Applying Trial-Derived Treatment Effects to Real-World Populations: Generalizing Cost-Effectiveness Estimates When Modeling Complex Hazards.

OBJECTIVES: Generalizability of trial-based cost-effectiveness estimates to real-world target populations is important for decision making. In the context of independent aggregate time-to-event baseline and relative effects data, complex hazards can make modeling of data for use in economic evaluation challenging. Our article provides an overview of methods that can be used to apply trial-derived relative treatment effects to external real-world baselines when faced with complex hazards and follows with a motivating example. METHODS: Approaches for applying trial-derived relative effects to real-world baselines are presented in the context of complex hazards. Appropriate methods are applied in a cost-effectiveness analysis using data from a previously published study assessing the real-world cost-effectiveness of a treatment for carcinoma of the head and neck as a motivating example. RESULTS: Lack of common hazards between the trial and target real-world population, a complex baseline hazard function, and nonproportional relative effects made the use of flexible models necessary to adequately estimate survival. Assuming common distributions between trial and real-world reference survival substantially affected survival and cost-effectiveness estimates. Modeling time-dependent vs proportional relative effects affected estimates to a lesser extent, dependent on assumptions used in cost-effectiveness modeling. CONCLUSIONS: Appropriately capturing reference treatment survival when attempting to generalize trial-derived relative treatment effects to real-world target populations can have important impacts on cost-effectiveness estimates. A balance between model complexity and adequacy for decision making should be considered where multiple data sources with complex hazards are being evaluated.

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model.

PURPOSE: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). MATERIALS AND METHODS: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. RESULTS: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases. CONCLUSION: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.

IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model.

PURPOSE: Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM). MATERIALS AND METHODS: We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across databases. RESULTS: IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the pandemic. CONCLUSION: For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.

Trends of use and characterisation of anti-dementia drugs users: a large multinational-network population-based study.

BACKGROUND: An updated time-trend analysis of anti-dementia drugs (ADDs) is lacking. The aim of this study is to assess the incident rate (IR) of ADD in individuals with dementia using real-world data. SETTING: Primary care data (country/database) from the UK/CPRD-GOLD (2007-20), Spain/SIDIAP (2010-20) and the Netherlands/IPCI (2008-20), standardised to a common data model. METHODS: Cohort study. Participants: dementia patients ≥40 years old with ≥1 year of previous data. Follow-up: until the end of the study period, transfer out of the catchment area, death or incident prescription of rivastigmine, galantamine, donepezil or memantine. Other variables: age/sex, type of dementia, comorbidities. Statistics: overall and yearly age/sex IR, with 95% confidence interval, per 100,000 person-years (IR per 105 PY (95%CI)). RESULTS: We identified a total of (incident anti-dementia users/dementia patients) 41,024/110,642 in UK/CPRD-GOLD, 51,667/134,927 in Spain/SIDIAP and 2,088/17,559 in the Netherlands/IPCI.In the UK, IR (per 105 PY (95%CI)) of ADD decreased from 2007 (30,829 (28,891-32,862)) to 2010 (17,793 (17,083-18,524)), then increased up to 2019 (31,601 (30,483 to 32,749)) and decrease in 2020 (24,067 (23,021-25,148)). In Spain, IR (per 105 PY (95%CI)) of ADD decreased by 72% from 2010 (51,003 (49,199-52,855)) to 2020 (14,571 (14,109-15,043)). In the Netherlands, IR (per 105 PY (95%CI)) of ADD decreased by 77% from 2009 (21,151 (14,967-29,031)) to 2020 (4763 (4176-5409)). Subjects aged ≥65-79 years and men (in the UK and the Netherlands) initiated more frequently an ADD. CONCLUSIONS: Treatment of dementia remains highly heterogeneous. Further consensus in the pharmacological management of patients living with dementia is urgently needed.

Classification of patients with osteoarthritis through clusters of comorbidities using 633 330 individuals from Spain.

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.

The Hand and Wrist: AntImicrobials and Infection (HAWAII) trial.

BACKGROUND: Hand trauma, comprising injuries to both the hand and wrist, affects over five million people per year in the NHS, resulting in 250 000 operations each year. Surgical site infection (SSI) following hand trauma surgery leads to significant morbidity. Triclosan-coated sutures may reduce SSI in major abdominal surgery but have never been tested in hand trauma. Feasibility needs to be ascertained before a definitive trial can be delivered in hand trauma. METHODS: A multicentre feasibility RCT of antimicrobial sutures versus standard sutures involving adults undergoing surgery for hand trauma to evaluate feasibility for a definitive trial. Secondary objectives were incidence of SSI in both groups, hand function measured with patient-reported outcome measures, health-related quality of life and change in employment. Randomization was performed on a 1:1 basis, stratified by age of the patient and whether the injury was open or closed, using a secure, centralized, online randomization service. Participants were blinded to allocation. RESULTS: 116 participants were recruited and randomized (60 intervention, 56 control). Of 227 screened, most were eligible (89.5 per cent), and most who were approached agreed to be included in the study (84.7 per cent). Retention was low: 57.5 per cent at 30 days, 52 per cent at 90 days and 45.1 per cent at 6 months. Incidence of SSI was >20 per cent in both groups. Hand function deteriorated after injury but recovered to near pre-injury levels during the study period. CONCLUSIONS: Risk of SSI after hand trauma is high. A definitive RCT of antimicrobial sutures in hand trauma surgery is feasible, if retention is improved. TRIAL REGISTRATION: ISRCTN10771059.

