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Participating in sport carries inherent risk of injury. Clinicians execute high-level clinical reasoning and decision making to support athletes to achieve the best outcomes. Accurately diagnosing a problem, estimating prognosis, or selecting the most suitable intervention for each athlete is challenging. Clinical prediction models are tools to assist clinicians in estimating the risk or probability of a health outcome for an individual by using data from multiple predictors. Although common in general medical literature, clinical prediction models are rare in sports medicine. The purpose of this article was to (1) describe the steps required to develop and validate (ie, evaluate) a clinical prediction model for clinical researchers, and (2) help sports medicine clinicians understand and interpret clinical prediction model studies. Using a case study to illustrate how to implement clinical prediction models in practice, we address the following issues in developing and validating a clinical prediction model: study design and data, sample size, missing data, selecting predictors, handling continuous predictors, model fitting, internal and external validation, performance measures, reporting, and model presentation. Our work builds on initiatives to improve diagnostic and prognostic clinical research, including the PROGnosis RESearch Strategy (PROGRESS) series of papers and textbook and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

Background Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers. Methods MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method. Results The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change. Conclusions The applicability of identified methods depends on the motivation for including longitudinal information and the method’s compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.

The belief that weather influences people’s health has been prevalent for millennia. Recent studies on the relationship between weather and pain for those who suffer from chronic pain remain indeterminate, with some studies finding strong effects and others finding no effects; most studies face limitations to their study design or dataset size. To address these limitations, a U.K.-wide smartphone study Cloudy with a Chance of Pain was conducted over 15 months with 10,584 citizen scientists who suffer from chronic pain, producing the largest dataset both in duration and number of participants. Compared to other similar citizen-science studies, our retention of participants was substantially better, with 15% still entering data nearly every day after 200 days. Analysis of the dataset using synoptic climatology and compositing revealed the daily weather associated with a prevalence of high pain and low pain across the population. Specifically, our results indicate that the top 10% of days with a high percentage of participants (about 20%) experiencing a pain event (represented here by a +1 change or greater in their pain level on a 5-point scale; referred to as a high-pain day) were associated with below-normal pressure, above-normal humidity, higher precipitation rate, and stronger wind. In contrast, the bottom 10% of days with a small percentage of participants (about 10%) experiencing a pain event (a low-pain day) were associated with above-normal pressure, below-normal humidity, lower precipitation rate, and weaker wind. Thus, these synoptic weather patterns support the beliefs of many participants who said that low pressure—and its accompanying weather—was associated with a pain event.

Background First-degree relatives (FDRs) of people with rheumatoid arthritis (RA) have a fourfold increased risk of developing RA. The Symptoms in Persons At Risk of Rheumatoid Arthritis (SPARRA) questionnaire was developed to document symptoms in persons at risk of RA. The aims of this study were (1) to describe symptoms in a cohort of FDRs of patients with RA overall and stratified by seropositivity and elevated CRP and (2) to determine if patient characteristics were associated with symptoms suggestive of RA. Methods A cross-sectional study of FDRs of patients with RA, in the PREVeNT-RA study, who completed a study questionnaire, provided a blood sample measured for rheumatoid factor, anti-CCP and CRP and completed the SPARRA questionnaire. Moderate/severe symptoms and symmetrical, small and large joint pain were identified and described. Symptoms associated with both seropositivity and elevated CRP were considered suggestive of RA. Logistic regression was used to determine if symptoms suggestive of RA were associated with patient characteristics. Results Eight hundred seventy participants provided all data, 43 (5%) were seropositive and 122 (14%) had elevated CRP. The most frequently reported symptoms were sleep disturbances (20.3%) and joint pain (17.9%). Symmetrical and small joint pain were 11.3% and 12.8% higher, respectively, in those who were seropositive and 11.5% and 10.7% higher in those with elevated CRP. In the logistic regression model, seropositivity, older age and feeling depressed were associated with increased odds of small and symmetrical joint pain. Conclusions This is the first time the SPARRA questionnaire has been applied in FDRs of patients with RA and has demonstrated that the presence of symmetrical and small joint pain in this group may be useful in identifying people at higher risk of developing RA.

This study used periodic health examination (PHE) data from 134 outfield elite male football players, over 5 seasons (1st July 2013 to 19th May 2018). Univariable and multivariable logistic regression models were used to determine prognostic associations between 36 variables and time-loss, lower extremity index indirect muscle injuries (I-IMIs). Non-linear associations were explored using fractional polynomials. During 317 participant-seasons, 138 I-IMIs were recorded. Univariable associations were determined for previous calf indirect muscle injury (IMI) frequency (OR=1.80, 95% confidence interval (CI) = 1.09 to 2.97), hamstring IMI frequency (OR=1.56, 95% CI=1.17 to 2.09), if the most recent hamstring IMI occurred > 12 months but < 3 years prior to PHE (OR= 2.95, 95% CI = 1.51 to 5.73) and age (OR =1.12 per 1-year increase, 95% CI = 1.06 to 1.18). Multivariable analyses demonstrated that if a player’s most recent previous hamstring IMI was >12 months but

Background Propensity scores are widely used to deal with confounding bias in medical research. An incorrectly specified propensity score model may lead to residual confounding bias; therefore it is essential to use diagnostics to assess propensity scores in a propensity score analysis. The current use of propensity score diagnostics in the medical literature is unknown. The objectives of this study are to (1) assess the use of propensity score diagnostics in medical studies published in high-ranking journals, and (2) assess whether the use of propensity score diagnostics differs between studies (a) in different research areas and (b) using different propensity score methods. Methods A PubMed search identified studies published in high-impact journals between Jan 1st 2014 and Dec 31st 2016 using propensity scores to answer an applied medical question. From each study we extracted information regarding how propensity scores were assessed and which propensity score method was used. Research area was defined using the journal categories from the Journal Citations Report. Results A total of 894 papers were included in the review. Of these, 187 (20.9%) failed to report whether the propensity score had been assessed. Commonly reported diagnostics were p-values from hypothesis tests (36.6%) and the standardised mean difference (34.6%). Statistical tests provided marginally stronger evidence for a difference in diagnostic use between studies in different research areas (p = 0.033) than studies using different propensity score methods (p = 0.061). Conclusions The use of diagnostics in the propensity score medical literature is far from optimal, with different diagnostics preferred in different areas of medicine. The propensity score literature may improve with focused efforts to change practice in areas where suboptimal practice is most common.

