlaps with the primary MLTC set.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eCondition selection outcome\u003c/h3\u003e\n\u003cp\u003eThe literature review identified 363 candidate conditions across eight key sources. Through a four-stage co-production process\u0026mdash;including clinical expert review, patient and public involvement, and final adjudication\u0026mdash;the list was refined to 60 conditions (Table 1), each agreed by unanimous clinical consensus as relevant to the definition of MLTC for the InflAIM programme (Figure 2). From the list of 60 diseases, we labelled a condition as \u0026lsquo;inflammatory\u0026rsquo; if it involved active, immune-driven tissue inflammation. For disease groupings, inclusion as inflammatory was based on whether key subtypes within the group clearly met this criterion. Autoimmune and infectious diseases were considered separately.\u003c/p\u003e\n\u003cp\u003eInsights from the PPIE group influenced several aspects of condition selection. They highlighted distinctions between clinical diagnoses and lived symptom experience\u0026mdash;particularly in mental health and fluctuating conditions\u0026mdash;prompting closer scrutiny of coding practices. PPIE members described gout as chronic yet presenting as acute flares with serious consequences for mobility, quality of life, and mental health. Similarly, inflammatory bowel disease (IBD) was described as episodic, with remission periods punctuated by debilitating flares. While these fluctuations may not always appear in clinical records, participants consistently framed them as central to the lived burden of disease. The group noted that treatments can reshape experience, introducing side effects or new susceptibilities not always captured in coding. This input supported retention of conditions that may be inconsistently coded but consistently described as impactful. Participants further stressed that some conditions affect multiple organ systems, prompting scrutiny of how conditions were grouped for analysis.\u003c/p\u003e\n\u003cp\u003eThe final list included conditions from a wide range of clinical domains: cardiovascular, metabolic, endocrine, musculoskeletal, respiratory, neurological, mental health, gastrointestinal, renal, dermatological, haematological, oncological, and infectious diseases.\u003c/p\u003e\n\u003cp\u003eOf the final MLTC list, 17 conditions were identified by clinical consensus as having significant inflammatory pathophysiology, including rheumatoid arthritis, inflammatory bowel disease and asthma (see Table 1, inflammatory conditions indicated by *).\u003c/p\u003e\n\u003cp\u003eTo further support InflAIM\u0026rsquo;s focus on inflammation, we identified a comprehensive list of autoimmune and infectious disease conditions through \u0026nbsp;separate literature and clinical reviews for targeted phenotyping.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCodelist Development\u003c/h3\u003e\n\u003cp\u003ePhenotyping algorithms were successfully developed for all 60 conditions using a hierarchical approach to sourcing, based on methodological rigour, validation status, and research adoption.\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eCALIBER provided codelists for 43 conditions (71.6%), forming the majority of the final set.\u003c/li\u003e\n \u003cli\u003eHDR UK Phenotype Library contributed 4 (6.6%), including ischaemic heart disease and tuberculosis.\u003c/li\u003e\n \u003cli\u003eMULTIPLY Initiative provided 8 (13.3%), including systemic connective tissue disorders.\u003c/li\u003e\n \u003cli\u003eCodelists for seven conditions (11.6%) were developed de novo through a semi-automated process, for conditions such as hypoparathyroidism and in situ neoplasms.\u003c/li\u003e\n \u003cli\u003eOf the conditions sourced above, two (3.2%) \u0026nbsp;required hybrid approaches combining multiple sources.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe initial compilation included 8,898 Medcodeids across three UK coding systems. Following automated and manual filtering to improve diagnostic precision, 1,472 codes (16.5%) were excluded, resulting in 7,426 validated codes. These were harmonised across SNOMED CT, Read Version 2, and CPRD Aurum Medcodeids\u0026nbsp;(Figure 2).\u003c/p\u003e\n\u003cp\u003eAutomated quality checks flagged 92 SNOMED CT concepts and 160 Read/Medcodeids that mapped to multiple conditions. These were adjudicated by clinical experts, with codes either reassigned to a single most appropriate condition or retained across multiple lists where justified by clinical overlap (Additional file, Table 1) . The taxonomic structure of the clinical codelists for 60 long-term conditions (LTCs) is visualized as a radial dendrogram in the Additional file, Figure 1.\u003c/p\u003e\n\u003cp\u003eSupplementary codelists were then developed for 228 autoimmune and 2,107 infectious diseases, based on a systematic sourcing and harmonisation process . The autoimmune codelist included 489 Read codes and 341 SNOMED CT codes. The infectious disease list comprised 2,107 Read codes and 1,390 SNOMED CT codes (see Additional files, Tables 2 and 3).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a systematically developed MLTC framework, tailored to support inflammation-focused research. Through a four-stage co-production process, we integrated evidence from the literature, clinical consensus, and public involvement to define a robust condition set and harmonised codelists. This addresses limitations in prior frameworks, which often lack transparency, patient input, or interchangeability across coding systems.\u003c/p\u003e\u003cp\u003eOur approach was shaped by the aims of the InflAIM programme, exploring inflammatory mechanisms in multimorbidity. This influenced condition selection\u0026mdash;prioritising those with inflammatory pathophysiology\u0026mdash;and the decision to develop supplementary autoimmune and infectious disease codelists. Choices around granularity were guided by biological relevance; for example, grouping spinal degenerative conditions while treating inflammatory arthropathies as distinct.\u003c/p\u003e\u003cp\u003ePatient and public contributors played a key role in refining the framework. Their insights prompted scrutiny of mental health and fluctuating conditions and highlighted mismatches between clinical codes and lived experience. This ensured the framework reflects not only diagnostic logic but also real-world complexity.