BACKGROUND:As poor sleep negatively impacts on adiposity, there is interest in examining whether improving sleep can improve adiposity markers. We reviewed studies aimed at improving sleep using (1) cognitive behavioral therapy for insomnia (CBT-I) and/or sleep hygiene and (2) sleep extension on adiposity markers, dietary intake, and physical activity. METHODS:Literature searches were performed on MEDLINE, Embase, CINAHL, and Cochrane Library. We included studies featuring adults ≥ 18 years without OSA, a sleep intervention as well as preintervention and postintervention measures. RESULTS:From a total of 27 studies, 25 were used in the meta-analyses. Using CBT-I and/or sleep hygiene in eight studies (n = 1384) led to significant BMI reductions averaging 0.64 kg/m2 (0.28, 1.01, p = 0.0006, I2 = 16%) but the reductions were not significant in five studies (n = 138) of sleep extension, 0.15 kg/m2 (-0.40, 0.11, p = 0.26). In obesity (n = 78, 3 studies) and in type 2 diabetes (n = 1121, 3 studies) BMI reductions averaged 1.53 (0.49, 2.56, p = 0.004, I2 = 0%) and 0.64 kg/m2 (0.37, 0.92, p < 0.00001, I2 = 0%), respectively. For body weight, using sleep interventions led to significant reductions in the obese (n = 72, 3 studies) and overweight ranges (n = 337, 6 studies), averaging 5.55 (4.13, 6.97, p < 0.00001, I2 = 0%) and 0.83 kg (0.32, 1.35, p = 0.002, I2 = 0%), respectively. Using sleep interventions reduced daily energy intake (n = 223, 5 studies) by 147.5 cal/day (5.74, 289.41, p = 0.04, I2 = 53%), but was higher in the obese/overweight range (n = 122, 3 studies) at 238.0 cal/day (138.86, 337.22, p < 0.00001, I2 = 0%); sugar consumption reduced (p = 0.05), and protein consumption increased (p = 0.001). There were no changes in physical activity or sedentary behavior levels. On quality assessment, 12 and 10 studies were of low and some concerns, respectively. CONCLUSION:Addressing poor sleep health through using sleep interventions has the potential to be integrated into obesity management programs, alongside current lifestyle strategies. PROSPERO IDENTIFICATION NUMBER:CRD42025644060.

We describe the harmonisation of five UK electronic birth cohorts to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, creating a large scale, standardised resource for maternal and child health research. The Mother and Infant Research Data Analysis (MIREDA) partnership developed and implemented reproducible guidelines for mapping maternal infant relationships and identifying pregnancy episodes within routinely collected healthcare data. Cohorts from England, Scotland, and Wales were transformed despite substantial heterogeneity in data structure, coding systems, and variable definitions. The resulting harmonised resource preserves each cohort as an independent dataset while enabling federated analyses to be conducted across sites without the need to share individual level data. Collectively, the cohorts capture over 17.5 million live births, providing sufficient scale to investigate rare exposures and outcomes, support trial emulation, and evaluate population level policy impacts across the UK. This article details the transformation pipeline and provides reusable methods to support extension to additional cohorts and networks. The harmonised datasets enable interoperable, reproducible research and facilitate cross national comparative studies in maternal and child health. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by the Medical Research Council (MRC) Partnership Grant [MR/X02055X/1]. Born in South London (eLIXIR) is funded by an MRC Longitudinal Population Cohort Grant MR/X009742/1. Additional funding was provided by Health Data Research UK and the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The datasets are anonymised individual level data. No data were de-anonymised for use in the project. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data for is securely held at each cohort's Trusted Research Environment (TRE) and access is granted via the each TRE's request and governance frameworks.