Background In elite football (soccer), periodic health examination (PHE) could provide prognostic factors to predict injury risk. Objective To develop and internally validate a prognostic model to predict individualised indirect (non-contact) muscle injury (IMI) risk during a season in elite footballers, only using PHE-derived candidate prognostic factors. Methods Routinely collected preseason PHE and injury data were used from 152 players over 5 seasons (1st July 2013 to 19th May 2018). Ten candidate prognostic factors (12 parameters) were included in model development. Multiple imputation was used to handle missing values. The outcome was any time-loss, index indirect muscle injury (I-IMI) affecting the lower extremity. A full logistic regression model was fitted, and a parsimonious model developed using backward-selection to remove factors that exceeded a threshold that was equivalent to Akaike’s Information Criterion (alpha 0.157). Predictive performance was assessed through calibration, discrimination and decision-curve analysis, averaged across all imputed datasets. The model was internally validated using bootstrapping and adjusted for overfitting. Results During 317 participant-seasons, 138 I-IMIs were recorded. The parsimonious model included only age and frequency of previous IMIs; apparent calibration was perfect, but discrimination was modest (C-index = 0.641, 95% confidence interval (CI) = 0.580 to 0.703), with clinical utility evident between risk thresholds of 37–71%. After validation and overfitting adjustment, performance deteriorated (C-index = 0.589 (95% CI = 0.528 to 0.651); calibration-in-the-large = − 0.009 (95% CI = − 0.239 to 0.239); calibration slope = 0.718 (95% CI = 0.275 to 1.161)). Conclusion The selected PHE data were insufficient prognostic factors from which to develop a useful model for predicting IMI risk in elite footballers. Further research should prioritise identifying novel prognostic factors to improve future risk prediction models in this field. Trial registration NCT03782389

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital or dying with the disease.Design Living systematic review and critical appraisal by the covid-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.Data sources PubMed and Embase through Ovid, up to 17 February 2021, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.Study selection Studies that developed or validated a multivariable covid-19 related prediction model.Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).Results 126 978 titles were screened, and 412 studies describing 731 new prediction models or validations were included. Of these 731, 125 were diagnostic models (including 75 based on medical imaging) and the remaining 606 were prognostic models for either identifying those at risk of covid-19 in the general population (13 models) or predicting diverse outcomes in those individuals with confirmed covid-19 (593 models). Owing to the widespread availability of diagnostic testing capacity after the summer of 2020, this living review has now focused on the prognostic models. Of these, 29 had low risk of bias, 32 had unclear risk of bias, and 545 had high risk of bias. The most common causes for high risk of bias were inadequate sample sizes (n=408, 67%) and inappropriate or incomplete evaluation of model performance (n=338, 56%). 381 models were newly developed, and 225 were external validations of existing models. The reported C indexes varied between 0.77 and 0.93 in development studies with low risk of bias, and between 0.56 and 0.78 in external validations with low risk of bias. The Qcovid models, the PRIEST score, Carr’s model, the ISARIC4C Deterioration model, and the Xie model showed adequate predictive performance in studies at low risk of bias. Details on all reviewed models are publicly available at https://www.covprecise.org/.Conclusion Prediction models for covid-19 entered the academic literature to support medical decision making at unprecedented speed and in large numbers. Most published prediction model studies were poorly reported and at high risk of bias such that their reported predictive performances are probably optimistic. Models with low risk of bias should be validated before clinical implementation, preferably through collaborative efforts to also allow an investigation of the heterogeneity in their performance across various populations and settings. Methodological guidance, as provided in this paper, should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction modellers should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.Readers’ note This article is the final version of a living systematic review that has been updated over the past two years to reflect emerging evidence. This version is update 4 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Many people believe that weather influences chronic musculoskeletal pain. Previous studies on this association are narratively reviewed, with particular focus on comparing methodologies and summarising study findings in light of study quality. We searched 5 databases (Medline, Embase, Web of Science, PsycINFO, and Scopus) for observational studies on the association between weather variables and self-reported musculoskeletal pain severity. Of 4707 located articles, 43 were eligible for inclusion. The majority (67%) found some association between pain and a weather variable. Temperature, atmospheric pressure, relative humidity, and precipitation were most often investigated. For each weather variable, some studies found an association with pain (in either direction), and others did not. Most studies (86%) had a longitudinal study design, usually collecting outcome data for less than a month, from fewer than 100 participants. Most studies blinded participants to study aims but were at a high risk of misclassification of exposure and did not meet reporting requirements. Pain severity was most often self-reported (84%) on a numeric rating scale or visual analog scale. Weather data were collected from local weather stations, usually on the assumption that participants stayed in their home city. Analysis methods, preparation of weather data, and adjustment for covariates varied widely between studies. The association between weather and pain has been difficult to characterise. To obtain more clarity, future studies should address 3 main limitations of the previous literature: small sample sizes and short study durations, misclassification of exposure, and approach to statistical analysis (specifically, multiple comparisons and adjusting for covariates).

Background Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. Objective The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. Methods We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants’ time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. Results For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data. Conclusions The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants.