\u003c/p\u003e\u003cp\u003eCodelists were developed via a hierarchical strategy prioritising validated algorithms across three UK coding systems. Where definitions were absent, we derived new algorithms using a semi-automated pipeline with clinical adjudication. Automated quality checks improved specificity and resolved overlaps. Primarily built for CPRD Aurum, the codelists can be applied to other EHR datasets (e.g., UK Biobank) using established mapping tables, enhancing reproducibility.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003e Our literature review focused on key sources, not exhaustive coverage. Clinical review involved judgement calls on chronicity and condition grouping. Despite rigorous harmonisation, some coding inconsistencies may persist. The PPIE process was qualitative, and while influential, its impact on final decisions is not easily quantifiable.\u003c/p\u003e\u003cp\u003eNonetheless, this work offers a flexible, transparent, and interoperable MLTC identification framework with broad applicability to other domains. By explicitly incorporating inflammation as a focal mechanism, and combining clinical evidence with patient perspectives, the approach strengthens the validity and relevance of MLTC research.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMLTC: Multiple Long-Term Conditions\u003cbr\u003e\u0026nbsp;NICE: National Institute for Health and Care Excellence\u003c/p\u003e\n\u003cp\u003eNIHR: National Institute for Health and Care Research\u003c/p\u003e\n\u003cp\u003eEHR: Electronic Health Records\u003c/p\u003e\n\u003cp\u003ePPIE: Patient and Public Involvement and Engagement\u003cbr\u003e\u0026nbsp;CPRD: Clinical Practice Research Datalink IBD: Inflammatory Bowel Disease\u003c/p\u003e\n\u003cp\u003eICD-10: International Classification of Diseases, 10th Revision\u003cbr\u003e\u0026nbsp;SNOMED CT: Systematized Nomenclature of Medicine Clinical Terms\u003cbr\u003e\u0026nbsp;CALIBER: CArdiovascular disease research using LInked Bespoke studies and Electronic health Records\u003c/p\u003e\n\u003cp\u003eAI MULTIPLY Initiative: MULTI-national consortia to study Polygenic risk scores by Large-scale health sYstems\u003c/p\u003e\n\u003cp\u003eTRUD: Technology Reference data Update Distribution HDR UK: Health Data Research UK\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organisation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNHS: National Health Service\u003c/p\u003e\n\u003cp\u003eAPI: Application Programming Interface\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate \u0026ndash;\u003c/h2\u003e\n\u003cp\u003ePPIE involvement and engagement in the InflAIM programme was approved by the University of Chester, Faculty of Health, Medicine and Society, School of Nursing and Midwifery\u0026rsquo;s Research Ethics Sub-Committee in May 2025. (REC Reference: FRC0425-954). PPIE Participants were informed about the aims and process of the programme at recruitment, provided with a programme handbook, and advised that they could withdraw from the programme at any time. Participants also provided written or verbal consent for the outcomes of the meetings to be shared for academic dissemination and other relevant purposes.\u003c/p\u003e\n\u003ch2\u003eConsent for publication -\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by NIHR grant NIHR205461as part of the InflAIM programme.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualisation: D.A., C.D., A.J.M.Data curation: D.A.Methodology: D.A., C.D., E.K., B.A.H.J., C.F., J.F., J.G., A.M.Literature review: D.A., C.D.Validation of clinical conditions: A.J.M., C.F., J.F.PPIE: J.G., C.D.Writing \u0026ndash; original draft preparation: D.A., C.D.Writing \u0026ndash; review and editing: D.A., C.D., E.K., B.A.H.J., C.F., J.F., J.G., A.J.M.All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank the PPIE group members for their invaluable contributions to this research and the clinical experts who provided consensus review. We acknowledge the whole InflAIM team for their support and advice.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe complete validated codelist repository are available at [https://github.com/Inflaim-Study](https:/github.com/Inflaim-Study) . All codelists are provided in multiple formats compatible with major UK electronic health record systems.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChowdhury, S.R., et al., \u003cem\u003eGlobal and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis.\u003c/em\u003e EClinicalMedicine, 2023. \u003cstrong\u003e57\u003c/strong\u003e: p. 101860.\u003c/li\u003e\n\u003cli\u003eBarnett, K., et al., \u003cem\u003eEpidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.\u003c/em\u003e Lancet, 2012. \u003cstrong\u003e380\u003c/strong\u003e(9836): p. 37\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eHo, I.S.S., et al., \u003cem\u003eMeasuring multimorbidity in research: Delphi consensus study.\u003c/em\u003e BMJ Med, 2022. \u003cstrong\u003e1\u003c/strong\u003e(1): p. e000247.\u003c/li\u003e\n\u003cli\u003eMacRae, C., et al., \u003cem\u003eImpact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service.\u003c/em\u003e BMC Medicine, 2023. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 309.\u003c/li\u003e\n\u003cli\u003eGEMINI Multimorbidity, \u003cem\u003eGEMINI: Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions\u003c/em\u003e. 2025.\u003c/li\u003e\n\u003cli\u003eDodds, R.M., et al., \u003cem\u003eSimple approaches to characterising multiple long-term conditions (multimorbidity) and rates of emergency hospital admission: Findings from 495,465 UK Biobank participants.\u003c/em\u003e J Intern Med, 2023. \u003cstrong\u003e293\u003c/strong\u003e(1): p. 100\u0026ndash;109.\u003c/li\u003e\n\u003cli\u003eLyall, L.M., et al., \u003cem\u003eThe associations between self-reported depression, self-reported chronic inflammatory conditions and cognitive abilities in UK Biobank.\u003c/em\u003e Eur Psychiatry, 2019. \u003cstrong\u003e60\u003c/strong\u003e: p. 63\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eHead, A., et al., \u003cem\u003eInequalities in incident and prevalent multimorbidity in England, 2004\u0026ndash;19: a population-based, descriptive study.\u003c/em\u003e The Lancet Healthy Longevity, 2021. \u003cstrong\u003e2\u003c/strong\u003e(8): p. e489\u0026ndash;e497.\u003c/li\u003e\n\u003cli\u003eBeaney, T., et al., \u003cem\u003eEffect of timeframes to define long term conditions and sociodemographic factors on prevalence of multimorbidity using disease code frequency in primary care electronic health records: retrospective study.\u003c/em\u003e BMJ Med, 2024. \u003cstrong\u003e3\u003c/strong\u003e(1): p. e000474.\u003c/li\u003e\n\u003cli\u003eGoodman, R.A., et al., \u003cem\u003eDefining and measuring chronic conditions: imperatives for research, policy, program, and practice.\u003c/em\u003e Prev Chronic Dis, 2013. \u003cstrong\u003e10\u003c/strong\u003e: p. E66.