Abstract Background With an increasing trend in the prevalence of maternal autoimmune diseases in pregnancy, there is need for evidence on the association between autoimmune diseases and pregnancy outcomes. Methods This population-based cohort study used data on pregnancies from primary care practices that contributed to the UK Clinical Practice Research Datalink (CPRD) database (Gold and Aurum) between 2000 and 2022, linked to Hospital Episode Statistics (HES). Modified Poisson regression with robust standard errors estimated adjusted relative risks (aRR) and 95% confidence intervals (95% CI) assessing the association between 17 autoimmune diseases and 12 pregnancy outcomes, selected following literature review and expert consultation. Models were adjusted for demographic and comorbidity variables. Findings were evaluated using the Benjamini–Yekutieli procedure to control for multiple testing across autoimmune disease–outcome associations. Results A total of 5,239,383 pregnancies from the CPRD pregnancy register and 2,485,366 births recorded in HES maternity data met the eligibility criteria. Women with autoimmune diseases had an increased risk across all pregnancy outcomes examined. Among antenatal outcomes, markedly elevated risks were observed for hyperemesis gravidarum in Addison’s disease (aRR 3.72, 95% CI 2.48–5.59), miscarriage in Sjögren’s syndrome (1.66, 1.02–2.70), gestational hypertension in type 1 diabetes mellitus (T1DM; 2.95, 2.77–3.15), pre-eclampsia/eclampsia in T1DM (3.56, 3.32–3.82), and gestational diabetes mellitus in Graves’ disease (1.37, 1.21–1.54). For obstetric outcomes, increased risks were observed for caesarean birth in inflammatory bowel disease (IBD; 1.27, 1.22–1.31), small for gestational age in systemic lupus erythematosus (SLE; 2.45, 1.65–3.62), preterm birth in rheumatoid arthritis (1.53, 1.33–1.76), and stillbirth in SLE (1.82, 1.12–1.84). Perinatal mental health outcomes were more common across several autoimmune diseases, with particularly high risks in myasthenia gravis (anxiety 3.05, 1.89–4.92; depression 1.77, 1.37–2.28), SLE (anxiety 2.11, 1.67–2.67; depression 1.36, 1.22–1.53), and multiple sclerosis (anxiety 1.76, 1.38–2.23; depression 1.94, 1.77–2.12). Inverse associations were observed for hyperemesis gravidarum in systemic sclerosis, SLE, and myasthenia gravis, and for hypertensive disorders of pregnancy in multiple sclerosis. After Benjamini–Yekutieli correction for multiple testing, the number of statistically significant associations was reduced, with a core set of robust associations persisting across selected autoimmune diseases particularly, T1DM, SLE, Graves’ disease, IBD and key pregnancy outcomes. Conclusions Autoimmune diseases were associated with increased risks across a wide range of adverse pregnancy outcomes, with marked heterogeneity between individual conditions. This study provides adjusted relative risks across multiple domains of pregnancy outcomes including antenatal (e.g. miscarriage, hyperemesis gravidarum, gestational hypertension, pre-eclampsia, gestational diabetes), obstetric (e.g. preterm birth, caesarean birth, small for gestational age, stillbirth), and perinatal mental health outcomes (anxiety and depression), for both common and less frequently studied autoimmune diseases. The findings highlight the importance of disease-specific evaluation of pregnancy risks.

BackgroundMultimorbidity, living with 2 or more long-term health conditions, is increasing globally and now affects over one-quarter of adults in England. People with multiple long-term conditions (MLTC) face complex health and treatment challenges, often experiencing fragmented care within systems oriented toward single-disease management. Artificial intelligence (AI) has the potential to support clinicians and patients by analyzing complex health data, optimizing treatment strategies, and predicting disease trajectories. ObjectiveThe OPTIMAL (Optimizing Therapies, Disease Trajectories, and AI-Assisted Clinical Management for Patients Living with Complex Multimorbidity) project aims to develop AI-enabled tools to support shared decision-making in primary care. This study explored how patients with MLTC perceive the use of AI to inform joint decision-making in primary care. MethodsSemistructured interviews were conducted via telephone or video call with 29 adults living with MLTC between July and November 2023. Participants were recruited through general practitioner practices via the Clinical Practice Research Datalink and community-based organizations across the West Midlands. Interviews were transcribed verbatim and analyzed thematically using an inductive approach. Members of a patient advisory group were involved in developing study materials, refining the interview guide, and reviewing emerging findings to ensure relevance and authenticity. ResultsParticipants identified potential benefits of AI in enhancing consultation efficiency and accuracy, improving access to information for patients and clinicians, promoting early detection of health changes, and reducing health care inequalities. However, concerns were raised about the loss of human interaction, data privacy and security, transparency of algorithms, and the potential for bias and inequity in AI systems. Trust and acceptance varied by age and familiarity with technology. Some participants expressed uncertainty about what AI entails and how it could be used in primary care. ConclusionsPatients with MLTC viewed AI-assisted decision-making in primary care with cautious optimism. While many recognized potential benefits for coordination and personalization of care, others expressed reservations about privacy, fairness, and the risk of diminished human connection.

Background Early diagnosis and continuity of care is vital for atrial fibrillation (AF), to reduce stroke ; There is a lack of understanding of when and how AF is being diagnosed and managed the care pathway) in in low- and middle-income countries (LMICs). We aimed to identify the AF care pathway in Northern Province, Sri Lanka and determine how the COVID-19 pandemic impacted the care pathway. Methods This descriptive longitudinal study utilised two quantitative questionnaires to evaluate the AF pathway: The first questionnaire (baseline) was used to identify where AF was being diagnosed and the second questionnaire (3 months following baseline) was used to identify where and how often AF follow-up care was being received. How the COVID-19 pandemic impacted the care pathway was asked in the second questionnaire. We aimed to recruit 236 adults (≥18 years) with AF from Jaffna Teaching Hospital. Data were collected between October 2020 and June 2021 and analysed using descriptive statistics. Results 151 participants were recruited (median age 57 years; 70% female). Most participants were diagnosed in the accident & emergency (38%) or inpatient department (26%), followed by an outpatient department (19%) or private facility (16%). Nearly all (97%) participants received follow-up care during the study period, with an average of 1.3 AF-related healthcare visits per person for a month; most visited an outpatient department (88%). The COVID-19 pandemic negatively impacted 39% of participants’ care: healthcare visits were reduced or, delayed or medications were unattainable, and longer intervals between blood tests were experienced; however, 24% of participants were able to receive their medication by ambulance, public health staff or post during lockdowns. Conclusions Primary care was not involved in the diagnosis of AF, indicating that most diagnoses occurr after a medical emergency. The frequency of blood tests was lower than the guideline recommendations of one per month which could in-part be due to the adverse impacts of the pandemic. Strengthening primary and community-based care may enable early diagnosis and improve continuity of care during and beyond future healthcare crises.