Introduction: Surgical intervention for patients presenting with a fractured neck of femur is common, however the best method of skin closure remains unclear. It has been suggested that the use of metallic skin clips in the closure of hip wounds may increase the rate of surgical site infections, however there are few high quality studies evaluating this. Methods: We designed a survey to assess surgeons preferred skin closure method and their reasons for choosing it. This was sent to 545 consultant orthopaedic surgeons. Results: The positive response rate to our survey was 68% (n = 370). The survey showed that 44% of surgeons used metallic skin clips, and this was the most common method of closure reported. Hip surgeons were less likely to use metallic skin clips when compared to surgeons with other principal subspecialty practices. Surgeons using clips were most likely to state the rationale for use as convenience, whereas those using non-clip skin closure methods were more likely to cite reduced rates of wound inflammation, discharge, and infection as the rationale for use. Only a small proportion of surgeons indicate that their decisions were evidence based. Conclusions: Skin closure following surgery for fractured neck of femur is not consistent amongst surgeons and is not driven by evidence. Greater levels of evidence in this field are required to improve outcomes in this patient group.

BackgroundIn elite football (soccer), periodic health examination (PHE) could provide prognostic factors to predict injury risk.ObjectiveTo develop and internally validate a prognostic model to predict individual indirect (non-contact) muscle injury (IMI) risk during a season in elite footballers, only using PHE-derived candidate prognostic factors.MethodsRoutinely collected preseason PHE and injury data were used from 119 players over 5 seasons (1stJuly 2013 to 19thMay 2018). Ten candidate prognostic factors (12 parameters) were included in model development. Multiple imputation was used to handle missing values. The outcome was any time-loss, index indirect muscle injury (I-IMI) affecting the lower extremity. A full logistic regression model was fitted, and a parsimonious model developed using backward-selection to remove non-significant factors. Predictive performance was assessed through calibration, discrimination and decision-curve analysis, averaged across all imputed datasets. The model was internally validated using bootstrapping and adjusted for overfitting.ResultsDuring 317 participant-seasons, 138 I-IMIs were recorded. The parsimonious model included only age and frequency of previous IMIs; apparent calibration was perfect but discrimination was modest (C-index = 0.641, 95% confidence interval (CI): 0.580 to 0.703), with clinical utility evident between risk thresholds of 37-71%. After validation and overfitting adjustment, performance deteriorated (C-index = 0.580; calibration-in-the-large =-0.031, calibration slope =0.663).ConclusionThe selected PHE data were insufficient prognostic factors from which to develop a useful model for predicting IMI risk in elite footballers. Further research should prioritise identifying novel prognostic factors to improve future risk prediction models in this field.Trial registration numberNCT03782389KEY POINTSFactors measured through preseason screening generally have weak prognostic strength for future indirect muscle injuries and further research is needed to identify novel, robust prognostic factors.Because of sample size restrictions, and until the evidence base improves, it is likely that any further attempts at creating a prognostic model at individual club level would also suffer from poor performance.The value of using preseason screening data to make injury predictions or to select bespoke injury prevention strategies remains to be demonstrated, so screening should only be considered as useful for detection of salient pathology or for rehabilitation/ performance monitoring purposes at this time.

Background Indirect muscle injuries (IMIs) are a considerable burden to elite football (soccer) teams, and prevention of these injuries offers many benefits. Preseason medical, musculoskeletal and performance screening (termed periodic health examination (PHE)) can be used to help determine players at risk of injuries such as IMIs, where identification of PHE-derived prognostic factors (PF) may inform IMI prevention strategies. Furthermore, using several PFs in combination within a multivariable prognostic model may allow individualised IMI risk estimation and specific targeting of prevention strategies, based upon an individual’s PF profile. No such models have been developed in elite football and the current IMI prognostic factor evidence is limited. This study aims to (1) develop and internally validate a prognostic model for individualised IMI risk prediction within a season in elite footballers, using the extent of the prognostic evidence and clinical reasoning; and (2) explore potential PHE-derived PFs associated with IMI outcomes in elite footballers, using available PHE data from a professional team. Methods This is a protocol for a retrospective cohort study. PHE and injury data were routinely collected over 5 seasons (1 July 2013 to 19 May 2018), from a population of elite male players aged 16–40 years old. Of 60 candidate PFs, 15 were excluded. Twelve variables (derived from 10 PFs) will be included in model development that were identified from a systematic review, missing data assessment, measurement reliability evaluation and clinical reasoning. A full multivariable logistic regression model will be fitted, to ensure adjustment before backward elimination. The performance and internal validation of the model will be assessed. The remaining 35 candidate PFs are eligible for further exploration, using univariable logistic regression to obtain unadjusted risk estimates. Exploratory PFs will also be incorporated into multivariable logistic regression models to determine risk estimates whilst adjusting for age, height and body weight. Discussion This study will offer insights into clinical usefulness of a model to predict IMI risk in elite football and highlight the practicalities of model development in this setting. Further exploration may identify other relevant PFs for future confirmatory studies and model updating, or influence future injury prevention research.

