Optimum Patient Care Research Database Research Articles

A machine learning algorithm for the detection of paroxysmal nocturnal haemoglobinuria (PNH) in UK primary care electronic health records

BackgroundParoxysmal Nocturnal Haemoglobinuria (PNH) is an ultra-rare, acquired disorder that is challenging to diagnose due to varied symptoms, heterogeneous patient presentations, and lack of awareness among healthcare professionals. This leads to frequent misdiagnosis and delays in diagnosis. This study evaluated the feasibility of a machine learning model to identify undiagnosed PNH patients using structured electronic health records.MethodsThe study used data from the Optimum Patient Care Research Database, which contains electronic health records from general practitioner (GP) practices across the United Kingdom. PNH patients were identified by the presence, and control patients by the absence of a PNH diagnosis code in their records. Clinical features (symptoms, diagnoses, healthcare utilisation) from 131 patients in the PNH group and 593,838 patients in the control group, were inputted to a tree-based XGBoost machine learning model to classify patients as either “positive” or “negative” for PNH suspicion. The algorithm was finalised after additional exclusions and inclusions applied. Performance was assessed using positive predictive value (PPV), recall and specificity. As the sample used to develop the algorithm was not representative of the true population prevalence, PPV was additionally adjusted to reflect performance in the wider population.ResultsOf all the patients in the PNH group, 27% were classified as positive (recall). 99.99% of the control group were classified as negative (specificity). Of all the patients classified as positive, 60.4% had a diagnosis of PNH in their record (PPV). The PPV adjusted for the population prevalence of PNH was 19.59 suggesting nearly 1 in 5 patients flagged may warrant further PNH investigation. The key clinical features in the model were aplastic anaemia, pancytopenia, haemolytic anaemia, myelodysplastic syndrome, and Budd-Chiari syndrome.ConclusionThis is the first study to combine clinical understanding of PNH with machine learning, demonstrating the ability to discriminate between PNH and control patients in retrospective electronic health records. With further investigation and validation, this algorithm could be deployed on live health data, potentially leading to earlier diagnosis for patients who currently experience long diagnostic delays or remain undiagnosed.

Exacerbation history and blood eosinophil count prior to diagnosis of COPD and risk of subsequent exacerbations.

Prior exacerbation history is used to guide initial maintenance therapy in COPD; however, the recommendations were derived from patients already diagnosed and treated. We assessed the rates of moderate (i.e. treated with antibiotics and/or systemic corticosteroids) and severe (i.e. hospitalised) exacerbations in the year following diagnosis in patients newly diagnosed with COPD according to their prior history of exacerbations, blood eosinophil count (BEC) and whether maintenance therapy was started. Data were extracted from the Optimum Patient Care Research Database. 73 189 patients were included. 61.9% had no exacerbations prior to diagnosis, 21.5% had 1 moderate, 16.5% had ≥2 moderate and 0.3% had ≥1 severe. 50% were started on maintenance therapy. In patients not started on maintenance therapy the rates of moderate exacerbations in the year after diagnosis in patients with no, 1 moderate, ≥2 moderate and ≥1 severe prior exacerbations were 0.34 (95% CI 0.33-0.35), 0.59 (95% CI 0.56-0.61), 1.18 (95% CI 1.14-1.23) and 1.21 (95% CI 0.73-1.69), respectively. Similar results were seen in patients started on maintenance therapy. BEC did not add significantly to the prediction of future exacerbation risk. A single moderate exacerbation in the year prior to diagnosis increases the risk of subsequent exacerbations, and more frequent or severe exacerbations prior to diagnosis are associated with a higher risk.

Identifying patients with undiagnosed small intestinal neuroendocrine tumours in primary care using statistical and machine learning: model development and validation study

BackgroundNeuroendocrine tumours (NETs) are increasing in incidence, often diagnosed at advanced stages, and individuals may experience years of diagnostic delay, particularly when arising from the small intestine (SI). Clinical prediction models could present novel opportunities for case finding in primary care.MethodsAn open cohort of adults (18+ years) contributing data to the Optimum Patient Care Research Database between 1st Jan 2000 and 30th March 2023 was identified. This database collects de-identified data from general practices in the UK. Model development approaches comprised logistic regression, penalised regression, and XGBoost. Performance (discrimination and calibration) was assessed using internal-external cross-validation. Decision analysis curves compared clinical utility.ResultsOf 11.7 million individuals, 382 had recorded SI NET diagnoses (0.003%). The XGBoost model had the highest AUC (0.869, 95% confidence interval [CI]: 0.841–0.898) but was mildly miscalibrated (slope 1.165, 95% CI: 1.088–1.243; calibration-in-the-large 0.010, 95% CI: −0.164 to 0.185). Clinical utility was similar across all models.DiscussionMultivariable prediction models may have clinical utility in identifying individuals with undiagnosed SI NETs using information in their primary care records. Further evaluation including external validation and health economics modelling may identify cost-effective strategies for case finding for this uncommon tumour.