OBJECTIVE:Half of individuals with type 2 diabetes (T2D) also experience dyslipidaemia. In a phase IV clinical trial, fenofibrate was shown to reduce the risk of referable diabetic retinopathy (DR) comparing to placebo in people with background diabetic retinopathy (BDR). We aimed to generate comprehensive real-world evidence on the effects of fenofibrate on the risk of sight-threatening diabetic retinopathy (STDR) in individuals with T2D, compared with statins. METHODS:This propensity score-matched, prevalent new-user comparative effectiveness trial emulation study included adults (≥18 years) with T2D, receiving either statins or fenofibrate, as recorded in Clinical Practice Research Datalink (a UK primary care database) from 1 January 2000 to 30 June 2022. The primary outcome was incident STDR. After propensity score matching, competing risk Cox proportional hazard regression models were used to estimate the crude and adjusted hazard ratios (aHR) in both intention-to-treat (ITT) and per-protocol (PP) analyses. RESULTS:Data from 16,337 fenofibrate new users and their matched 25,764 statin users were included in this study. Participants were followed up for up to 12 years. Fenofibrate use was associated with a reduced risk of developing STDR (aHR: ITT 0.87, 95% CI 0.82-0.93; PP 0.80, 0.74-0.87) in comparison with the statin group. The results were consistent across sensitivity analysis and subgroups based on age, sex, ethnicity, and lipid levels. However, the protective effect was not observed in South Asians. CONCLUSIONS:Fenofibrate use was associated with a reduced risk of STDR in individuals with T2D in the real-world clinical practice. These fundings suggest that fenofibrate could be considered as a DR preventive therapy for individuals with T2D.

Electronic Health Records (EHRs) provide rich opportunities for developing risk prediction tools to support clinical decision-making, yet they are inherently incomplete because data are recorded selectively during routine care. Such missingness may be informative, reflecting clinical judgment and patient status, and missing data patterns can shift between model development and real-world deployment. These challenges limit the reliability and transportability of predictive models in healthcare settings. We propose an imputation-free framework that jointly trains Conditional Variational Autoencoders with deep survival models to enable risk prediction directly from incomplete EHR data. We demonstrate the approach using the deep survival model DeSurv and evaluate its performance through simulation studies and two retrospective cohorts from the Clinical Practice Research Datalink primary care database. The proposed framework consistently outperforms conventional missing data methods, achieving superior performance on ground-truth metrics in simulations and improved calibration-based survival metrics in real-world cohorts. It also demonstrates increased robustness to unseen missingness patterns and distributional shifts. By providing a unified strategy for handling missing data across development, validation, and deployment, this work advances methodological robustness in healthcare informatics and supports more reliable clinical risk prediction in practice.

Background:The prevalence of multiple long-term conditions (MLTCs) is increasing globally, leading to complex health care needs and polypharmacy. Shared decision-making (SDM) is important for supporting patient-centered care, yet barriers such as limited consultation time, discontinuity of care, and communication challenges hinder implementation. Artificial intelligence (AI) has the potential to support SDM by providing personalized, data-driven recommendations, particularly for medication management in patients with MLTCs. Objective:This study aimed to explore the perspectives of patients with MLTCs regarding SDM with their general practitioners (GPs) and to explore patients' views about the use of an AI tool to support SDM, particularly in relation to prescribing decisions. Methods:This qualitative study explored the perspectives of 18 patients with MLTCs on SDM and the use of an AI tool prototype during GP consultations. Semistructured interviews used a simulated patient vignette and a visual AI tool dashboard to facilitate discussion. Participants were recruited through GP practices via the Clinical Practice Research Datalink and community-based organizations across the West Midlands. The data were then analyzed using thematic analysis. Results:Two overarching categories were identified: SDM in GP consultations and the AI tool for SDM. Within SDM, themes included communication and collaboration and system-level barriers, such as limited consultation time, lack of continuity, and fragmented records. Within the AI tool category, themes were related to practical design and implementation, implications for clinical practice and decision-making, and perceived risks and limitations. Participants valued the tool's potential to summarize health information and support discussions but highlighted the need for clear explanations, accessible design, and clinician guidance. Concerns included time pressures, depersonalization, trust, and transparency, with participants emphasizing that AI should support rather than replace clinical judgment. Conclusions:Overall, patients perceived AI as a promising way to enhance SDM by improving communication and collaboration between patient and clinician. However, patients also had concerns about the accuracy and veracity of AI. The study provides recommendations for AI tools in GP consultations, emphasizing clear, accessible outputs and the use of lay language. AI tools should enhance rather than replace clinical judgment, be transparent about data sources, and be developed with diverse patient input to ensure inclusivity and usability, particularly for those with MLTCs.