BackgroundIn 2017, the WHO produced its first minimum data set (MDS) for emergency medical team (EMT) daily reporting during the sudden-onset disasters (SODs), following expert consensus. The MDS was deliberately designed to be simple in order to improve the rate of data capture; however, it is new and untested. This study assesses the inter-rater agreement between practitioners when performing the injury aspect of coding within the WHO EMT MDS.Methods25 clinical case vignettes were developed, reflecting potential injuries encountered in an SOD. These were presented online from April to July 2018 to practitioners who have experience of/training in managing patients in SODs The practitioners were from UK-Med’s members, Australian Medical Assistance Team’s Northern Territory members and New Zealand Medical Assistance Team members. Practitioners were asked to code injuries according to WHO EMT MDS case classifications. Randolph’s kappa statistic for free-marginal multirater data was calculated for the whole dataset as well as subgroups to ascertain inter-rater agreement.Results86 practitioners responded (20.6% response rate), giving >2000 individual case responses. Overall agreement was moderate at 67.9% with a kappa of 0.59 (95% CI 0.49 to 0.69). Despite subgroups of paramedics (kappa 0.63, 95% CI 0.53 to 0.72), doctors (kappa 0.61, 95% CI 0.52 to 0.69) and those with disaster experience (kappa 0.62, 95% CI 0.52 to 0.71) suggesting slightly higher agreement, their CIs (and those of other subgroups) suggest overall similar and moderate levels of practitioner agreement in classifying injuries according to the MDS categories.ConclusionsAn inter-rater agreement of 0.59 is moderate, at best, however, it gives ministries of health some sense of how tightly they may interpret injury data derived from daily reports using WHO EMT MDS. Furthermore, this kappa is similar to established but more complex (thus more contextually impractical) injury scores. Similar studies, with weighting for injury likelihood using sample data from SODs would further refine the level of expected inter-rater agreement.

Objectives To characterise the 10 year relationship between anti-transcriptional intermediary factor 1 antibody (anti-TIF1-Ab) positivity and cancer onset in a large UK-based adult dermatomyositis (DM) cohort. Methods Data from anti-TIF1-Ab positive/negative adults with verified diagnoses of DM from UKMYONET were analysed. Each patient was followed up until they developed cancer. Kaplan-Meier methods and Cox-proportional hazard modelling were employed to estimate cumulative cancer incidence. Results Data from 263 DM cases were analysed, with a total of 3,252 person-years and a median 11 years follow-up; 55 (21%) of DM cases were anti-TIF1-Ab positive. After 10 years of follow up, a higher proportion of anti-TIF1-Ab positive cases developed cancer, compared with anti-TIF1-Ab negative cases: 38% vs 15% (hazard ratio 3.4 [95% CI 2.2, 5.4]). All the detected malignancy cases in the anti-TIF1-Ab positive cohort occurred between 3 years prior to and 2.5 years after DM onset. No cancer cases were detected within the following 7.5 years in this group, whereas cancers were detected during this period in the anti-TIF1-Ab negative cases. Ovarian cancer was more common in the anti-TIF1-Ab positive vs negative cohort – 19% versus 2% respectively (p-value

Objective In order to develop interventions to optimize MTX use for the treatment of RA we evaluated the rate of, reasons for and predictors of MTX non-adherence during the first 6 months of therapy. Methods The Rheumatoid Arthritis Medication Study (RAMS) is a prospective multicentre cohort study of incident MTX users in the UK. Prior to MTX commencement demographic, clinical and psychological data were collected. A weekly patient-completed diary recorded MTX dose, possible side effects and adherence over 26 weeks. The number of non-adherent weeks was calculated. Potential baseline predictors of ever non-adherence (⩾1 week non-adherent) during the first 6 months of MTX therapy were identified using logistic regression analyses. Results 606 patients with RA were included; 69% female, mean (s.d.) age 60 (13) years and DAS28 score 4.2 (1.2). Over the first 6 months following MTX initiation, 158 (26%) patients were ever non-adherent (71% intentional, 19% non-intentional, 10% unexplained) and mean (s.d.) number of non-adherent weeks was 2.5 (2.1). Multivariable predictors of ever non-adherence included DAS28 [odds ratios (OR) 1.1, 95% CI 1.0, 1.4], fatigue (OR 1.1, 95% CI 1.0, 1.2 per cm), ⩾2 comorbidities vs no comorbidities (OR 1.9, 95% CI 1.1, 3.5) and high medication concerns despite perceived need (OR 1.1, 95% CI 1.0, 1.1 per unit decrease in need/concern differential). Conclusion This is the largest study evaluating early intentional and non-intentional non-adherence to MTX, which has identified that patient beliefs and multi-morbidity strongly link with non-adherence. These findings can direct the design of and provide potential targets for interventions to improve patient adherence.

Background Patient and public involvement (PPI) in research is increasingly common (1), but the experience of research participants is rarely evaluated, missing opportunities to gain insights for improving future studies. (2) Observational studies are often used to study natural progression and treatment response in chronic diseases like rheumatoid arthritis (RA). Objectives We aimed to pilot a participant experience questionnaire in an observational study of RA patients, to gain feedback on the study and evaluate the questionnaire as a feedback tool. Methods The Rheumatoid Arthritis Medication Study (RAMS) is a large UK prospective observational study of patients with RA or undifferentiated polyarthritis starting methotrexate (MTX) for the first time. Participants were recruited prior to initiation of MTX and followed-up at 3, 6 and 12 months. At visits, disease activity is measured and participants complete a study questionnaire including patient reported outcomes. Participants also complete a weekly diary about their MTX use. (3) A subset of RAMS participants were given a feedback questionnaire at their final visit. The questionnaire was designed by researchers and study coordinators with feedback from patients. Questions addressed the value of participation, study conduct and priorities for future research. Multiple-choice question responses were summarised and key themes were identified in the free-text responses. Results 201 of 357 (56%) patients eligible to receive the questionnaire completed it. Multiple-choice questions had 100% completion and responses are presented in Table 1. 189 (94%) participants agreed that participating in RAMS was worthwhile and 180 (90%) would recommend participation to eligible friends and family. The most frequent theme in recommending the study was the desire to “help people” (47%) and most respondents (57%) did not know what future research they would like carried out or did not answer. Conclusion The majority of RAMS participants were happy to share their experience of participation and had positive views. Many had altruistic reasons for taking part. Multiple-choice questions had high completion rates and free-text fields were less completed. More details on participant research priorities may be gained by offering options to select from. Future investigation of participant experience should also consider those lost to follow-up, participants who choose to withdraw, and potential participants who did not enrol. References [1] INVOLVE. Approaches to Public Involvement: NIHR; [http://www.invo.org.uk/posttyperesource/approaches-to-public-involvement/] [2] Planner C, Bower P. Trials need participants but not their feedback? A scoping review of measures of participant experience of taking part in trials. School for Primary Care Research - 2017 Showcase Blavatnik School of Government; Oxford: NIHR; 2017 [3] Sergeant JC, Hyrich KL, Anderson J, et al. OP0013 Prediction of Non-Response To Methotrexate Therapy in The Rheumatoid Arthritis Medication Study (RAMS). Annals of the Rheumatic Diseases2016. p. 57-8 Disclosure of Interests None declared

