Objective:We examined the independent risk of incident comorbidities associated with prescribed oral paracetamol, NSAIDs and opioids and their interaction and mediation with OA. Methods:Exposure of these analgesics and the association with nine systemic groups of incident comorbidities were examined in this 5-year cohort study of 261 273 people with incident OA and 261 273 age-, sex- and practice-matched controls (no OA) in the large UK primary care database. A propensity score-adjusted time-varying exposure analysis was undertaken using multivariable Cox models to estimate the hazard ratios (HRs) for incident comorbidities associated with OA and analgesics. Interaction was defined when the combined term for OA and analgesic was significant. The proportion of the risk associated with OA mediated by analgesics was also calculated. Results:The mean age of the cohort was 60 years and 57.7% were female. NSAIDs and opioids were independently associated with all comorbidities. Paracetamol interacted with OA leading to an extra risk of renal comorbidities [multiplicative HR (mHR) 1.14 (95% CI 1.06, 1.22)]. NSAIDs and opioids interacted with OA with extra risk of cardiovascular [mHR 1.05 (95% CI 1.01, 1.11) and mHR 1.06 (95% CI 1.02, 1.11)] and endocrine [mHR 1.11 (95% CI 1.05, 1.17) and mHR 1.08 (95% CI 1.02, 1.13)] comorbidities. About 16% and 7% risk from OA to psychological comorbidities were indirectly mediated by NSAIDs and opioids, respectively. Conclusion:Our findings suggest that in people with OA, analgesic prescriptions may be associated with increased risk of certain incident comorbidities, particularly in long-term use.

OBJECTIVES:Frailty is a dynamic health state that changes over time. Our hypothesis was that there are identifiable subgroups of the older population that have specific patterns of deterioration. The objective of this study was to evaluate the application of joint latent class model in identifying trajectories of frailty progression over time and their group-specific risk of death in older people. STUDY DESIGN AND SETTING:The primary care records of UK patients, aged over 65 as of January 1, 2010, included in the Clinical Practice Research Datalink: GOLD and AURUM databases, were analyzed and linked to mortality data. The electronic frailty index (eFI) scores were calculated at baseline and annually in subsequent years (2010-2013). Joint latent class model was used to divide the population into clusters with different trajectories and associated mortality hazard ratios. The model was built in GOLD and validated in AURUM. RESULTS:Five trajectory clusters were identified and characterized based on baseline and speed of progression: low-slow, low-moderate, low-rapid, high-slow, and high-rapid. The high-rapid cluster had the highest average starting eFI score; 7.9, while the low-rapid cluster had the steepest rate of eFI progression; 1.7. Taking the low-slow cluster as reference, low-rapid and high-rapid had the highest hazard ratios: 3.73 (95% CI 3.71, 3.76) and 3.63 (3.57-3.69), respectively. Good validation was found in the AURUM population. CONCLUSION:Our research found that there are vulnerable subgroups of the older population who are currently frail or have rapid frailty progression. Such groups may be targeted for greater healthcare monitoring.

Background: Evidence on health and social care resource utilisation and associated costs, and how this varies across people with Alzheimer's disease (AD) dementia is limited. Methods: Retrospective cohort, using the Discover dataset, which includes linked health and social care records for approximately 2.5 million people in North-West London. Individuals were followed up from the latter of 2010 or from diagnosis of AD dementia (index) up to 2019. Outcomes were overall survival, care home admission, health and social care utilisation and associated costs, and cardiovascular outcomes. Variation was explored in subpopulations and by stratifying by cost quintile. Generalised linear modelling was used to estimate the association between clinical and demographic characteristics and healthcare costs. Results: The cohort included 18,116 people diagnosed with AD dementia whose mean age at index was 81 years, 62 % were female and 65 % were of white ethnicity. Median survival from index was 4.9 years (95 %CI: 4.8-5.0). Mean healthcare costs were 4,548( pound 4,491- pound 4,604) pound per person year(ppy). Healthcare costs for the 48 % who used social care were 5,433ppy( pound 5,353- pound 5,514) pound and social care costs were 24,374ppy( pound 24,372- pound 24,376) pound. In the overall cohort costs in the highest cost quintile were 13,665ppy( pound 13,420- pound 13,911) pound, of which 70 % was from inpatient hospitalisation. Subpopulations admitted to care home ( 7,535 pound 7,362- pound 7,709) pound, with cardiovascular disease ( 6,106; pound 5,990- pound 6,222) pound and with type 2 diabetes ( 6,049; pound 5,901- pound 6,198) pound accrued the highest healthcare costs. Factors most strongly associated with cost were dying during follow up ( + 2,224; pound 2,010- pound 2,493) pound, being frail ( + 1,246; pound 1,051- pound 1,440) pound and prior stroke ( + 1,207; pound 908- pound 1,507) pound. Conclusion: Characteristics of individuals with high healthcare costs include requirement for social care and cardiometabolic comorbidities. Identifying individuals early in their disease course may improve health outcomes and reduce the cost of AD dementia in later life.