ObjectiveTo estimate the risk of miscarriage amongst pregnant women with migraine compared to pregnant women without migraine. To compare the odds of miscarriage in women taking medication for migraine to women with migraine who did not take medication and to explore this association with different types of medications.DesignMatched cohort study and nested case-control.SettingClinical Practice Research Datalink (CPRD) GOLD pregnancy register. All pregnancies meeting data quality requirements between 2000 and 2019 were eligible for inclusion.ParticipantsCohort study: 193,208 pregnancies of women with migraine were matched one-to-one to women without migraine. Nested case-control: 20,778 pregnancies of women with migraine that ended in miscarriage were matched to 40,122 pregnancies of women with migraine that did not end in miscarriage.Main outcome measuresCohort study: miscarriage recorded in primary care. Nested case-control: odds of miscarriage amongst migraineurs using migraine medication.ResultsMiscarriage occurred in 10% (n = 19,233) of women without migraine compared to 10.8% (n = 20,778) of women with migraine. Having migraine was associated with an 8% higher relative risk of miscarriage (risk ratio (RR) 1.08, 95% confidence interval (CI) 1.06-1.10, p < 0.001) and remained significant after adjustment for demographic factors, body mass index (BMI), smoking and comorbidities (aRR 1.06 95% CI [1.04-1.08][p = 0.001]).Of the pregnancies ending in miscarriage, 719 (3.46%), 380 (1.83%), 173 (0.83%) and 733 (3.52%) were exposed to triptans, amitriptyline, beta-blockers and non-steroidal anti-inflammatory drugs (NSAIDs), respectively. Of the matched pregnancies that did not end in miscarriage, 1099 (2.74%), 542 (1.35%), 294 (0.73%) and 780 (1.94%) were exposed to these medications, respectively.Exposure to triptans, amitriptyline and NSAIDs were associated with a significantly higher odds of miscarriage (aORs 1.24 [1.11-1.38][p < 0.001], 1.25 [1.08-1.45][p = 0.003] and 1.74 [1.57-1.93][p < 0.001] respectively). Beta-blockers were not associated with a higher risk of miscarriage.ConclusionsMigraine and triptan, amitriptyline and NSAID exposure were all associated with higher risk of miscarriage. Further work is needed to understand the potential causative mechanisms.

Abstract Background Among women in the UK, over 186,000 new cancer diagnoses and around 78,000 cancer deaths occurred annually from 2017 to 2019. Evidence suggests that pregnancy complications are linked to mortality and morbidity risks in later life. This umbrella review aims to assess the association between pregnancy complications and cancer risk. It forms part of a series of studies exploring associations between pregnancy complications and long-term health conditions. Methods MEDLINE, Embase, and Cochrane databases were searched from inception to April 2024. Key search terms encompassed ‘cancer’ and ‘pregnancy complications’ or ‘pregnancy risk factors’. Screening data extraction and quality appraisal (AMSTAR 2) were completed by two independent reviewers. Data were synthesised narratively and quantitatively. Relative risks (RR)/odds ratio (OR)/hazard ratios (HR) with 95% confidence intervals were reported. Results Of the 25 reviews assessed for methodological quality, 2 were rated high, 12 moderate, 9 low, and 2 critically low. After excluding 10 overlapping reviews and 2 critically low reviews, 13 reviews included reviews consisted of 170 primary studies. Associations between 7 pregnancy complications and 17 cancers are reported. Women with molar pregnancy had four-fold higher risk of developing gestational trophoblastic neoplasia [OR 4.72 (1.81–12.32)]. Miscarriage was associated with thyroid cancer [OR 1.29 (95% CI 1.15–1.44)], but not with breast or ovarian cancer. Pre-eclampsia was associated with a reduced risk of breast cancer [RR 0.89 (0.83–0.95)] and an almost twofold higher risk of ovarian cancer [RR 1.82 (1.16–2.85)]. Gestational diabetes mellitus was associated with a higher risk of thyroid cancer [RR 1.28 (1.16–1.42)], stomach cancer [RR 1.43 (1.02–2.00)], liver cancer [RR 1.27 (1.03–1.55)], and blood cancer [RR 1.48 (1.04–2.09)] but was not associated with other cancers studied. Preterm birth showed a very small association with breast cancer risk (OR 1.03, 95% CI 1.00–1.07). There was no significant association between caesarean section and cervical cancer or multiple births and breast cancer. Conclusions Some pregnancy complications were associated with selected cancer outcomes, although the evidence was heterogeneous and limited by potential bias, confounding, and inconsistent review quality.