Drug utilization analysis of osteoporosis medications in seven European electronic health databases.

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. PURPOSE: To characterise the patients prescribed osteoporosis therapy and describe the drug utilization patterns. METHODS: 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. RESULTS: 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. CONCLUSION: Our results show suboptimal persistence to medications that varied across different databases and treatment switching was relatively rare.

Real-world evidence: new opportunities for osteoporosis research. Recommendations from a Working Group from the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO).

This narrative review summarises the recommendations of a Working Group of the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) for the conduct and reporting of real-world evidence studies with a focus on osteoporosis research. PURPOSE: Vast amounts of data are routinely generated at every healthcare contact and activity, and there is increasing recognition that these real-world data can be analysed to generate scientific evidence. Real-world evidence (RWE) is increasingly used to delineate the natural history of disease, assess real-life drug effectiveness, understand adverse events and in health economic analysis. The aim of this work was to understand the benefits and limitations of this type of data and outline approaches to ensure that transparent and high-quality evidence is generated. METHODS: A ESCEO Working Group was convened in December 2022 to discuss the applicability of RWE to osteoporosis research and approaches to best practice. RESULTS: This narrative review summarises the agreed recommendations for the conduct and reporting of RWE studies with a focus on osteoporosis research. CONCLUSIONS: It is imperative that research using real-world data is conducted to the highest standards with close attention to limitations and biases of these data, and with transparency at all stages of study design, data acquisition and curation, analysis and reporting to increase the trustworthiness of RWE study findings.

Characteristics and outcomes of COVID-19 patients with and without asthma from the United States, South Korea, and Europe.

Objective: Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma.Methods: We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients ('diagnosed' and 'hospitalized') based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions.Results: The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6-8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7-6.8) to 18.5% (95% CI 18.2-18.8) in the diagnosed cohort and 5.2% (95% CI 4.0-6.8) to 20.5% (95% CI 18.6-22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8-2.4) to 16.9% (95% CI 13.8-20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15-30% of hospitalized COVID-19 asthma patients.Conclusion: The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.[Box: see text]Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392 .

Risk of adverse events following the initiation of antihypertensives in older people with complex health needs: a self-controlled case series in the United Kingdom.

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.

Characterising complex health needs and the use of preventive therapies in the older population: a population-based cohort analysis of UK primary care and hospital linked data.

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.

Socioeconomic Inequalities in COVID-19 Vaccination and Infection in Adults, Catalonia, Spain.

Evidence on the impact of the COVID-19 vaccine rollout on socioeconomic COVID-19-related inequalities is scarce. We analyzed associations between socioeconomic deprivation index (SDI) and COVID-19 vaccination, infection, and hospitalization before and after vaccine rollout in Catalonia, Spain. We conducted a population-based cohort study during September 2020-June 2021 that comprised 2,297,146 adults >40 years of age. We estimated odds ratio of nonvaccination and hazard ratios (HRs) of infection and hospitalization by SDI quintile relative to the least deprived quintile, Q1. Six months after rollout, vaccination coverage differed by SDI quintile in working-age (40-64 years) persons: 81% for Q1, 71% for Q5. Before rollout, we found a pattern of increased HR of infection and hospitalization with deprivation among working-age and retirement-age (>65 years) persons. After rollout, infection inequalities decreased in both age groups, whereas hospitalization inequalities decreased among retirement-age persons. Our findings suggest that mass vaccination reduced socioeconomic COVID-19-related inequalities.

Cancer and the risk of coronavirus disease 2019 diagnosis, hospitalisation and death: A population-based multistate cohort study including 4 618 377 adults in Catalonia, Spain.