•High-cost AD dementia subpopulations cost 1.32 to 1.66 times the average (£4,548).•For the 48% who used social care, healthcare costs were £5,433per person year (ppy) and social care costs £24,374ppy.•Dying during follow up, frailty and having had a stroke were associated with high costs.•Characteristics of individuals with high healthcare costs include requirement for social care and cardiometabolic comorbidities.

Objectives To explore clustering of comorbidities among patients with a new diagnosis of OA and estimate the 10-year mortality risk for each identified cluster. Methods This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand or 'unspecified' site between 2006 and 2020, using SIDIAP (a primary care database representative of Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in >= 1% of the individuals (n = 35) were fitted into two cluster algorithms, k-means and latent class analysis. Models were assessed using a range of internal and external evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards. Results We identified 633 330 patients with a diagnosis of OA. Our proposed best solution used latent class analysis to identify four clusters: 'low-morbidity' (relatively low number of comorbidities), 'back/neck pain plus mental health', 'metabolic syndrome' and 'multimorbidity' (higher prevalence of all studied comorbidities). Compared with the 'low-morbidity' cluster, the 'multimorbidity' cluster had the highest risk of 10-year mortality (adjusted hazard ratio [HR]: 2.19 [95% CI: 2.15, 2.23]), followed by the 'metabolic syndrome' cluster (adjusted HR: 1.24 [95% CI: 1.22, 1.27]) and the 'back/neck pain plus mental health' cluster (adjusted HR: 1.12 [95% CI: 1.09, 1.15]). Conclusion Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results.

We studied the characteristics of patients prescribed osteoporosis medication and patterns of use in European databases. Patients were mostly female, older, had hypertension. There was suboptimal persistence particularly for oral medications. Our findings would be useful to healthcare providers to focus their resources on improving persistence to specific osteoporosis treatments. To characterise the patients prescribed osteoporosis therapy and describe the drug utilization patterns. We investigated the treatment patterns of bisphosphonates, denosumab, teriparatide, and selective estrogen receptor modulators (SERMs) in seven European databases in the United Kingdom, Italy, the Netherlands, Denmark, Spain, and Germany. In this cohort study, we included adults aged ≥ 18 years, with ≥ 1 year of registration in the respective databases, who were new users of the osteoporosis medications. The study period was between 01 January 2018 to 31 January 2022. Overall, patients were most commonly initiated on alendronate. Persistence decreased over time across all medications and databases, ranging from 52–73% at 6 months to 29–53% at 12 months for alendronate. For other oral bisphosphonates, the proportion of persistent users was 50–66% at 6 months and decreased to 30–44% at 12 months. For SERMs, the proportion of persistent users at 6 months was 40–73% and decreased to 25–59% at 12 months. For parenteral treatment groups, the proportions of persistence with denosumab were 50–85% (6 month), 30–63% (12 month) and with teriparatide 40–75% (6 month) decreasing to 21–54% (12 month). Switching occurred most frequently in the alendronate group (2.8–5.8%) and in the teriparatide group (7.1–14%). Switching typically occurred in the first 6 months and decreased over time. Patients in the alendronate group most often switched to other oral or intravenous bisphosphonates and denosumab. Our results show suboptimal persistence to medications that varied across different databases and treatment switching was relatively rare.