Background First degree relatives (FDRs) of people with rheumatoid arthritis (RA) have a four-fold increased risk of developing RA. The Symptoms in Persons At Risk of Rheumatoid Arthritis (SPARRA) questionnaire was developed to document symptoms in persons at risk of RA. Objectives To describe i) symptoms in a cohort of FDRs of patients with RA using the SPARRA questionnaire and (ii) the proportion of these symptoms in participants with detectable autoantibodies or elevated CRP. Methods The PRe-clinical EValuation of Novel Targets in RA (PREVeNT-RA) study is a cohort of FDRs of patients with RA, who are without inflammatory arthritis and aged ≥30 years at recruitment. Consented participants completed a study questionnaire and provided a blood sample for measurement of Rheumatoid factor (RF), anti-cyclic citrullinated peptide antibodies (anti-CCP) and C-reactive protein (CRP). Those who agreed to future contact were asked to complete the SPARRA questionnaire. This questionnaire asks about a variety of joint symptoms, other symptoms and severity of joint pain in specific parts of the body, allowing identification of symmetrical, small and large joint pain. We identified subjects with moderate/severe symptoms and rheumatoid distribution (symmetrical, small and large joints). We also stratified these groups by 1) seropositivity (RF or anti-CCP positive) and 2) elevated CRP. Results By July 2018, 1866 participants had completed the study questionnaire and provided a blood sample. Of those 870 (47%) returned the SPARRA questionnaire and in this subgroup, 43 (5%) were seropositive and 122 (14%) had elevated CRP. The most frequently reported symptoms were sleep problems (20%), joint pain (18%) and fatigue (17%). The proportion with joint stiffness, symmetrical joint pain or small joint pain was higher in the seropositive and elevated CRP groups. This difference was statistically significant in those with elevated CRP, respective difference in proportions (95% CI) of small and symmetrical joint involvement were 10.7 (1.9 to 19.5) and 11.5 (3.2 to 19.8) (Table 1). Conclusion This is the first time the SPARRA questionnaire has been applied in FDRs of patients with RA. Some of the most prevalent symptoms e.g. sleep problems or fatigue, did not identify patterns suggestive of progression to RA. However, the distribution of joint involvement (symmetrical, small joint pain), was in keeping with RA features. This part of the questionnaire may be useful in identifying individuals most likely to develop RA. Disclosure of Interests Ruth Costello: None declared, Mohammad Haris: None declared, Jenny Humphreys: None declared, Jamie Sergeant: None declared, Fiona Stirling: None declared, Karim Raza: None declared, Dirkjan van Schaardenburg: None declared, Ian N. Bruce Grant/research support from: Genzyme Sanofi, GlaxoSmithKline, Consultant for: AstraZeneca, Eli Lilly, GlaxoSmithKline, ILTOO Pharma, MedImmune, Merck Serono, Speakers bureau: GlaxoSmithKline, UCB Pharma

Objective Patients with systemic lupus erythematosus (SLE) have an increased risk of cardiovascular disease (CVD) and impaired endothelial repair. Although vitamin D deficiency is associated with increased CVD risk in the general population, a causal relationship has not been demonstrated. We aimed to determine whether vitamin D deficiency directly modulates endothelial dysfunction and immune responses in a murine model of SLE. Methods Vitamin D deficiency was induced in lupus-prone MRL/lpr mice by dietary restriction for 6 weeks. Endothelium-dependent vasorelaxation was quantified using aortic ring myography, and endothelial repair mechanisms were assessed by evaluating the phenotype and function of bone marrow endothelial progenitor cells (EPCs) and with the use of an in vivo Matrigel plug model. Lupus disease activity was determined by evaluating expression of interferon-stimulated genes (ISGs) in splenic tissue, positivity for serum autoantibodies, and renal histology. To validate the findings, expression of ISGs was also measured in whole blood from vitamin D–deficient and vitamin D–sufficient patients with SLE. Results Vitamin D deficiency resulted in impaired endothelium-dependent vasorelaxation and decreases in neoangiogenesis without a change in the total number of EPCs. There were no differences in anti–double-stranded DNA titers, proteinuria, or glomerulonephritis (activity or chronicity) between vitamin D–deficient or sufficient mice. Vitamin D deficiency was associated with a trend toward increased ISG expression both in mice and in patients with SLE. Conclusion These findings indicate that vitamin D deficiency is associated with hampered vascular repair and reduced endothelial function, and may modulate type I interferon responses.

Introduction In recent years there has been a rise in the number of trauma and orthopaedics trainees working on full shift patterns. Historically, most trauma and orthopaedics trainees worked 24 hours non-resident on-call shifts. The effect of this change in shift patterns has not previously been measured. As two trusts (one trauma unit, one major trauma centre) in our region underwent a change to full shift working, we assessed the impact on the trainees’ operating experience. Methods Fifty-five logbooks were analysed across the two trusts over a two-year period, with comparisons made between pre- and post-shift working. Results Overall operating fell by 13% for trainees working full shift patterns, which was statistically significant. There was a loss of elective operating of 15% at the trauma unit and 32% at the major trauma centre for trainees doing shift work. The effect on trauma operating opportunities was mixed. Index operating was largely preserved. Conclusions Shift working significantly impacts on surgical training opportunities. We explore approaches to minimising this effect.