Enhancing Transparency in Optimum Patient Care Research Database Access Procedures: A Multifaceted Approach Addressing Improvements in Transparency Standards

Enhancing Transparency in Optimum Patient Care Research Database Access Procedures: A Multifaceted Approach Addressing Improvements in Transparency Standards

Budesonide/Glycopyrrolate/Formoterol for the Management of COPD in a UK Primary Care Population: Real-World Use and Early Medication Success.

Real-life research is needed to evaluate the effectiveness of budesonide/glycopyrrolate/formoterol (BGF) in routine COPD primary care management. We assessed the frequency of medication success among patients with COPD who initiated BGF using real-world data. Patients with a recorded diagnostic COPD code who started BGF with ≥2 prescriptions within 90-days were identified in the UK Optimum Patient Care Research Database and followed from first prescription until censoring at the end of follow-up (180-days), death, leaving database or end of data at 24/10/2022. The primary outcome was medication success at 90-days post-BGF initiation, defined as no major cardiac or respiratory event (ie no complicated COPD exacerbation, hospitalization for any respiratory event, myocardial infarction, new/hospitalized heart failure, and death) and no incidence of pneumonia. Medication success was also assessed at 180-days post-BGF initiation. Overall real-life medication success was claimed if the lower 95% confidence interval (CI) for the proportion of patients meeting the primary outcome was ≥70% (defined a priori). Two hundred eighty-five patients were included. Prior to BGF initiation, these patients often had severe airflow obstruction (mean ppFEV1: 54.5%), were highly symptomatic (mMRC ≥2: 77.9% (n = 205/263); mean CAT score: 21.7 (SD 7.8)), with evidence of short-acting β2-agonist (SABA) over-use (≥3 inhalers/year: 62.1%, n=179/285), repeat OCS prescriptions (≥2 courses/year: 33.0%, n = 95/285) and multiple primary care consultations (≥2 visits/year: 61.1%, n = 174/285). Overall, 39.6% of patients (n = 113/285) switched from previous triple therapies. Real-life medication success was achieved by 96.5% of patients (n = 275/285 [95% CI: 93.6, 98.3]) during 90-days treatment with BGF and by 91.8% (n = 169/184 [95% CI: 86.9, 95.4]) of patients at 180-days. The prescribed daily dose of SABA remained stable over the study period. The majority of patients initiating BGF experienced real-life medication success reflecting the absence of severe cardiopulmonary events. These benefits were apparent after 90-days of treatment and sustained over 180-days.

Trends in Systemic Glucocorticoid Utilization in the United Kingdom from 1990 to 2019: A Population-Based, Serial Cross-Sectional Analysis.

Associations between systemic glucocorticoid (SGC) exposure and risk for adverse outcomes have spurred a move toward steroid-sparing treatment strategies. Real-world changes in SGC exposure over time, after the introduction of steroid-sparing treatment strategies, reveal areas of successful risk mitigation as well as unmet needs. A population-based ecological study was performed from the Optimum Patient Care Research Database to describe SGC prescribing trends of steroid-sparing treatment strategies in primary care practices before and after licensure of biologics in the United Kingdom from 1990 to 2019. Each analysis year included patients aged ≥5 years who were registered for ≥1 year with a participating primary care practice. The primary analysis was SGC exposure, defined as total cumulative SGC dose per patient per year, for asthma, severe asthma, chronic obstructive pulmonary disease (COPD), nasal polyps, Crohn's disease, rheumatoid arthritis, ulcerative colitis, and systemic lupus erythematosus. Secondary outcomes were percentages of patients prescribed SGCs and number of SGC prescriptions per patient per year. The number of patients who met study inclusion criteria ranged from 219,862 (1990) to 1,261,550 (2019). At the population level, patients with asthma or COPD accounted for 67.7% to 73.2% of patients per year with an SGC prescription. Over three decades, decreases in SGC total yearly dose ≥1000 mg have been achieved in multiple conditions. Patients with COPD prescribed SGCs increased from 5.8% (1990) to 34.8% (2017). SGC prescribing trends for severe asthma, Crohn's disease, and ulcerative colitis show decreased prescribing trends after the introduction of biologics. Decreases in total yearly SGC doses have been shown in multiple conditions; however, for conditions such as severe asthma and COPD, an unmet need remains for increased awareness of SGC burden and the adoption or development of SGC-sparing alternatives to reduce overuse.