Abstract Background While several definitions exist for multimorbidity, frailty or polypharmacy, it is yet unclear to what extent single healthcare markers capture the complexity of health-related needs in older people in the community. We aimed to identify and characterise older people with complex health needs based on healthcare resource use (unplanned hospitalisations or polypharmacy) or frailty using large population-based linked records. Methods In this cohort study, data was extracted from UK primary care records (CPRD GOLD), with linked Hospital Episode Statistics inpatient data. People aged > 65 on 1st January 2010, registered in CPRD for ≥ 1 year were included. We identified complex health needs as the top quintile of unplanned hospitalisations, number of prescribed medicines, and electronic frailty index. We characterised all three cohorts, and quantified point-prevalence and incidence rates of preventive medicines use. Results Overall, 90,597, 110,225 and 116,076 individuals were included in the hospitalisation, frailty, and polypharmacy cohorts respectively; 28,259 (5.9%) were in all three cohorts, while 277,332 (58.3%) were not in any (background population). Frailty and polypharmacy cohorts had the highest bi-directional overlap. Most comorbidities such as diabetes and chronic kidney disease were more common in the frailty and polypharmacy cohorts compared to the hospitalisation cohort. Generally, prevalence of preventive medicines use was highest in the polypharmacy cohort compared to the other two cohorts: For instance, one-year point-prevalence of statins was 64.2% in the polypharmacy cohort vs. 60.5% in the frailty cohort. Conclusions Three distinct groups of older people with complex health needs were identified. Compared to the hospitalisation cohort, frailty and polypharmacy cohorts had more comorbidities and higher preventive therapies use. Research is needed into the benefit-risk of different definitions of complex health needs and use of preventive therapies in the older population.

Abstract Background We assessed the risk of adverse events—severe acute kidney injury (AKI), falls and fractures—associated with use of antihypertensives in older patients with complex health needs (CHN). Setting UK primary care linked to inpatient and mortality records. Methods The source population comprised patients aged >65, with ≥1 year of registration and unexposed to antihypertensives in the year before study start. We identified three cohorts of patients with CHN, namely, unplanned hospitalisations, frailty (electronic frailty index deficit count ≥3) and polypharmacy (prescription of ≥10 medicines). Patients in any of these cohorts were included in the CHN cohort. We conducted self-controlled case series for each cohort and outcome (AKI, falls, fractures). Incidence rate ratios (IRRs) were estimated by dividing event rates (i) during overall antihypertensive exposed patient-time over unexposed patient-time; and (ii) in the first 30 days after treatment initiation over unexposed patient-time. Results Among 42,483 patients in the CHN cohort, 7,240, 5,164 and 450 individuals had falls, fractures or AKI, respectively. We observed an increased risk for AKI associated with exposure to antihypertensives across all cohorts (CHN: IRR 2.36 [95% CI: 1.68–3.31]). In the 30 days post-antihypertensive treatment initiation, a 35–50% increased risk for falls was found across all cohorts and increased fracture risk in the frailty cohort (IRR 1.38 [1.03–1.84]). No increased risk for falls/fractures was associated with continuation of antihypertensive treatment or overall use. Conclusion Treatment with antihypertensives in older patients was associated with increased risk of AKI and transiently elevated risk of falls in the 30 days after starting antihypertensive therapy.