Background:Physical inactivity and suboptimal diet in pregnancy are important modifiable risk factors for gestational diabetes, a major contributor to pregnancy complications. Objectives:We aimed to assess the effects of physical activity and/or diet-based lifestyle interventions during pregnancy on gestational diabetes and if these vary by maternal (body mass index, age, parity, ethnicity, education) and intervention characteristics using individual participant data meta-analysis of randomised trials, and a cost-effectiveness analysis. Data sources:International Weight Management in Pregnancy Collaborative Network database was updated by searching major databases from February 2017 to March 2022. Review methods:The main outcomes were gestational diabetes by any criteria and by the National Institute for Health and Care Excellence. Other outcomes were gestational diabetes as per International Association of Diabetes in Pregnancy Study Group and maternal and perinatal outcomes. We performed a two-stage random-effects individual participant data meta-analysis to obtain summary estimates (odds ratio) with 95% confidence intervals. Study quality of included trials was assessed, and heterogeneity summarised using τ2. Where possible, we added the aggregate data from non-individual participant data trials to the meta-analysis. We ranked interventions by effectiveness using network meta-analysis and undertook model-based economic evaluation to assess cost-effectiveness. The cost-effectiveness analysis took an NHS cost perspective compared an overall lifestyle intervention versus usual care with a time horizon covering the beginning of pregnancy until the discharge of the mother and infant from the hospital following delivery. Results:Ninety-two trials (32,284 women) were included; 54 (23,698 women) provided individual participant data. Lifestyle interventions reduced the odds of gestational diabetes (any criteria) by 10% in individual participant data trials (odds ratio 0.90, 95% confidence interval 0.80 to 1.02, 54 studies, 23,361 women), and the findings reached statistical significance when non-individual participant data were included (odds ratio 0.81, 95% confidence interval 0.73 to 0.89, 92 studies, 31,947 women). Physical activity significantly reduced the odds of gestational diabetes by 36% (odds ratio 0.64; 95% confidence interval 0.48 to 0.84), and diet by 19% (odds ratio 0.81; 0.69 to 0.96), but not mixed interventions. Women with middle (odds ratio 0.68, 95% confidence interval 0.51 to 0.90) and high educational level (odds ratio 0.71, 95% confidence interval 0.54 to 0.93) benefited more than those with low educational status, and no differences by maternal body mass index, age, parity or ethnicity. There was no significant reduction in gestational diabetes defined by National Institute for Health and Care Excellence criteria (odds ratio 0.98, 95% confidence interval 0.84 to 1.13) in individual participant data trials. For gestational diabetes defined using International Association of Diabetes in Pregnancy Study Group criteria, interventions reduced gestational diabetes by 14% (odds ratio 0.86, 95% confidence interval 0.75 to 0.97, τ2 = 0.00, 16 studies, 6174 women) in individual participant data trials and by 17% (odds ratio 0.83, 95% confidence interval 0.72 to 0.95, τ2 = 0.01, 25 studies, 7883 women) when non-individual participant data trials were added. Overall, physical activity reduced caesarean section (odds ratio 0.83; 0.72 to 0.96), small-for-gestational age (odds ratio 0.72; 0.56 to 0.92) and large-for-gestational age babies (odds ratio 0.81; 0.71 to 0.94); diet-based interventions reduced any preterm birth (odds ratio 0.37; 0.20 to 0.68) compared to controls. No differences were observed for other outcomes. Lifestyle interventions were on average more expensive and more effective at averted gestational diabetes and major outcome averted compared to usual care. Limitations:We could not identify the specific intervention components and delivery methods associated with improved outcomes, due to variations in reporting. Conclusion:Lifestyle interventions in pregnancy prevent gestational diabetes, and the effects vary according to the definition of gestational diabetes. Physical activity-based interventions may be the most effective. Future work:Lifestyle interventions should be implemented and evaluated in routine clinical practice to prevent gestational diabetes, with additional support for women with low socioeconomic status. Study registration:This study is registered as PROSPERO CRD42020212884. www.crd.york.ac.uk/PROSPERO/view/CRD42020212884. Funding:This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR129715) and is published in full in Health Technology Assessment; Vol. 30, No. 39. See the NIHR Funding and Awards website for further award information.

Existing literature suggests a link between migraine and various adverse pregnancy outcomes, but findings remain inconsistent. This study aimed to evaluate the association between pre-pregnancy migraine and obstetric outcomes. This retrospective cohort study used linked electronic health records from Clinical Practice Research Datalink (CPRD) GOLD and Hospital Episode Statistics (HES) maternity data. We included women aged 15–50 years in England with singleton deliveries between 2000 and 2019. Deliveries from women with a recorded migraine diagnosis before pregnancy were matched 1:1 by maternal age to those without migraine. Logistic regression models, adjusted for demographic and clinical factors, were used to examine associations with preterm birth, low birth weight, small for gestational age (SGA), mode of delivery, and stillbirth. The cohort included 428,217 deliveries from 317,016 women, comprising 46,560 (10.9