The Diagnostic Odyssey in Children and Adolescents With X-linked Hypophosphatemia: Population-Based, Case-Control Study.

X-linked hypophosphatemia (XLH) is a rare genetic disorder causing renal phosphate wasting, which predicates musculoskeletal manifestations such as rickets. Diagnosis is often delayed. To explore the recording of clinical features, and the diagnostic odyssey of children and adolescents with XLH in primary care electronic healthcare records (EHRs) in the United Kingdom. Using the Optimum Patient Care Research Database, individuals aged 20 years or younger after January 1, 2000, at date of recorded XLH diagnosis were identified using Systematized Nomenclature of Medicine Clinical Terms (SNOMED)/Read codes and age-matched to 100 controls. Recording of XLH-related clinical features was summarized then compared between cases and controls using chi-squared or Fisher's exact test. In total, 261 XLH cases were identified; 99 met the inclusion criteria. Of these, 84/99 had at least 1 XLH-related clinical feature recorded in their primary care EHR. Clinical codes for rickets, genu varum, and low phosphate were recorded prior to XLH diagnosis in under 20% of cases (median of 1, 1, and 3 years prior, respectively). Rickets, genu varum, low phosphate, nephrocalcinosis, and growth delay were significantly more likely to be recorded in cases. This characterization of the EHR phenotypes of children and adolescents with XLH may inform future case-finding approaches to expedite diagnosis in primary care.

The Association Between Short-Acting β2-Agonist Over-Prescription, and Patient-Reported Acquisition and Use on Asthma Control and Exacerbations: Data from Australia.

In Australia, short-acting β2-agonists (SABA) are available both over the counter (OTC) and on prescription. This ease of access may impact SABA use in the Australian population. Our aim was to assess patterns and outcome associations of prescribed, acquired OTC and reported use of SABA by Australians with asthma. This was a cross-sectional study, using data derived from primary care electronic medical records (EMRs) and patient completed questionnaires within Optimum Patient Care Research Database Australia (OPCRDA). A total of 720 individuals aged ≥ 12years with an asthma diagnosis in their EMRs and receiving asthma therapy were included. The annual number of SABA inhalers authorised on prescription, acquired OTC and reported, and the association with self-reported exacerbations and asthma control were investigated. 92.9% (n = 380/409) of individuals issued with SABA prescription were authorised ≥ 3 inhalers annually, although this differed from self-reported usage. Of individuals reporting SABA use (n = 546) in the last 12months, 37.0% reported using ≥ 3 inhalers. These patients who reported SABA overuse experienced 2.52 (95% confidence interval [CI] 1.73-3.70) times more severe exacerbations and were 4.51 times (95% CI 3.13-6.55) more likely to have poor asthma control than those who reported using 1-2 SABA inhalers. Patients who did not receive SABA on prescription (43.2%; n = 311/720) also experienced 2.71 (95% CI 1.07-7.26) times more severe exacerbations than those prescribed 1-2 inhalers. Of these patients, 38.9% reported using OTC SABA and other prescription medications, 26.4% reported using SABA OTC as their only asthma medication, 13.2% were prescribed other therapies but not SABA OTC and 14.5% were not using any medication. Both self-reported SABA overuse and zero SABA prescriptions were associated with poor asthma outcomes. The disconnect between prescribing authorisation, OTC availability and actual use, make it difficult for clinicians to quantify SABA use.

Impact of mental health comorbidity in children and young adults with inflammatory bowel disease: a UK population-based cohort study

ObjectiveTo evaluate the impact of mental health comorbidity in children and young adults with inflammatory bowel disease (IBD).DesignRetrospective observational study.SettingRepresentative population, routinely collected primary care data from the UK Optimum...

Impact of sex on severe asthma: a cross-sectional retrospective analysis of UK primary and specialist care

IntroductionAfter puberty, females are more likely to develop asthma and in a more severe form than males. The associations between asthma and sex are complex with multiple intrinsic and external...