BackgroundOsteoarthritis (OA) patients are more likely to have other comorbidities (Swain, Sarmanova et al. 2020). Improving the understanding of comorbidity profiles of OA patients may lead to improvement in their clinical care.ObjectivesTo identify sub-groups in patients diagnosed with hip OA using patterns of comorbidity.MethodsRoutinely-collected data of individuals ≥18 years with an incident diagnosis of hip OA (baseline/time of diagnosis), with at least 1 year of follow-up in SIDIAP (Information System for Research in Primary Care, a primary case database from Spain) were collected from January 1st 2006 to June 31st 2020. Those with soft-tissue disorders or other bone/cartilage diseases at the same joint in the year prior/after baseline were excluded. Comorbidities associated with OA in the literature and present in ≥1% of the study population were included. Clusters of comorbidities were identified at baseline using latent class analysis (LCA), a soft clustering method that classifies individuals according to the distribution of their measured items. The number of clusters or sub-groups within the study population was decided by comparing goodness of fit parameters (CAIC, BIC, ABIC) and log-likelihood changes of models from 2 to 8 clusters. The selected model was externally evaluated by a survival analysis assessing 10 years mortality within each cluster, where the weight of the posterior probability was used as a probability of sampling weight.ResultsWe identified 94,720 individuals with an incident diagnosis of hip OA, 56.3% women and 43.7% men, with a mean age (SD) of 67.2 (13.1) years. We selected the LCA model with 5 clusters that could be described as: healthier (lower prevalence of all comorbidities than average in the cohort), multimorbidity (higher prevalence of all comorbidities, multiple comorbidities), back/neck pain plus mental health (B/N-mental), cardiovascular disease (CVD), and metabolic syndrome (MetS) (Figure 1). Cox regression (HR [95CI%]) showed higher mortality risk for multimorbidity (3.76 [3.70-3.83]), CVD (1.56 [1.53-1.59]) and MetS (4.56 [4.35-4.78]), compared to healthy. No difference was observed for B/N-mental cluster.Figure 1.Distribution of comorbidities within each cluster using latent class analysis. Clusters were described as Healthier, Multimorbidity, B/N-mental, CVD and MetS. Black horizontal lines represent the prevalence of the comorbidity before the clusterization. Abbreviations: Healthier, lower prevalence of all comorbidities; Multimorbidity, higher prevalence of all comorbidities; B/N-mental, back/neck pain plus mental health disorders; CVD, cardiovascular disease; Met, metabolic syndrome; Bhp, benign prostate hypertrophy; Chd, chronic heart disease; Chf, chronic heart failure; Ckd, chronic kidney disease; Copd, chronic obstructive pulmonary disease; Gbs, gall bladder stone; Gerd, gastroesophageal reflux disease; Ibd, inflammatory bowel disease; Ovd, other vessel diseases; Substance, substance abuse.ConclusionClustering of co-morbidities in hip OA patients at the time of diagnosis has the potential to detect sub-groups of hip OA patients who might require additional care.References[1]Swain, S., A. Sarmanova, C. Coupland, M. Doherty and W. Zhang (2020). “Comorbidities in Osteoarthritis: A Systematic Review and Meta-Analysis of Observational Studies.” Arthritis Care Res (Hoboken) 72(7): 991-1000.AcknowledgementsWe thank the Patient Research Participants (PRP) members Jenny Cockshull, Stevie Vanhegan, and Irene Pitsillidou for their involvement since the beginning of the project. We would like to thank the FOREUM for financially supporting the research.Disclosure of InterestsNone declared