BACKGROUND:Type 2 diabetes mellitus (T2DM) is often the first condition on the pathway to patients developing multiple long-term conditions (MLTCs). The impact of sodium-glucose cotransporter 2 inhibitors (SGLT-2i), a key T2DM treatment, on hospitalization and MLTC progression remains uncertain. METHODS:In this comparative effectiveness study, we emulated a target trial using linked primary care, hospital, and mortality records in England (2012-2023). Adults aged ≥40 years with T2DM initiating SGLT-2i or dipeptidyl peptidase-4 inhibitors (DPP-4i) were included (n = 364 522). Outcomes included all-cause mortality, first hospitalization, number of hospitalizations, and incidence of 59 prespecified long-term conditions across 17 organ systems. Weighted Cox proportional hazards and negative binomial regression models were used to estimate adjusted hazard ratios (aHRs) or adjusted mean differences (aMDs), with 95% CIs and Benjamini-Yekutieli correction for multiple testing. FINDINGS:Compared with DPP-4i, SGLT-2i use was associated with lower risks of all-cause mortality (aHR 0.73, 95% CI 0.71-0.75), first hospitalization (aHR 0.86, 95% CI 0.85-0.87), and number of hospitalizations (aMD -0.36, 95% CI -0.38 to -0.34; all P < .001). SGLT-2i initiators had reduced risks for 28 conditions, including dementia, cancer, metabolic dysfunction-associated steatotic liver disease, diabetic foot ulcer, epilepsy, and rheumatoid arthritis. Increased risks were observed for four conditions, including candidiasis and diabetic ketoacidosis. INTERPRETATION:SGLT-2i use in T2DM was associated with significant reductions in mortality, hospitalization, and MLTC burden. The broad protective effects across multiple organ systems highlight the potential of SGLT-2i to provide a holistic therapeutic strategy in the management of diabetes and its multisystem complications.

Multiple long-term conditions (MLTCs), or multimorbidity-the co-occurrence of multiple chronic conditions-present a growing challenge for primary care. Existing predictive models typically focus on single outcomes and often fail to capture the temporal and competing-risk structure inherent in longitudinal electronic health records (EHRs). Here, we present SurvivEHR, a generative transformer-based foundation model trained on over 7.6 billion coded events from 23 million patients in UK primary care. SurvivEHR is pre-trained using a competing-risk, time-to-next-event objective, enabling calibrated risk stratification across a broad range of diagnoses, investigations, medications, and mortality events. We show that this pre-training objective yields strong next-event discrimination and learns clinically meaningful patient trajectories. When adapted through fine-tuning, SurvivEHR achieves improved performance on downstream prognostic tasks, including longer-horizon risk prediction, with particular benefits in low-resource settings. By learning longitudinal patient representations directly from routine primary care records, SurvivEHR provides a scalable foundation for developing generalisable clinical risk models that reflect the complexity of MLTCs in primary care.

OBJECTIVES:To assess the effects of lifestyle interventions on gestational diabetes, determine whether the effects vary by maternal body mass index, age, parity, ethnicity, education level, or intervention, and rank interventions by effectiveness. DESIGN:Individual participant data (IPD) and network meta-analysis. DATA SOURCES:Major electronic databases (January 1990 to April 2025). METHODS:This meta-analysis included randomised trials on the effects of lifestyle interventions (physical activity based, diet based, or mixed) in pregnancy on gestational diabetes. Main outcomes were gestational diabetes defined by any criteria and by UK NICE (National Institute for Health and Care Excellence) criteria; other outcomes included IADPSG (International Association of Diabetes in Pregnancy Study Group) and modified IADPSG defined gestational diabetes. A two stage IPD meta-analysis estimated summary odds ratios and 95% confidence intervals and interactions (subgroup effects), along with absolute risk reduction estimates. Aggregate data from non-IPD trials were added to the meta-analysis when possible. Intervention effects were ranked using network meta-analysis. RESULTS:104 randomised trials (35 993 women) were included, with IPD for 68% of participants (24 391 women; 54 studies). Lifestyle interventions reduced gestational diabetes defined by any criteria by 10% in IPD trials (odds ratio 0.90, 95% confidence interval (CI) 0.80 to 1.02; absolute risk reduction 1.3%, 95% CI -0.3% to 2.6%), and by 20% when combining IPD and non-IPD trials (odds ratio 0.80, 95% CI 0.73 to 0.88; absolute risk reduction 2.6%, 95% CI 1.6% to 3.6%), and no reduction was observed using NICE criteria (odds ratio 0.98, 95% CI 0.84to 1.13). Lifestyle interventions reduced gestational diabetes defined using IADPSG criteria by 14% in IPD trials (odds ratio 0.86, 95% CI 0.75 to 0.97; absolute risk reduction 2.7%, 95% CI 0.6% to 5.0%) and by 18% when combining IPD and non-IPD trials (odds ratio 0.82, 95% CI 0.72 to 0.93; absolute risk reduction 3.5%, 95% CI 1.3% to 5.7%). Effects did not vary by maternal characteristics, except for education. Although women of all educational levels benefited from the intervention, the benefit was less in those with low education (low v middle interaction: odds ratio 0.68, 95% CI 0.51 to 0.90; low v high interaction: odds ratio 0.71, 95% CI 0.54 to 0.93). Benefits did not vary by intervention characteristics, except for greater effectiveness with group format (odds ratio 0.81, 95% CI 0.68 to 0.97; absolute risk reduction 2.5%, 95% CI 0.4% to 4.3%) and newly trained facilitators (odds ratio 0.82, 95% CI 0.69 to 0.96; absolute risk reduction 2.4%, 95% CI 0.5% to 4.2%). Physical activity based interventions ranked highest (mean rank 1.1, 95% CI 1 to 2) in preventing gestational diabetes. CONCLUSIONS:Lifestyle interventions in pregnancy are likely to prevent gestational diabetes, with effects varying according to diagnostic criteria. Implementation strategies should address inequalities by maternal education, and consider group formats, provider training, and physical activity based interventions to prevent gestational diabetes. STUDY REGISTRATION:PROSPERO CRD42020212884.