The relationship between cancer and coronavirus disease 2019 (COVID-19) infection and severity remains poorly understood. We conducted a population-based cohort study between 1 March and 6 May 2020 describing the associations between cancer and risk of COVID-19 diagnosis, hospitalisation and COVID-19-related death. Data were obtained from the Information System for Research in Primary Care (SIDIAP) database, including primary care electronic health records from ~80% of the population in Catalonia, Spain. Cancer was defined as any primary invasive malignancy excluding non-melanoma skin cancer. We estimated adjusted hazard ratios (aHRs) for the risk of COVID-19 (outpatient) clinical diagnosis, hospitalisation (with or without a prior COVID-19 diagnosis) and COVID-19-related death using Cox proportional hazard regressions. Models were estimated for the overall cancer population and by years since cancer diagnosis (<1 year, 1-5 years and ≥5 years), sex, age and cancer type; and adjusted for age, sex, smoking status, deprivation and comorbidities. We included 4 618 377 adults, of which 260 667 (5.6%) had a history of cancer. A total of 98 951 individuals (5.5% with cancer) were diagnosed, and 6355 (16.4% with cancer) were directly hospitalised with COVID-19. Of those diagnosed, 6851 were subsequently hospitalised (10.7% with cancer), and 3227 died without being hospitalised (18.5% with cancer). Among those hospitalised, 1963 (22.5% with cancer) died. Cancer was associated with an increased risk of COVID-19 diagnosis (aHR: 1.08; 95% confidence interval [1.05-1.11]), direct COVID-19 hospitalisation (1.33 [1.24-1.43]) and death following hospitalisation (1.12 [1.01-1.25]). These associations were stronger for patients recently diagnosed with cancer, aged <70 years, and with haematological cancers. These patients should be prioritised in COVID-19 vaccination campaigns and continued non-pharmaceutical interventions.

Knee and hip osteoarthritis and risk of nine cancers in a large real-world matched cohort study.

OBJECTIVES: Joint replacement due to end-stage OA has been linked to incidence of several cancers. We aimed to estimate the association between newly diagnosed knee and hip OA and incidence of nine common cancer types. METHODS: We identified persons with incident knee or hip OA, aged ≥40 years, between 2009 and 2015 in the SIDIAP database in Catalonia, Spain. We matched up to three OA-free controls on age, sex and general practitioner. We followed participants from 1 year after OA diagnosis until migration, death, end of study at 31 December 2017 or incident cancer of: stomach, colorectal, liver, pancreas, lung, skin, breast, prostate and bladder. We used flexible parametric survival models, adjusted for confounders. Estimates were corrected for misclassification using probabilistic bias analysis. RESULTS: We included 117 750 persons with knee OA and matched 309 913 persons without, with mean (s.d.) age of 67.5 (11.1) years and 63% women. The hip cohort consisted of 39 133 persons with hip OA and 116 713 controls. For most of the included cancers, the hazard ratios (HRs) were close to 1. The HR of lung cancer for knee OA exposure was 0.80 (95% CI: 0.71, 0.89) and attenuated to 0.98 (0.76, 1.27) in non-smokers. The hazard of colorectal cancer was lower in persons with both knee and hip OA by 10-20%. CONCLUSIONS: Knee and hip OA are not associated with studied incident cancers, apart from lower risk of colorectal cancer. The often-reported protective association of knee OA with lung cancer is explained by residual confounding.

One- and 2-year incidence of osteoporotic fracture: a multi-cohort observational study using routinely collected real-world data.

We estimated and characterized the imminent fracture risk (1-2 years) of high-risk fracture patients through a multinational (UK, Spain, Denmark) cohort study. Older individuals with newly diagnosed osteoporosis and individuals who had a fracture while on treatment with a bisphosphonate were at a high risk of imminent fracture. PURPOSE: To characterize and estimate 1- to 2-year fracture risk in high-risk fracture patients. METHODS: Multi-cohort study in (database/study period) UK (CPRD/1995-2017), Spain (SIDIAP/2006-2016) and Denmark (DHR/1995-2016) including individuals ≥ 50 years old in NDO (newly diagnosed osteoporosis), OFx (incident osteoporotic fracture), BP (incident oral bisphosphonates use) or FWOT (fracture while on treatment with bisphosphonates). Outcomes (ICD-10/READ): hip, clinical spine, non-hip, non-spine and hip/humerus/distal forearm fracture. FOLLOW-UP: from cohort entry until death, migration/transfer or end of the study. STATISTICS: baseline characteristics and incidence rate (IR per 1000 persons). RESULTS (1-YEAR IR): NDO included 69,899 (UK), 37,901 (Spain) and 158,191 (Denmark) individuals. Spanish-IR was lowest for hip (4.7), clinical spine (2.5) and major osteoporotic fracture (MOF) (17.3) and highest in Denmark (74.2, 26.0 and 120.1, respectively). OFx included 83,514 (UK), 51,044 (Spain) and 509,551 (Denmark) individuals. IR in Denmark was highest for hip (24.1) and MOF (47.2), in Spain was highest for the clinical spine (9.4) and lowest for hip (9.5) and in the UK was lowest for the clinical spine (2.8) and MOF (20.7). BP included 148,507 (UK), 52,037 (Spain) and 204,010 (Denmark) individuals. Spanish-IR was lowest for hip (5.0) and MOF (21.1) and highest in Denmark (20.3 and 48.6, respectively). FWOT included 28,930 (UK), 1,865 (Spain) and 31,882 (Denmark) individuals. Clinical spine-IR was highest for Spain (12.0). Hip-IR was lowest for Spain (7.6) and highest for Denmark (33.6). Comparing young subjects, those who have FWOT started with an increased fracture rate. CONCLUSION: OFx and FWOT individuals experience higher re-fracture incidence rates than those with osteoporosis with or without treatment.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Development and validation of a model for predicting mortality in patients with hip fracture.