ABSTRACT Although oral bisphosphonates (BP) are commonly used, there is conflicting evidence for their safety in the elderly. Safety concerns might trump BP use in older patients with complex health needs. Our study evaluated the safety of BP, focusing on severe acute kidney injury (AKI), gastrointestinal ulcer (GI ulcer), osteonecrosis of the jaw (ONJ), and femur fractures. We used UK primary care data (Clinical Practice Research Datalink [CPRD GOLD]), linked to hospital (Hospital Episode Statistics [HES] inpatient) and ONS mortality data. We included all patients aged >65 with complex health needs and no BP use in the year before study start (January 1, 2010). Complex health needs were defined in three cohorts: an electronic frailty index score ≥3 (frailty cohort), one or more unplanned hospitalization/s (hospitalization cohort); and prescription of ≥10 different medicines in 2009 (polypharmacy cohort). Incidence rates were calculated for all outcomes. Subsequently, all individuals who experienced AKI or GI ulcer anytime during follow-up were included for Self-Controlled Case Series (SCCS) analyses. Incidence rate ratios (IRRs) were estimated separately for AKI and GI ulcer, comparing event rates between BP-exposed and unexposed time windows. No SCCS were conducted for ONJ and femur fractures. We identified 94,364 individuals in the frailty cohort, as well as 78,184 and 95,621 persons in the hospitalization and polypharmacy cohorts. Of those, 3023, 1950, and 2992 individuals experienced AKI and 1403, 1019, and 1453 had GI ulcer/s during follow-up, respectively. Age-adjusted SCCS models found evidence of increased risk of AKI associated with BP use (frailty cohort: IRR 1.65; 95% confidence interval [CI], 1.25–2.19), but no association with GI ulcers (frailty cohort: IRR 1.24; 95% CI, 0.86–1.78). Similar results were obtained for the hospitalization and polypharmacy cohorts. Our study found a 50% to 65% increased risk of AKI associated with BP use in elderly patients with complex health needs. Future studies should further investigate the risk–benefit of BP use in these patients. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Introduction Osteoarthritis (OA) is one of the leading chronic conditions in the older population. People with OA are more likely to have one or more other chronic conditions than those without. However, the temporal associations, clusters of the comorbidities, role of analgesics and the causality and variation between populations are yet to be investigated. This paper describes the protocol of a multinational study in four European countries (UK, Netherlands, Sweden and Spain) exploring comorbidities in people with OA. Methods and analysis This multinational study will investigate (1) the temporal associations of 61 identified comorbidities with OA, (2) the clusters and trajectories of comorbidities in people with OA, (3) the role of analgesics on incidence of comorbidities in people with OA, (4) the potential biomarkers and causality between OA and the comorbidities, and (5) variations between countries. A combined case–control and cohort study will be conducted to find the temporal association of OA with the comorbidities using the national or regional health databases. Latent class analysis will be performed to identify the clusters at baseline and joint latent class analysis will be used to examine trajectories during the follow-up. A cohort study will be undertaken to evaluate the role of non-steroidal anti-inflammatory drugs (NSAIDs), opioids and paracetamol on the incidence of comorbidities. Mendelian randomisation will be performed to investigate the potential biomarkers for causality between OA and the comorbidities using the UK Biobank and the Rotterdam Study databases. Finally, a meta-analyses will be used to examine the variations and pool the results from different countries. Ethics and dissemination Research ethics was obtained according to each database requirement. Results will be disseminated through the FOREUM website, scientific meetings, publications and in partnership with patient organisations.