BACKGROUND:Around 20% of pregnant women in the UK have multiple long-term conditions (MLTCs), defined as two or more physical or mental health conditions. The current evidence related to adverse maternal outcomes at a population level is scarce. The aim of this study was to compare antenatal outcomes between women with and without MLTCs across the four UK nations. METHODS:This retrospective cohort study included pregnant women aged 15-49 years from England, Northern Ireland, Scotland, and Wales. Data sources included Clinical Practice Research Datalink Gold (CPRD; 2000-22), Born in Bradford (2006-10), Secure Anonymised Information Linkage (2000-19), Scottish routine health records (2010-19), and the Northern Ireland Maternity System (2011-21). MLTCs were defined using a list of 79 health conditions developed with input from women and clinicians, with the number of conditions recorded including the mental health conditions. Antenatal outcomes included miscarriage, venous thromboembolism, gestational hypertension, pre-eclampsia, gestational diabetes, obstetric cholestasis, vomiting and hyperemesis, and antenatal mental health outcomes (anxiety and depression). Modified Poisson regression was used to estimate the association between MLTCs and antenatal outcomes separately for each dataset, adjusting for maternal age, gravidity, ethnicity, deprivation, BMI, smoking, and year of pregnancy. Pooled estimates across datasets were derived through meta-analysis when outcomes were available. Miscarriage and termination of pregnancy were reported using CPRD only, whereas antenatal anxiety and depression were pooled across datasets when ascertainment was available. FINDINGS:A total of 2 225 701 pregnancies or birth events were analysed. Compared with women without MLTCs, those with MLTCs had increased risks of antenatal complications, including hyperemesis (adjusted risk ratio 1·69 [95% CI 1·58-1·81]), vomiting (1·61 [1·46-1·78]), miscarriage (1·20 [1·18-1·22]), gestational diabetes (1·26 [1·18-1·34]), gestational hypertension (1·18 [1·13-1·22]), obstetric cholestasis (1·28 [1·11-1·47]), pre-eclampsia (1·42 [1·25-1·61]), placental abruption (1·32 [1·22-1·44]), venous thromboembolism (2·19 [1·73-2·78]), antenatal anxiety (4·25 [2·59-6·99]), and depression (4·09 [3·27-5·11]). No significant associations were found for termination of pregnancy or chorioamnionitis. The risk of complications increased with the number of coexisting conditions. INTERPRETATION:Maternal MLTCs were associated with increased risk of antenatal complications. These findings highlight the urgent need for integrated care pathways and targeted risk assessment for pregnant women with MLTCs. FUNDING:Medical Research Council, National Institute for Health and Care Research, Economic and Social Research Council, Engineering and Physical Sciences Research Council.

OBJECTIVES:To assess whether prodromal symptoms of RA, as recorded in the Clinical Practice Research Datalink Aurum (CPRD) database of English primary care records, differ by ethnicity and socioeconomic status. METHODS:A cross-sectional study to determine the coding of common symptoms (≥0.1% in the sample) in the 24 months preceding RA diagnosis in CPRD Aurum, recorded between 1 January 2004 and 1 May 2022. Eligible cases were adults with a code for RA diagnosis. For each symptom, a logistic regression was performed with the symptom as dependent variable, and ethnicity and socioeconomic status as independent variables. Results were adjusted for sex, age, BMI and smoking status. White ethnicity and the highest socioeconomic quintile were comparators. RESULTS:In total, 70 115 cases were eligible for inclusion, of which 66.4% were female. Twenty-one symptoms were coded in >0.1% of cases so were included in the analysis. Patients of South Asian ethnicity had higher frequency of codes for several symptoms, with the largest difference by odds ratio being muscle cramps (1.71, 99.76 % confidence interval 1.44-2.57) and shoulder pain (1.44, 1.25-1.66). Patients of Black ethnicity had higher prevalence of several codes including unintended weight loss (2.02, 1.25-3.28) and ankle pain (1.51, 1.02-2.23). Low socioeconomic status was associated with morning stiffness (1.74, 1.08-2.80) and falls (1.37, 2.03-1.82). CONCLUSION:There are significant differences in coded symptoms between demographic groups, which must be considered in clinical practice in diverse populations and to avoid algorithmic bias in prediction tools derived from routinely collected healthcare data.