\u003c/li\u003e\n\u003cli\u003eKuan, V., et al., \u003cem\u003eA chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service.\u003c/em\u003e Lancet Digit Health, 2019. \u003cstrong\u003e1\u003c/strong\u003e(2): p. e63\u0026ndash;e77.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 \u0026ndash; MLTC conditions, ICD-10 chapter and Inflammatory indicator\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\u003cstrong\u003eICD-10\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003e\u003cstrong\u003eMLTC Condition\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eA15-A19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eTuberculosis*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eA69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eOther spirochaetal infections*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eB18-B19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eChronic viral hepatitis*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eB20-B24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eHIV disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003eC00-C80, C97, D37-D44, D47, D48\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 423px;\"\u003eMalignant neoplasms\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eC81-C96\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 423px;\"\u003eMalignant neoplasms, stated or presumed to be primary, of lymphoid, haematopoietic and related tissue\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eD00-D09\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 423px;\"\u003eIn situ neoplasms\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eD50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eIron deficiency anaemia\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eE00-E07\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDisorders of thyroid gland\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eE10-E14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDiabetes\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eE20\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eHypoparathyroidism\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eE21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eHyperparathyroidism\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eE27\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eAddison\u0026apos;s disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003eF00-F03\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 423px;\"\u003eDementia\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eF10-F19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDrug or alcohol misuse\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eF20-F29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eSchizophrenia\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eF30-F39\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eMood disorders\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eF40-F48\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eNeurotic, stress-related and somatoform disorders\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eF50\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eEating disorders\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eG20-G21\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eParkinson disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eG35, H46\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eMultiple sclerosis (and optic neuritis)*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eG40, F80.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eEpilepsy\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eG43\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eMigraine\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eG47\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eSleep disorders\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003eG61-G64\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003ePolyneuropathies and other disorders of the peripheral nervous system\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eH25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eCataract\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eH33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eRetinal detachments and breaks\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eH35\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eOther retinal disorders (Macular degeneration)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eH40-H42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eGlaucoma\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eH53-H54\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eVisual disturbances and blindness\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eH81.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eMeniere disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eH90-H91\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eConductive and sensorineural hearing loss\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eI05-I09\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eChronic rheumatic heart diseases*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eI10-I15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eHypertensive diseases\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eI20-I25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eIschaemic heart diseases\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eI30-I52\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eOther forms of heart disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003eI60-I69\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 423px;\"\u003eCerebrovascular Disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003eI70-I79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDisease of arteries, arterioles and capillaries\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003eI80-I89\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 423px;\"\u003eDiseases of veins, lymphatic vessels and lymph nodes, note elsewhere classified\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eJ30-J32\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eRhinitis and Sinusitis*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eJ40-J47\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eChronic lower respiratory diseases*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eJ84\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eOther interstitial pulmonary diseases*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eK21, K27, K29\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDiseases of oesophagus, stomach and duodenum\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eK50-K52\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eNoninfective enteritis and colitis*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eK57\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDiverticular disease of the intestine*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eK58\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eIrritable Bowel Syndrome\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003eK70-K77\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 423px;\"\u003eDiseases of the liver\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eK80-K87\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDisorders of gallbladder, biliary tract and pancreas\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eL20-L30\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDermatitis and eczema*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eL40\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003ePsoriasis*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eM05-M14\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eInflammatory polyarthropathies*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eM15-M19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eArthrosis\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eM30-M36\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eSystemic connective tissue disorders*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eM45\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eAnkylosing spondylitis*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eM47, M51\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eSpinal degenerative disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eM80-M85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDisorders of bone density and structure\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eN18-N19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eChronic kidney disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eN40-N42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eDisorders of the prostate\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eN70-N77\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eInflammatory diseases of the female pelvic organs*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003eN80\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003eEndometriosis*\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Inflammatory condition\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple Long-Term Conditions, Multimorbidity, Co-production, Patient and Public Involvement, Medical Coding, Electronic Health Records, Inflammation","lastPublishedDoi":"10.21203/rs.3.rs-7773449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7773449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eInflammation is implicated in many chronic diseases, but its role in driving multiple long-term conditions (MLTCs) remains unclear. The InflAIM programme aims to explore this potential link. Existing MLTC codelists are valuable but have not been designed for inflammation-focused research and often lack harmonisation across coding systems.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo develop a transparent, co-produced MLTC framework with harmonised codelists for epidemiological research, focusing on inflammatory conditions within the InflAIM programme.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing a systematic, co-produced approach, we combined literature review, clinical consensus, and patient input. From eight sources, 363 candidate conditions were identified, refined to 60 validated MLTCs. Patients informed terminology and diagnostic experiences. Codelists were drawn from CALIBER (43), HDR UK Phenotype Library (4), MULTIPLY (8), with 7 developed de novo. Harmonisation covered Read v2, SNOMED CT, and Medcodes. Quality assurance excluded 16.5% of codes and resolved 92 overlaps.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe produced harmonised codelists for 60 MLTCs, comprising 7,426 validated MedCodes. Clinical consensus identified 17 conditions with potential inflammatory components. Additional lists were developed for autoimmune and infectious diseases to support inflammation-focused analyses.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis framework extends existing resources to meet the needs of inflammation-focused research, offering a transparent, harmonised, and patient-informed foundation for the InflAIM programme.\u003c/p\u003e","manuscriptTitle":"Harmonising Definitions of Multiple Long Terms Conditions for Inflammation Research: A Co-Production Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 14:51:31","doi":"10.21203/rs.3.rs-7773449/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T17:11:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T11:44:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T21:03:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T04:01:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13286844819194316040269370512351605479","date":"2025-10-17T10:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177623072309288118453556138019479592621","date":"2025-10-16T22:24:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329360095171794063258578780748208176385","date":"2025-10-16T18:30:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34324021108026688903315306263940817895","date":"2025-10-16T02:57:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T10:14:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-14T09:58:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T08:41:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T20:32:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2025-10-08T20:29:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16221798-d2e4-47e7-85ec-93f71316c771","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:24:19+00:00","versionOfRecord":{"articleIdentity":"rs-7773449","link":"https://doi.org/10.1186/s13104-025-07626-0","journal":{"identity":"bmc-research-notes","isVorOnly":false,"title":"BMC Research Notes"},"publishedOn":"2026-01-13 16:30:30","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-10-29 14:51:31","video":"","vorDoi":"10.1186/s13104-025-07626-0","vorDoiUrl":"https://doi.org/10.1186/s13104-025-07626-0","workflowStages":[]},"version":"v1","identity":"rs-7773449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7773449","identity":"rs-7773449","version":["v1"]},"buildId":"veTbxFhMMB0_faC6-Wkog","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}