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care.

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

The risk of venous thromboembolism in atopic dermatitis: A matched cohort analysis in UK primary care.

Atopic dermatitis (AD) is a common chronic inflammatory skin condition. Whilst other chronic inflammatory conditions are associated with increased risk of venous thromboembolism (VTE), associations between AD and VTE have not been established. We examined whether AD is associated with increased risk of VTE in a population-based study. Electronic health records were extracted from UK general practices contributing to Optimum Patient Care Research Database. All adults with AD over 2010-2020 identified (n=150,975), and were age and sex matched with unaffected controls (n=603,770). Risk of VTE, consisting of pulmonary embolism (PE) or deep vein thrombosis (DVT), were compared in people with AD versus controls using Cox proportional hazard models. PE and DVT were examined separately as secondary outcomes. 150,975 adults with active AD were identified and matched with 603,770 unaffected controls. During the study, 2,576 of the people with active AD and 7,563 of the matched controls developed VTE. Individuals with AD had a higher risk of VTE than controls (adjusted hazard ratio [aHR] 1.17, 95% Confidence Interval [CI] 1.12-1.22). When assessing VTE components, AD was associated with a higher risk of DVT (aHR 1.30, 95%CI 1.23-1.37) but not PE (aHR 0.94, 95%CI 0.87-1.02). VTE risk was greater in older people with AD (≥65 years: aHR 1.22, 95%CI 1.15-1.29; 45-65 years: aHR 1.15, 95%CI 1.05-1.26; <45 years: aHR 1.07, 95%CI 0.97-1.19) and those with obesity (BMI≥30: aHR 1.25, 95%CI 1.12-1.39; BMI <30: aHR 1.08, 95%CI 1.01-1.15). Risk was broadly consistent across mild, moderate or severe AD. AD is associated with a small increase in risk of VTE and DVT, with no increase in risk of PE. The magnitude of this risk increase is modest in younger people, and those without obesity.

007 Vitiligo is associated with a reduction in incidence of melanoma and nonmelanoma skin cancer: UK population-based cohort study

Abstract Vitiligo is a common, acquired depigmenting skin disease. There have been concerns that vitiligo and its treatment may increase the risks of skin cancer. In a large UK population-based study, we aimed to establish whether vitiligo is associated with an increased risk of skin cancer. We used routinely collected electronic medical records of adults contributing to the Optimum Patient Care Research Database (2010–2020). Cases of vitiligo were identified using specific diagnosis codes. Those with other depigmenting conditions were excluded. Vitiligo cases were age and sex matched with up to four controls registered at the same general practice and with at least one primary care interaction in the preceding year. The primary outcome was risk of skin cancer; comprisied of melanoma, squamous cell carcinoma (SCC) and basal cell carcinoma (BCC). Individual skin cancers and actinic keratoses (AK) were included as secondary outcomes. Associations between vitiligo and skin cancer were estimated using Cox regression, with adjustment for age, sex, socioeconomic deprivation, ethnicity, body mass index, smoking and alcohol status, immunotherapy, phototherapy and 13 common comorbidities. Prespecified subgroup analysis comprised stratification by sex, age group (18–45 years, 45–65 years and &amp;gt; 65 years), ethnicity (White and non-White) and socioeconomic deprivation. In total, 14 976 patients with vitiligo were matched to 54 392 controls. Over the 10-year study period, 133 patients with vitiligo and 783 controls developed new-onset skin cancer. Vitiligo was associated with a 38% reduced risk of any new-onset skin cancer [adjusted hazard ratio (aHR) 0.62, 95% confidence interval (CI) 0.51–0.74; P &amp;lt; 0.001]. This risk reduction was observed for all individual skin cancer types: melanoma (aHR 0.38, 95% CI 0.23–0.64; P &amp;lt; 0.001), SCC (aHR 0.65, 95% CI 0.48–0.87; P = 0.004), BCC (aHR 0.65, 95% CI 0.51–0.83; P &amp;lt; 0.001) and AK (aHR 0.86, 95% CI 0.75–0.98; P = 0.028). In a subgroup analysis, vitiligo was associated with a reduction in skin cancer risk in people identifying as White (aHR 0.68, 95% CI 0.55–0.84; P &amp;lt; 0.001), but there was no association in those identifying as non-White (aHR 0.93, 95% CI 0.44–1.98). Vitiligo-associated reductions in skin cancer risk were consistently observed in the other prespecified subgroups. Vitiligo is associated with a strong protective effect against common types of skin cancer. Vitiligo-associated risk reduction is most marked for melanoma (62% lower risk). Protective effects associated with vitiligo may be restricted to people with light skin, although skin cancers in the those with darker skin were uncommon, which requires further study. FundingThis research was sponsored by Pfizer. Medical writing and statistical support was provided by Momentum Data and was funded by Pfizer.