INTRODUCTION:Patients with inflammatory bowel disease (IBD) may experience delays in their diagnosis. This study aimed to develop and validate a risk prediction tool for IBD. METHODS:A retrospective cohort study was conducted using primary care data from 2010 to 2019, including symptomatic patients aged ≥18. UK-based primary care databases linked to hospital records were utilized for model development and validation. Cox proportional hazards models were used to derive risk equations for IBD, ulcerative colitis (UC), and Crohn's disease (CD) in men and women. Candidate predictors included demographics, comorbidities, symptoms, extraintestinal manifestations, and laboratory results. Model performance was evaluated using measures of fit, discrimination, and calibration at 1, 2, 3, and 5 years after symptom onset. RESULTS:In total, 2 054 530 patients were included in the derivation cohort and 673 320 in the validation cohort. In the derivation cohort, 0.7% were diagnosed with IBD (66.3% UC and 33.7% CD). Predictors in the final IBD model included age, smoking, body mass index, gastrointestinal symptoms, extraintestinal manifestations, comorbidities, family history of IBD, and laboratory investigations. The model demonstrated good discrimination and calibration; C-statistic 0.78 (95% confidence interval [CI], 0.77-0.79) in men and 0.78 (95% CI, 0.77-0.79) in women. In the validation cohort, the model tended to slightly overestimate IBD risk at higher risk thresholds. CONCLUSIONS:A risk model using patient demographics, symptoms, and laboratory results accurately predicted IBD, UC, and CD at 1, 2, 3, and 5 years after symptom onset, potentially aiding in prioritizing patients for a referral or fecal calprotectin testing in primary care.

Managing multiple long-term conditions (MLTC) is a complex healthcare domain. It presents both challenges and opportunities for artificial intelligence (AI) tools. Understanding healthcare practitioners’ (HCPs) experiences of MLTC management and the factors influencing their attitudes towards using AI in complex clinical decision-making is crucial for successful implementation. We aimed to explore the perspectives of primary care HCPs on managing MLTC and their attitudes to using AI tools to support clinical decision-making in MLTC. Twenty HCPs including general practitioners, geriatricians, nurses and pharmacists, were interviewed. A patient case study was used to explore how an AI tool might alter the way participants approach clinical decision-making with a patient with MLTC. We derived concepts inductively from the interview transcripts and structured them according to the five categories of Buck’s model exploring determinants of attitudes to AI. These included the concerns and expectations that contributed to the minimum requirements for HCPs to consider using an AI decision-making tool, as well as the individual characteristics and environmental influences determining their attitudes. HCPs perspectives on managing MLTC were grouped into three main themes: (1) balancing multiple competing factors including accounting for patients’ social circumstances, (2) managing polypharmacy, and (3) working beyond single condition guidelines. HCPs typically expected that AI tools would improve the safety and quality of clinical decision-making. However, they expressed concerns about the impact on the therapeutic clinician-patient relationship that is fundamental to the care of patients with MLTC. The key prerequisites for clinicians adopting AI-tools in this context included improving public and patient trust in AI, saving time and integrating with existing systems, and ensuring that the rationale behind a recommendation is apparent, to enable a final decision made by an experienced human clinician. This is the first study to examine the attitudes of HCPs to using AI-decision making tools in the context of managing MLTCs. HCPs were positive about the potential for AI tools to improve the safety and quality of care but unequivocal that the human touch is irreplaceable when managing patients with complex medical and social circumstances. RR2-10.1136/bmjopen-2023-077156

Rigorous study design and analytical standards are required to generate reliable findings in healthcare from artificial intelligence (AI) research. One crucial but often overlooked aspect is the determination of appropriate sample sizes for studies developing AI-based prediction models for individual diagnosis or prognosis. Specifically, the number of participants and outcome events required in datasets for model training and evaluation remains inadequately addressed. Most AI studies do not provide a rationale for their chosen sample sizes and frequently rely on datasets that are inadequate for training or evaluating a clinical prediction model. Among the ten principles of Good Machine Learning Practice established by the US Food and Drug Administration, the UK Medicines and Healthcare products Regulatory Agency, and Health Canada, guidance on sample size is directly relevant to at least three principles. To reinforce this recommendation, we outline seven reasons why inadequate sample size negatively affects model training, evaluation, and performance. Using a range of examples, we illustrate these issues and discuss the potentially harmful consequences for patient care and clinical adoption. Additionally, we address challenges associated with increasing sample sizes in AI research and highlight existing approaches and software for calculating the minimum sample sizes required for model training and evaluation.