Background Methotrexate (MTX) remains the disease-modifying anti-rheumatic drug of first choice in rheumatoid arthritis (RA) but response varies. Predicting non-response to MTX could enable earlier access to alternative or additional medications and control of disease progression. We aimed to identify baseline predictors of non-response to MTX and combine these into a prediction algorithm. Methods This study included patients recruited to the Rheumatoid Arthritis Medication Study (RAMS), a UK multi-centre prospective observational study of patients with RA or undifferentiated polyarthritis, commencing MTX for the first time. Non-response to MTX at 6 months was defined as “no response” using the European League Against Rheumatism (EULAR) response criteria, discontinuation of MTX due to inefficacy or starting biologic therapy. The association of baseline demographic, clinical and psychosocial predictors with non-response was assessed using logistic regression. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots. Results Of 1050 patients, 449 (43%) were classified as non-responders. Independent multivariable predictors of MTX non-response (OR (95% CI)) were rheumatoid factor (RF) negativity (0.62 (0.45, 0.86) for RF positivity versus negativity), higher Health Assessment Questionnaire score (1.64 (1.25, 2.15)), higher tender joint count (1.06 (1.02, 1.10)), lower Disease Activity score in 28 joints (0.29 (0.23, 0.39)) and higher Hospital Anxiety and Depression Scale anxiety score (1.07 (1.03, 1.12)). The optimism-corrected AUC was 0.74. Conclusions This is the first model for MTX non-response to be developed in a large contemporary study of patients commencing MTX in which demographic, clinical and psychosocial predictors were considered. Patient anxiety was a predictor of non-response and could be addressed at treatment commencement. Electronic supplementary material The online version of this article (10.1186/s13075-018-1645-5) contains supplementary material, which is available to authorized users.

Background Rapid acute pain exacerbations, colloquially called pain flares, affect quality of life and are a key driver for patients to seek healthcare. There is no standardised definition of pain flare.1 Daily collection of patient-reported symptoms with mobile technology enables monitoring pain flares in real-time. The choice of definition may identify different people and patterns of pain severity. If remote monitoring of flares is to inform clinical practice and research, it is important to understand the implications of these choices. Objectives Investigate the frequency of pain flares from daily pain symptoms under various definitions in a population with chronic pain. Methods Participants with chronic (≥3 months) musculoskeletal pain in the smartphone study Cloudy with a Chance of Pain2 reported daily pain severity and impact of pain on a 5-point scale. Pain flares were defined in five ways: 1. Worse than average: pain severity higher than personal median 2. Above threshold: pain value 4 or 5 3. Move to above threshold: pain value 1 or 2 or 3 yesterday to 4 or 5 today 4. Absolute change: 2-point increase in pain since yesterday 5. Composite: 2-point increase in pain severity since yesterday and impact 4 or 5 Daily pain-flare rate was calculated by dividing the number of pain flares by the number of days a pain flare would have been possible, hereafter called at-risk days (def. 1+2: total days of symptom entry; def 3 to 5: days of data entry for which participant also entered data on preceding day). Monthly pain-flare rates per person were calculated by multiplying the rate by 30. Results The study smartphone app was downloaded by 13 256 people. After excluding people that never reported pain severity (n=2020), did not complete the baseline questionnaire (n=947), stayed in the study for less than 7 days (n=3418), and reported non-musculoskeletal chronic pain (n=728), 6143 were eligible for analysis. Table 1 shows that the portion of eligible people with at least one pain flare varies by definition, with 42% reporting at least one pain flare according to the most restrictive classification criterion. Depending on the criterion used, the monthly pain-flare rate per ranges from 0.9 to 8.7. Under the ‘worse than average’ and ‘above threshold’ definition, most participants have more than 10 pain flares per month (figure 1). Under the two most stringent definitions, most participants have between 0 and 3 pain flares per month. Conclusions The five plausible definitions for a pain flare are demonstrated to generate quite different flare rates through time. Pain flares in people with chronic musculoskeletal pain, however, remain common even as classification criteria become increasingly strict. As daily data collection of patient-generated data becomes possible, careful thought must be given to flares should best be defined for clinical practice and research. References [1] Gossec L, et al. doi:10.1136/annrheumdis-2015-208593 [2] Druce KL, et al. doi:10.2196/mhealth.8162 Disclosure of Interest None declared

Background Patients with SLE display marked clinical and immunlogical heterogeneity. The purpose of the study was to investigate patterns of serum cytokines in patients with active and stable systemic lupus erythematosus (SLE) and to determine how they relate to clinical phenotype. Methods Serum levels of 10 cytokines were measured retrospectively in a cohort of patients with SLE and in healthy controls using a high-sensitivity multiplex bead array. Disease activity was determined using the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) and British Isles Lupus Assessment Group (BILAG-2004) indices. Logistic regression models were used to determine the association between cytokine levels and active SLE. Principal component analysis (PCA) and cluster analysis was then used to identify subgroups of patients on the basis of cytokine levels. Results Serum chemokine (C-X-C motif) ligand 10 (CXCL10) and CXCL13 were significantly higher in patients with SLE compared to healthy controls. Two cytokines (pentraxin-related protein (PTX3) and CXCL10) were significantly higher in patients with active disease after adjustment for potential confounding factors. Measurement of four cytokines (CXCL10, IL-10, IL-21 and PTX3) significantly improved the performance of a model to identify patients with clinically active disease. Cluster analysis revealed that the patients formed 3 distinct groups, characterised by higher levels of interferon alpha (IFNα) and B lymphocyte stimulator (BLyS) (group 1), increased CXCL10 and CXCL13 (group 2) or low levels of cytokines (group 3). Group 2 had significantly lower serum complement and higher anti-double-stranded DNA antibodies and increased prevalence of inflammatory arthritis. Conclusions Multiplex analysis has identified a serum cytokine signature for active SLE. Within the SLE population distinct cytokine subgroups were identified, with differing clinical and immunological phenotypes that appeared stable over time. Assessment of cytokine profiles may reveal unique insights into disease heterogeneity. Electronic supplementary material The online version of this article (10.1186/s13075-018-1666-0) contains supplementary material, which is available to authorized users.