Identification of key opportunities for optimising the management of high-risk COPD patients in the UK using the CONQUEST quality standards: an observational longitudinal study.

This study compared management of high-risk COPD patients in the UK to national and international management recommendations and quality standards, including the COllaboratioN on QUality improvement initiative for achieving Excellence in STandards of COPD care (CONQUEST). The primary comparison was in 2019, but trends from 2000 to 2019 were also examined. Patients identified in the Optimum Patient Care Research Database were categorised as newly diagnosed (≤12 months after diagnosis), already diagnosed, and potential COPD (smokers having exacerbation-like events). High-risk patients had a history of ≥2 moderate or ≥1 severe exacerbations in the previous 12 months. For diagnosed patients, the median time between diagnosis and first meeting the high-risk criteria was 617 days (Q1-Q3: 3246). The use of spirometry for diagnosis increased dramatically after 2004 before plateauing and falling in recent years. In 2019, 41% (95% CI 39-44%; n=550/1343) of newly diagnosed patients had no record of spirometry in the previous year, and 45% (95% CI 43-48%; n=352/783) had no record of a COPD medication review within 6 months of treatment initiation or change. In 2019, 39% (n=6893/17,858) of already diagnosed patients had no consideration of exacerbation rates, 46% (95% CI 45-47%; n=4942/10,725) were not offered or referred for pulmonary rehabilitation, and 41% (95% CI 40-42%; n=3026/7361) had not had a COPD review within 6 weeks of respiratory hospitalization. Opportunities for early diagnosis of COPD patients at high risk of exacerbations are being missed. Newly and already diagnosed patients at high-risk are not being assessed or treated promptly. There is substantial scope to improve the assessment and treatment optimisation of these patients. This study is conducted by the Observational & Pragmatic Research International Ltd and was co-funded by Optimum Patient Care and AstraZeneca. No funding was received by the Observational & Pragmatic Research Institute Pte Ltd (OPRI) for its contribution.

Risk of Melanoma and Nonmelanoma Skin Cancer in People with Vitiligo: United Kingdom Population−Based Cohort Study

Although genetic studies have found an inverse relationship between vitiligo and skin cancer, epidemiological evidence is conflicting. We investigated the risk of skin cancer in adults with vitiligo using United Kingdom electronic primary care records from the Optimum Patient Care Research Database 2010-2020. Vitiligo cases were age, sex, and general practitioner practice matched to population controls without vitiligo. Incidence of melanoma, nonmelanoma skin cancers (squamous cell carcinoma and basal cell carcinoma), and actinic keratoses was compared between vitiligo cases and controls using Cox regression. A total of 15,156 vitiligo cases were matched to 60,615 controls. Vitiligo was associated with a 38% reduced risk of new-onset skin cancer (adjusted hazard ratio [aHR]= 0.62, 95% confidence interval [CI]= 0.52-0.75, P < 0.001) and skin cancer subtypes; melanoma (aHR= 0.39, 95% CI= 0.23-0.65, P < 0.001), squamous cell carcinoma (aHR= 0.67, 95% CI= 0.49-0.90, P < 0.01), basal cell carcinoma (aHR= 0.65, 95% CI= 0.51-0.83, P < 0.001). There was no significant association for actinic keratosis (aHR= 0.88, 95% CI= 0.77-1.01). People with vitiligo have a markedly reduced incidence of melanoma and nonmelanoma skin cancer. Given concerns that some treatments, such as phototherapy, may increase skin cancer risk, this finding provides reassurance to people with vitiligo and clinicians managing the condition.

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged &lt;25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85–0.87 and 1.00, 95% CI 0.95–1.05 respectively. In the external validation dataset, which included 2,670 participants aged &lt;25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83–0.88, and calibration slope 1.22, 95% CI 1.09–1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

Data-Resource Profile: United Kingdom Optimum Patient Care Research Database.

