Risk of hip, subtrochanteric, and femoral shaft fractures among mid and long term users of alendronate: nationwide cohort and nested case-control study

Objectives To determine the skeletal safety and efficacy of long term (≥10 years) alendronate use in patients with osteoporosis. Design Open register based cohort study containing two nested case control studies. Setting Nationwide study of population of Denmark. Participants 61 990 men and women aged 50-94 at the start of treatment, who had not previously taken alendronate, 1996-2007. Interventions Treatment with alendronate. Main outcome measures Incident fracture of the subtrochanteric femur or femoral shaft (ST/FS) or the hip. Non-fracture controls from the cohort were matched to fracture cases by sex, year of birth, and year of initiation of alendronate treatment. Conditional logistic regression models were fitted to calculate odds ratios with and without adjustment for comorbidity and comedications. Sensitivity analyses investigated subsequent treatment with other drugs for osteoporosis. Results 1428 participants sustained a ST/FS (incidence rate 3.4/1000 person years, 95% confidence interval 3.2 to 3.6), and 6784 sustained a hip fracture (16.2/1000 person years, 15.8 to 16.6). The risk of ST/FS was lower with high adherence to treatment with alendronate (medication possession ratio (MPR, a proxy for compliance) >80%) compared with poor adherence (MPR 80% was associated with a decreased risk of hip fracture (0.73, 0.68 to 0.78; P<0.001) as was longer term cumulative use for 5-10 dose years (0.74, 0.67 to 0.83; P<0.001) or ≥10 dose years (0.74, 0.56 to 0.97; P=0.03). Conclusions These findings support an acceptable balance between benefit and risk with treatment with alendronate in terms of fracture outcomes, even for over 10 years of continuous use

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 &gt;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.</p

The Challenges and Opportunities of Pharmacoepidemiology in Bone Diseases

Altres ajuts: This work was supported by the National Health Medical Research Council Australia (NHMRC project ID; DA 1114676, DB 1073430, and JRC 1008219). This work was partially supported by the NIHR Biomedical Research Centre, Oxford. DPA is funded by a National Institute for Health Research Clinician Scientist award (CS-2013-13-012). This article presents independent research funded by the National Institute for Health Research (NIHR). Other funding bodies were the Bupa Health Foundation (formerly MBF Foundation) and the Mrs Gibson and Ernst Heine Family Foundation. The views expressed are those of the authors and not necessarily those of the NHMRC and the NIHR.Pharmacoepidemiology is used extensively in osteoporosis research and involves the study of the use and effects of drugs in large numbers of people. Randomized controlled trials are considered the gold standard in assessing treatment efficacy and safety. However, their results can have limited external validity when applied to day-to-day patients. Pharmacoepidemiological studies aim to assess the effect/s of treatments in actual practice conditions, but they are limited by the quality, completeness, and inherent bias due to confounding. Sources of information include prospectively collected (primary) as well as readily available routinely collected (secondary) (eg, electronic medical records, administrative/claims databases) data. Although the former enable the collection of ad hoc measurements, the latter provide a unique opportunity for the study of large representative populations and for the assessment of rare events at relatively low cost. Observational cohort and case-control studies, the most commonly implemented study designs in pharmacoepidemiology, each have their strengths and limitations. However, the choice of the study design depends on the research question that needs to be answered. Despite the many advantages of observational studies, they also have limitations. First, missing data is a common issue in routine data, frequently dealt with using multiple imputation. Second, confounding by indication arises because of the lack of randomization; multivariable regression and more specific techniques such as propensity scores (adjustment, matching, stratification, trimming, or weighting) are used to minimize such biases. In addition, immortal time bias (time period during which a subject is artefactually event-free by study design) and time-varying confounding (patient characteristics changing over time) are other types of biases usually accounted for using time-dependent modeling. Finally, residual "uncontrolled" confounding is difficult to assess, and hence to account for it, sensitivity analyses and specific methods (eg, instrumental variables) should be considered. © 2018 The Authors JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research

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.</p

Cholinesterase inhibitors and non-steroidal anti-inflammatory drugs and the risk of peptic ulcers:A self-controlled study

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) should be used with caution in adults aged 65 years and older. Their gastrointestinal adverse event risk might be further reinforced when using concomitant cholinesterase inhibitors (ChEIs). We aimed to investigate the association between NSAIDs and ChEI use and the risk of peptic ulcers in adults aged 65 years and older. Methods: Register-based self-controlled case series study including adults ≥65 years with a new prescription of ChEIs and NSAIDs, diagnosed with incident peptic ulcer in Sweden, 2007–2020. We identified persons from the Total Population Register individually linked to several nationwide registers. We estimated the incidence rate ratio (IRR) of peptic ulcer with a conditional Poisson regression model for four mutually exclusive risk periods: use of ChEIs, NSAIDs, and the combination of ChEIs and NSAIDs, compared with the non-treatment in the same individual. Risk periods were identified based on the prescribed daily dose, extracted via a text-parsing algorithm, and a 30-day grace period. Results: Of 70,060 individuals initiating both ChEIs and NSAIDs, we identified 1500 persons with peptic ulcer (median age at peptic ulcer 80 years), of whom 58% were females. Compared with the non-treatment periods, the risk of peptic ulcer substantially increased for the combination of ChEIs and NSAIDs (IRR: 9.0, [6.8–11.8]), more than for NSAIDs alone (5.2, [4.4–6.0]). No increased risks were found for the use of ChEIs alone (1.0, [0.9–1.2]). Discussion: We found that the risk of peptic ulcer associated with the concomitant use of NSAIDs and ChEIs was over and beyond the risk associated with NSAIDs alone. Our results underscore the importance of carefully considering the risk of peptic ulcers when co-prescribing NSAIDs and ChEIs to adults aged 65 years and older.</p

Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study.