Background People with osteoarthritis (OA) are at higher risk of developing a wide array of comorbidities. Whether the use of non-steroidal anti-inflammatory drugs (NSAIDs) contributes to the increased risk of some incident comorbidities remains unknown. Objectives To examine the contribution of NSAIDs in the development of a wide range of comorbidities in people with and without OA. Methods This observational cohort study used the UK primary care Clinical Practice Research Datalink (CPRD) GOLD containing data on 20+ million people covering 937 practices. We identified 259,000 people with incident OA and 259,000 age (±2 years), sex, and practice matched controls at 1:1 ratio. Controls were assigned the same index date (the date of first diagnosis of OA) as cases for the start of follow-up. Both cases and controls were further divided into two groups according to NSAID prescriptions at any time after the index date. This allowed us to examine both the main effect of each exposure and interaction between OA and NSAID exposure after the index date. People with an NSAID prescription before the index date were excluded from the study. NSAID exposure was defined as at least two prescriptions within 90 days. Exposure status of each participant was assessed every six months as yes/no until the end of the study/outcome of interests/death/last data available, whichever came first. Comorbidities were grouped into 9 categories as cancer, cardiovascular disease (CVD), endocrine, psychological, renal, gastrointestinal (GI), genitourinary, hepatic, and neurological conditions. Propensity scores for the prescription of NSAIDs were calculated using a logistic regression model including age, sex, body mass index (BMI), musculoskeletal and pain related conditions covariates. The propensity score adjusted time varying exposure analysis was undertaken using a multivariate COX model and hazard ratio (HR) and 95% confidence intervals were calculated. Proportional hazard assumption was tested using Schoenfeld test. Smoking, alcohol, ever prescription of proton pump inhibitors (PPIs) and other comorbidities were included in the adjusted model. The additional contribution of NSAIDs and OA towards the incident comorbidity was estimated using addictive interaction methods. We also investigated the individual risk across non-selective, and COX-2 selective NSAIDs. Results The mean age was 59.4±12.8 years in people with OA and 60.2±12.8 years for controls with 57.7% being female. Nearly two thirds of people with OA were prescribed NSAIDs as defined, compared to one third in the control population. People with OA and exposed to NSAIDs had highest risk of developing psychological (1.51; 1.43,1.60), CVD (1.38; 1.33,1.43), cancer (1.34; 1.25,1.44), GI (1.25; 1.16,1.34) and renal (1.17; 1.11,1.24) comorbidities after adjusting for all the covariates and PPI drugs, compared to the non-OA and non-NSAID group. (Figure 1) Interaction between OA and NSAID was significant for cancer, GI, renal, hepatic, and neurological outcomes. Within people with OA, non-selective NSAIDs increased the risk of CVD (1.25; 1.20,1.30), cancer (1.11; 1.04,1.19), endocrine (1.15; 1.10,1.19), renal (1.19; 1.13,1.26) and psychological (1.21; 1.15,1.28) comorbidities, whereas COX-2 selective NSAIDs increased risk of incident CVD (1.34; 1.25,1.44), endocrine (1.13; 1.04,1.21), renal (1.25; 1.14,1.37), and psychological (1.21; 1.09,1.34) comorbidities. Figure 1. Hazard ratio of developing different comorbidities (reference group: no OA and no NSAIDs) OA- Osteoarthritis; NSAIDS- Non-steroidal anti-inflammatory drugs. Conclusion Use of NSAIDs among people with OA is associated with increased risk of a wide variety of comorbidities. Non-selective and COX-2 selective NSAIDs are both associated with increased risk of cardiovascular, renal, and psychological comorbidities. Acknowledgements We thank the Patient Research Participants (PRP) members Jenny Cockshull, Stevie Vanhegan, and Irene Pitsillidou for their involvement since the beginning of the project. We would like to thank the FOREUM for financially supporting the research. Disclosure of Interests Subhashisa Swain: None declared, Anne Kamps: None declared, Jos Runhaar: None declared, Andrea Dell’Isola: None declared, Aleksandra Turkiewicz: None declared, Danielle E Robinson: None declared, Victoria Y Strauss: None declared, Christian Mallen: None declared, Chang-Fu Kuo: None declared, Carol Coupland: None declared, Michael Doherty Consultant of: Consultant of: Advisory borads on gout for Grunenthal and Mallinckrodt, Grant/research support from: Michael Doherty Grant/research support from: AstraZeneca funded the Nottingham Sons of Gout study, Aliya Sarmanova: None declared, Daniel Prieto-Alhambra Speakers bureau: paid speaker services from Amgen and UCB Biopharma., Consultant of: His department has received advisory or consultancy fees from Amgen, Astellas, AstraZeneca, Johnson, and Johnson, and UCB Biopharma, Grant/research support from: Prof. Prieto-Alhambra’s research group has received grant support from Amgen, Chesi-Taylor, Novartis, and UCB Biopharma., Martin Englund: None declared, S.M.A. Bierma-Zeinstra: None declared, Weiya Zhang Speakers bureau: Speakers bureau: Bioiberica as an invited speaker for EULAR 2016 satellite symposium, Consultant of: Consultant of: Grunenthal for advice on gout management,

With the advent of big data in healthcare, machine learning has rapidly gained popularity due to its potential to analyse large volumes of complex data from a variety of sources. Unsupervised learning can be used to mine data and discover patterns such as sub-groups within large patient populations. However challenges with implementation in large-scale datasets and interpretability of solutions in a real-world context remain. This work presents an application of unsupervised clustering techniques for discovering patterns of comorbidities in a large dataset of osteoarthritis patients with a view to discover interpretable and clinically-meaningful patterns.