Electronic medical records (EMRs) maintained in primary care in the UK and collected and stored in EMR databases offer a world-leading resource for observational clinical research. We aimed to profile one such database: the Optimum Patient Care Research Database (OPCRD). The OPCRD, incepted in 2010, is a growing primary care EMR database collecting data from 992 general practices within the UK. It covers over 16.6 million patients across all four countries within the UK, and is broadly representative of the UK population in terms of age, sex, ethnicity and socio-economic status. Patients have a mean duration of 11.7 years' follow-up (SD 17.50), with a majority having key summary data from birth to last data entry. Data for the OPCRD are collected incrementally monthly and extracted from all of the major clinical software systems used within the UK and across all four coding systems (Read version 2, Read CTV3, SNOMED DM+D and SNOMED CT codes). Via quality-improvement programmes provided to GP surgeries, the OPCRD also includes patient-reported outcomes from a range of disease-specific validated questionnaires, with over 66,000 patient responses on asthma, COPD, and COVID-19. Further, bespoke data collection is possible by working with GPs to collect new research via patient-reported questionnaires. The OPCRD has contributed to over 96 peer-reviewed research publications since its inception encompassing a broad range of medical conditions, including COVID-19. The OPCRD represents a unique resource with great potential to support epidemiological research, from retrospective observational studies through to embedded cluster-randomised trials. Advantages of the OPCRD over other EMR databases are its large size, UK-wide geographical coverage, the availability of up-to-date patient data from all major GP software systems, and the unique collection of patient-reported information on respiratory health.

P822 The negative impact of mental health comorbidity in children and young adults with inflammatory bowel disease: a UK population-based cohort study

Abstract Background Whilst the high burden of mental health comorbidity in people with inflammatory bowel disease (IBD) is now being recognised, less is known about the additional impacts of having concurrent IBD and a mental health condition, especially in children and young adults. Methods We conducted a retrospective, observational study using a large UK primary care dataset (Optimum Patient Care Research Database). Children and young adults (aged 5-25 years) diagnosed with IBD prior to 2015 were identified and grouped into those with and without prevalent mental health conditions (any of depression, anxiety, eating disorders, body image disorder, attention-deficit disorder, behavioural disorders, adjustment disorder, acute stress disorder, schizophrenia, bipolar disorder or post-traumatic stress disorder). The following outcomes were compared over 5 years in the two groups; quality-of-life indicators (bowel symptoms [abdominal pain, discomfort or bloating, diarrhoea], systemic symptoms [fatigue, sleep problems, weight-loss, underweight, growth retardation], emotional function [low mood, substance abuse, deliberate self-harm, suicidal attempt or parasuicide] and social function [absence from school, absence from work, unemployment]), IBD-specific therapy interventions (diet [nutritional supplements], medications [oral and rectal 5-aminosalicylic acid and steroids, antimetabolites and tumour necrosis factor inhibitors] and surgery [abdominal and stoma formation]), and healthcare utilisation (primary care interactions and hospital admissions). Adjustment for confounders was performed using Cox regression for non-recurring outcomes (e.g. stoma formation), and negative binomial or Poisson regression for outcomes with recurring events (e.g. primary care interactions). Results 1,943 individuals with IBD were identified. 295 of these had a prevalent mental health condition. Mental health comorbidity was associated with increased bowel symptoms (adjusted Incident Rate Ratio [aIRR] 1.82; 95% Confidence Interval [CI] 1.33, 2.52), sleep disturbance (adjusted Hazard Ratio [aHR] 1.63; 95% CI 1.02, 2.62), substance abuse (aHR 3.63; 95% CI 1.69, 7.78), primary care interactions (aIRR 1.33; 95% CI 1.12, 1.58) and hospital admissions (aIRR 1.87; 95% CI 1.29, 2.75) (Figure 1). In those &amp;gt;18 years old, mental health comorbidity was associated with increased time off work (aHR 1.55; 95% CI 1.21, 1.99) (Figure 1). Conclusion Mental health comorbidity in children and young adults with IBD is associated with increased IBD symptoms, healthcare utilisation and time off work. Given the impact of mental health comorbidity, it is crucial those affected are monitored and receive appropriate mental health support as part of their multidisciplinary care.