OBJECTIVE: to develop a user-friendly prediction tool of 1-year mortality for patients with hip fracture, in order to guide clinicians and patients on appropriate targeted preventive measures. DESIGN: population-based cohort study from 2011 to 2017 using nationwide data from the Danish Hip Fracture Registry. SUBJECTS: a total of 28,791 patients age 65 and above undergoing surgery for a first-time hip fracture. METHODS: patient-related prognostic factors at the time of admission were assessed as potential predictors: Nursing home residency, comorbidity (Charlson Comorbidity Index [CCI] Score), frailty (Hospital Frailty Risk Score), basic mobility (Cumulated Ambulation Score), atrial fibrillation, fracture type, body mass index (BMI), age and sex. Association with 1-year mortality examined by determining the cumulative incidence, applying univariable logistic regression and assessing discrimination (area under the receiver operating characteristics curve [AUROC]). The final model (logistic regression) was utilised on a development cohort (70% of patients). Discrimination and calibration were assessed on the validation cohort (remaining 30% of patients). RESULTS: all predictors showed an association with 1-year mortality, but discrimination was moderate. The final model included nursing home residency, CCI Score, Cumulated Ambulation Score, BMI and age. It had an acceptable discrimination (AUROC 0.74) and calibration, and predicted 1-year mortality risk spanning from 5 to 91% depending on the combination of predictors in the individual patient. CONCLUSIONS: using information obtainable at the time of admission, 1-year mortality among patients with hip fracture can be predicted. We present a user-friendly chart for daily clinical practice and provide new insight regarding the interplay between prognostic factors.

90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model.

PURPOSE: The purpose of this study was to develop and validate a prediction model for 90-day mortality following a total knee replacement (TKR). TKR is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify high-risk patients who could be targeted with preventative interventions. The aim was to develop and validate a simple model to help inform treatment choices. METHODS: A mortality prediction model for knee OA patients following TKR was developed and externally validated using a US claims database and a UK general practice database. The target population consisted of patients undergoing a primary TKR for knee OA, aged ≥ 40 years and registered for ≥ 1 year before surgery. LASSO logistic regression models were developed for post-operative (90-day) mortality. A second mortality model was developed with a reduced feature set to increase interpretability and usability. RESULTS: A total of 193,615 patients were included, with 40,950 in The Health Improvement Network (THIN) database and 152,665 in Optum. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and 0.70 when externally validated on THIN. The 12 variable model achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. CONCLUSIONS: A simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR was developed that demonstrated good, robust performance. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and targeting prophylaxis for those at high risk. LEVEL OF EVIDENCE: III.

Risk factors for a second nonsimultaneous hip fracture in a prospective cohort study.

INTRODUCTION: The risk factors for a second nonsimultaneous hip fracture are unclear, and in general, it is empirically assumed that they are similar to those associated with the first hip fracture. We aimed to determine the incidence of a second hip fracture and define the characteristics of the patients sustaining the event in a prospective cohort study in a Spanish population. MATERIALS AND METHODS: We conducted a multicentric, prospective cohort study in a representative sample of 45 hospitals from 15 autonomic regions in Spain. In total, the study included 994 patients. One hundred and one patients presented a nonsimultaneous contralateral hip fracture, constituting the intervention group. The remaining 893 patients presenting with a hip fracture formed the control group. The main outcome measures of this study were sociodemographic characteristics of the patient, comorbid conditions, and baseline and postfracture clinical outcomes (inpatient complications and acute mortality). RESULTS: The key fracture risk factors were a history of fragility fractures, the need for assistance when walking outdoors and a history of falls. There were no associations between the groups in any of the common fragility risk factors, including rheumatoid arthritis, secondary osteoporosis, or steroid consumption. The results showed that patients suffering a nonsimultaneous hip fracture had an increased risk of mortality after discharge compared with the control group. CONCLUSION: A nonsimultaneous second hip fracture leads to a near-significant increase in four-month mortality. In our study, this fracture was associated with a history of falls, prior fragility fractures, and the need for a walking aid.

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom.

BACKGROUND: A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. METHODS: We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. RESULTS: We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. CONCLUSION: We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.