Interrupted time series (ITS) analysis is being increasingly used in epidemiology. Despite its growing popularity, there is a scarcity of guidance on power and sample size considerations within the ITS framework. Our aim of this study was to assess the statistical power to detect an intervention effect under various real-life ITS scenarios. ITS datasets were created using Monte Carlo simulations to generate cumulative incidence (outcome) values over time. We generated 1,000 datasets per scenario, varying the number of time points, average sample size per time point, average relative reduction post intervention, location of intervention in the time series, and reduction mediated via a 1) slope change and 2) step change. Performance measures included power and percentage bias. We found that sample size per time point had a large impact on power. Even in scenarios with 12 pre-intervention and 12 post-intervention time points with moderate intervention effect sizes, most analyses were underpowered if the sample size per time point was low. We conclude that various factors need to be collectively considered to ensure adequate power for an ITS study. We demonstrate a means of providing insight into underlying sample size requirements in ordinary least squares (OLS) ITS analysis of cumulative incidence measures, based on prespecified parameters and have developed Stata code to estimate this

Costs of joint replacement in osteoarthritis:a study using the National Joint Registry and Clinical Practice Research Datalink datasets

Objectives: The aim of this study was to estimate the costs of primary hip and knee replacement in individuals with osteoarthritis up to 2 years post-surgery, compare costs before and after the surgery, and identify predictors of hospital costs. Methods: Patients aged 18 years or over with primary planned hip or knee replacements and osteoarthritis in England between 2008 and 2016 were identified from the National Joint Registry and linked with Hospital Episode Statistics data containing inpatient episodes. Primary care data linked with hospital outpatient records were also used to identify patients aged 18 years or over with primary hip or knee replacements between 2008 and 2016. All healthcare resource use was valued using 2016/17 costs and non-parametric censoring methods were used to estimate total 1-year and 2-year costs. Results: We identified 854,866 individuals undergoing hip or knee replacement. The mean censor-adjusted 1-year hospitalisation costs for hip and knee replacement were £7,827 (95% CI £7,813 to £7,842) and £7,805 (95% CI £7,790 to £7,818), respectively. Complications and revisions were associated with up to a three-fold increase in 1-year hospitalisation costs. The censor-adjusted 2-year costs were £9,258 (95 % CI £9,233 to £9,280) and £9,452 (95%CI £9,430 to £9,475) for hip and knee replacement. Adding primary and outpatient care, the mean total hip and knee replacement 2-year costs were £11,987 and £12,578, respectively. Conclusions: There are significant costs following joint replacement. Revisions and complications accounted for considerable costs and there is a significant incentive to identify best approaches to reduce these.</p

Riesgo de fractura asociado a los estadios previos al diagnóstico de la diabetes tipo 2 : estudio de caso-control anidado (cohorte DIAFOS)

El objetivo es comparar la prevalencia de fractura en casos incidentes de diabetes y en controles apareados.L'objectiu és comparar la prevalença de fractura en casos incidents de diabetis i controls aparellats

Fracture risk in type 2 diabetic patients: A clinical prediction tool based on a large population-based cohort

BACKGROUND: An increased fracture risk has been described as a complication of Type 2 diabetes mellitus (T2DM). Clinical prediction models for general population have a limited predictive accuracy for fractures in T2DM patients. The aim was to develop and validate a clinical prediction tool for the estimation of 5-year hip and major fracture risk in T2DM patients. METHODS AND RESULTS: A cohort of newly diagnosed T2DM patients (n = 51,143, aged 50-85, 57% men) was extracted from the Information System for the Development of Research in Primary Care (SIDIAP) database, containing computerized primary care records for >80% of the population of Catalonia, Spain (>6 million people). Patients were followed up from T2DM diagnosis until the earliest of death, transfer out, fracture, or end of study. Cox proportional hazards regression was used to model the 5-year risk of hip and major fracture. Calibration and discrimination were assessed. Hip and major fracture incidence rates were 1.84 [95%CI 1.64 to 2.05] and 7.12 [95%CI 6.72 to 7.53] per 1,000 person-years, respectively. Both hip and major fracture prediction models included age, sex, previous major fracture, statins use, and calcium/vitamin D supplements; previous ischemic heart disease was also included for hip fracture and stroke for major fracture. Discrimination (0.81 for hip and 0.72 for major fracture) and calibration plots support excellent internal validity. CONCLUSIONS: The proposed prediction models have good discrimination and calibration for the estimation of both hip and major fracture risk in incident T2DM patients. These tools incorporate key T2DM macrovascular complications generally available in primary care electronic medical records, as well as more generic fracture risk predictors. Future work will focus on validation of these models in external cohorts