OP28 Children and young adults with inflammatory bowel disease have an increased incidence and risk of developing mental health conditions: a UK population-based cohort study

Abstract Background While it is widely recognised that inflammatory bowel disease (IBD) is associated with depression and anxiety, the burden of mental health conditions in children and young adults with IBD remains unclear. This study assessed this burden using UK population-based data. Methods A retrospective observational study was performed using a large UK primary care dataset (Optimum Patient Care Research Database). Children and young adults aged 5–25 years diagnosed with IBD between 2010 and 2020 were propensity score matched with up to four population controls. The association between IBD and incident depression, anxiety disorder (AD), eating disorders, body image disorders, attention-deficit disorders, behavioural disorders, adjustment disorders, acute stress disorder, schizophrenia, bipolar disorder, post-traumatic stress disorder (PTSD), self-harm, parasuicide (including suicide), sleep disturbance and ‘any mental health condition’ (a composite consisting of all these mental health conditions) was investigated. Absolute risk was derived using Kaplan-Meier analysis. Relative risk of all outcomes up to 10-years in the IBD cohort versus controls was estimated using adjusted Cox proportional hazards models controlling for age, sex, socioeconomic status, ethnicity, and other conditions (asthma, atopic dermatitis, allergic rhinitis, thyroid disease, epilepsy, diabetes mellitus, cancer, juvenile idiopathic arthritis, systemic lupus erythematosus, ankylosing spondylitis, and psoriatic arthritis). Results 3,898 young patients with incident IBD were matched to 15,571 controls. IBD patients were significantly more likely to develop PTSD (adjusted Hazard Ratio [aHR] 2.47, 95% CI 1.23-4.94), eating disorders (aHR 1.85, 95% CI 1.05-3.26), self-harm (aHR 1.49, 95% CI 1.00-2.21), sleep disturbance (aHR 1.40, 95% CI 1.15-1.71), depression (aHR 1.34, 95% CI 1.16- 1.56), AD (aHR 1.25, 95% CI 1.06-1.48), and ‘any mental health condition’ (aHR 1.28, 95% CI 1.12-1.46) (Figure 1). In subgroup analysis, male sex, and IBD patients aged 12 to 17 appear to have the greatest additional risk for developing mental health conditions (Figure 2) and Crohn’s disease had a higher risk than ulcerative colitis (aHR 1.32, 95% CI 1.10-1.58 and aHR 1.19, 95% CI 0.95-1.49 respectively). Conclusion Young IBD patients have a significantly higher incidence and risk of new mental health conditions. Mental health remains a critically overlooked aspect of IBD patient management. Further research into identifying optimal monitoring tools and support for these patients is required to improve patient care.

Defining clinical subtypes of adult asthma using electronic health records: Analysis of a large UK primary care database with external validation

IntroductionAsthma is one of the commonest chronic conditions in the world. Subtypes of asthma have been defined, typically from clinical datasets on small, well-characterised subpopulations of asthma patients. We sought to define asthma subtypes from large longitudinal primary care electronic health records (EHRs) using cluster analysis. MethodsIn this retrospective cohort study, we extracted asthma subpopulations from the Optimum Patient Care Research Database (OPCRD) to robustly train and test algorithms, and externally validated findings in the Secure Anonymised Information Linkage (SAIL) Databank. In both databases, we identified adults with an asthma diagnosis code recorded in the three years prior to an index date. Train and test datasets were selected from OPCRD using an index date of Jan 1, 2016. Two internal validation datasets were selected from OPCRD using index dates of Jan 1, 2017 and 2018. Three external validation datasets were selected from SAIL using index dates of Jan 1, 2016, 2017 and 2018. Each dataset comprised 50,000 randomly selected non-overlapping patients. Subtypes were defined by applying multiple correspondence analysis and k-means cluster analysis to the train dataset, and were validated in the internal and external validation datasets. ResultsWe defined six asthma subtypes with clear clinical interpretability: low inhaled corticosteroid (ICS) use and low healthcare utilisation (30% of patients); low-to-medium ICS use (36%); low-to-medium ICS use and comorbidities (12%); varied ICS use and comorbid chronic obstructive pulmonary disease (4%); high (10%) and very high ICS use (7%). The subtypes were replicated with high accuracy in internal (91–92%) and external (84–86%) datasets. ConclusionAsthma subtypes derived and validated in large independent EHR databases were primarily defined by level of ICS use, level of healthcare use, and presence of comorbidities. This has important clinical implications towards defining asthma subtypes, facilitating patient stratification, and developing more personalised monitoring and treatment strategies.