Background High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. Methods Women participating in the “Predicting Risk Of Cancer At Screening” (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. Results The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72–7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56–3.78), 2.17 (95% CI 1.41–3.33) and 2.12 (95% CI 1.30–3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67–1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79–7.18), 2.87 (95% CI 1.77–4.64) and 2.34 (95% CI 1.50–3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85–2.05). Conclusions Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification. Electronic supplementary material The online version of this article (10.1186/s13058-018-0932-z) contains supplementary material, which is available to authorized users.

Objective.Several global measures to assess at-work productivity loss or presenteeism in patients with rheumatic diseases have been proposed, but the comparative validity is hampered by the lack of data on test-retest reliability and comparative concurrent and construct validity. Our objective was to test-retest 5 global measures of presenteeism and to compare the association between these scales and health-related well-being.Methods.Sixty-five participants with inflammatory arthritis or osteoarthritis in paid employment were recruited from 7 countries (UK, Canada, Netherlands, France, Sweden, Romania, and Italy). At baseline and 2 weeks later, 5 global measures of presenteeism were evaluated: the Work Productivity Scale–Rheumatoid Arthritis (WPS-RA), Work Productivity and Activity Impairment Questionnaire (WPAI), Work Ability Index (WAI), Quality and Quantity questionnaire (QQ), and the WHO Health and Performance Questionnaire (HPQ). Agreement between the 2 timepoints was assessed using single-measure intraclass correlations (ICC) and correlated between each other and with visual analog scale general well-being scores at followup by Spearman correlation.Results.ICC between measures ranged from fair (HPQ 0.59) to excellent (WPS-RA 0.78). Spearman correlations between measures were moderate (Qquality vs WAI, r = 0.51) to strong (WPS-RA vs WPAI, r = 0.88). Correlations between measures and general well-being were low to moderate, ranging from −0.44 ≤ r ≤ 0.66.Conclusion.Test-retest results of 4 out of 5 global measures were good, and the correlations between these were moderate. The latter probably reflect differences in the concepts, recall periods, and references used in the measures, which implies that some measures are probably not interchangeable.

Body: Introduction In the IBIS I Prevention Trial we demonstrated that tamoxifen prevented breast cancer in women with greater than 9 % absolute reduction in visually assessed mammographic density over a 12-18 month period as assessed by an expert radiologist, RW (1). In this study we investigated: whether RW obtained similar results in a different group of women treated with tamoxifen; how other expert mammographic film readers with experience of density assessment (radiologists, advanced practitioner radiographers and a breast physician) estimated mammographic change; how visual change relates to change in automated measures of volumetric density; and whether we can predict change by serum markers or SNPs. Methods 105 women aged 33 to 46 at greater than 1 in 6 lifetime breast cancer risk completed one year’s treatment with tamoxifen. RW assessed mammographic percentage density in increments of 5% for baseline and one year mammograms. Two or three of a pool of eight readers estimated % density using a visual analogue scale (VAS). Percent change was also estimated using a computer-assisted thresholding technique (Cumulus). Change in volume of dense tissue (Quantra™ & Volpara™) was measured. Changes in lipids, IGF1, insulin and relevant SNPs were measured to determine whether they predicted density change. Results. Estimates of change obtained by RW were broadly consistent with those from our previous study, as were those from Cumulus, but could not be replicated using estimates from the pooled results of VAS readers. Tamoxifen was associated with marked reductions of dense volume of the breast (Table 1) However the different methods were at best only moderately correlated with RW (r less than 0.6). Change in serum triglycerides significantly predicted density change as measured by RW but none of the other serum measures or SNPs did. Table 1 Numbers of women and percentage change in density measures after one year of tamoxifen% Density changeRW %changeOther radiologists %changeCumulus %change_____% Dense volume change^Quantra %changeVolpara %change40+001 40+15930+503 30+141020+12013 20+202010+35630 10+17260+498143 0+16190-4188 0-1615Number10510598 9899RW = Ruth Warren. CUMULUS was measured by JS, a trained operator of the software. * Percentage point change eg if density changed from 40% to 30% this was regarded as a 10% change.^ Reduction in dense volume expressed as a percentage of baseline dense volume. We used Quantra version 2.0 and Volpara version 1.4.5 Conclusions Whilst RW remained consistent with previous density estimation it was not possible to use a pool of other expert readers to predict change. Cumulus gave similar results to RW but is difficult to use in practice. There was good agreement between the two objective volumetric measures used (r=0.5) and since these are automated they may be suitable for clinical practice. However their relationship to the long term breast cancer preventative effect of tamoxifen needs to be established. Reference 1 Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case-control study. Cuzick J, Warwick J, Pinney E, Duffy JW, Cawthorn S, Howell A, Forbes JF, Warren RM. J Natl Cancer Inst. 2011 May 4; 103 (9): 744-52. Citation Format: Anthony Howell, Sue Astley, Elaine Harkness, Julia Wiseman, Jill Fox, Paula Stavrinos, Mary Wilson, Yit Lim, Valerie Reece, Ursula Beetles, Anil Jain, Jamie Sergeant, Jack Cuzick, Ruth Warren, Gareth Evans. Predicting the effect of tamoxifen on the breast: Change in measures of breast density, serum markers and SNPs [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P5-12-01.

Background: Ovarian suppression in premenopausal women is known to reduce breast cancer risk. This study aimed to assess uptake and compliance with ovarian suppression using the luteinizing hormone releasing hormone (LHRH) analogue, goserelin, with add-back raloxifene, as a potential regimen for breast cancer prevention. Methods: Women at ≥30% lifetime risk breast cancer were approached and randomized to mammographic screening alone (C-Control) or screening in addition to monthly subcutaneous injections of 3.6 mg goserelin and continuous 60 mg raloxifene daily orally (T-Treated) for 2 years. The primary endpoint was therapy adherence. Secondary endpoints were toxicity/quality of life, change in bone density, and mammographic density. Results: A total of 75/950 (7.9%) women approached agreed to randomization. In the T-arm, 20 of 38 (52%) of women completed the 2-year period of study compared with the C-arm (27/37, 73.0%). Dropouts were related to toxicity but also the wish to have established risk-reducing procedures and proven chemoprevention. As relatively few women completed the study, data are limited, but those in the T-arm reported significant increases in toxicity and sexual problems, no change in anxiety, and less cancer worry. Lumbar spine bone density declined by 7.0% and visually assessed mammographic density by 4.7% over the 2-year treatment period. Conclusions: Uptake is somewhat lower than comparable studies with tamoxifen for prevention with higher dropout rates. Raloxifene may preserve bone density, but reduction in mammographic density reversed after treatment was completed. Impact: This study indicates that breast cancer risk reduction may be possible using LHRH agonists, but reducing toxicity and preventing bone changes would make this a more attractive option. Cancer Epidemiol Biomarkers Prev; 27(1); 58–66. ©2017 AACR.

ObjectivesTo identify predictors of remission and disease activity patterns in patients with rheumatoid arthritis (RA) using individual participant data (IPD) from clinical trials.MethodsPhase II and III clinical trials completed between 2002 and 2012 were identified by systematic literature review and contact with UK market authorisation holders. Anonymised baseline and follow-up IPD from non-biological arms were amalgamated. Multiple imputation was used to handle missing outcome and covariate information. Random effects logistic regression was used to identify predictors of remission, measured by the Disease Activity Score 28 (DAS28) at 6 months. Novel latent class mixed models characterised DAS28 over time.ResultsIPD of 3290 participants from 18 trials were included. Of these participants, 92% received methotrexate (MTX). Remission rates were estimated at 8.4%(95%CI 7.4%to9.5%) overall, 17%(95%CI 14.8%to19.4%) for MTX-naïve patients with early RA and 3.2% (95% CI 2.4% to 4.3%) for those with prior MTX exposure at entry. In prior MTX-exposed patients, lower baseline DAS28 and MTX reinitiation were associated with remission. In MTX-naïve patients, being young, white, male, with better functional and mental health, lower baseline DAS28 and receiving concomitant glucocorticoids were associated with remission. Three DAS28 trajectory subpopulations were identified in MTX-naïve and MTX-exposed patients. A number of variables were associated with subpopulation membership and DAS28 levels within subpopulations.Conclusions Predictors of remission differed between MTX-naïve and prior MTX-exposed patients at entry. Latent class mixed models supported differential non-biological therapy response, with three distinct trajectories observed in both MTX-naïve and MTX-exposed patients. Findings should be useful when designing future RA trials and interpreting results of biomarker studies.

Background: Medical screening and load monitoring procedures are commonly used in professional football to assess factors perceived to be associated with injury.Objectives: To identify prognostic factors (PFs) and models for lower extremity and spinal musculoskeletal injuries in professional/elite football players from medical screening and training load monitoring processes.Methods: The MEDLINE, AMED, EMBASE, CINAHL Plus, SPORTDiscus and PubMed electronic bibliographic databases were searched (from inception to January 2017). Prospective and retrospective cohort studies of lower extremity and spinal musculoskeletal injury incidence in professional/elite football players aged between 16 and 40 years were included. The Quality in Prognostic Studies appraisal tool and the modified Grading of Recommendations Assessment, Development and Evaluation synthesis approach was used to assess the quality of the evidence.Results: Fourteen studies were included. 16 specific lower extremity injury outcomes were identified. No spinal injury outcomes were identified. Meta-analysis was not possible due to heterogeneity and study quality. All evidence related to PFs and specific lower extremity injury outcomes was of very low to low quality. On the few occasions where multiple studies could be used to compare PFs and outcomes, only two factors demonstrated consensus. A history of previous hamstring injuries (HSI) and increasing age may be prognostic for future HSI in male players.Conclusions: The assumed ability of medical screening tests to predict specific musculoskeletal injuries is not supported by the current evidence. Screening procedures should currently be considered as benchmarks of function or performance only. The prognostic value of load monitoring modalities is unknown.

The aim of this survey was to explore knee surgeons’ opinions and practices regarding the management of symptomatic knee osteoarthritis in obesity. Questionnaires were sent electronically to all consultant members of the British Association for Surgery of the Knee. The response rate was 52%. The survey showed considerable practice variation and divided opinion on the evidence base. The majority stated that weight loss should be the first-line treatment. 53% (91/172) stated that such cases should ideally be assessed by a specialist multidisciplinary service; however, only 24% (41/169) would be interested in being the orthopaedic surgeon in such a service. The optimal pathway of care for the obese patient with symptomatic knee osteoarthritis remains unclear. Given recent debate around the rationing of knee arthroplasty surgery in obesity, we felt it was timely to survey knee surgeons’ current practice. Our survey has shown considerable variation in the opinions and practice of surgeons on the management of symptomatic knee osteoarthritis in obesity, together with divided views on current literature.

Background Smartphones can facilitate collection of temporally-rich self-reported data, and have proven to enable large recruitment. However, their viability to support epidemiological research is uncertain due to concerns about selection bias and unsustained engagement. Objectives To examine the characteristics and engagement of participants in the first six months of Cloudy with a Chance of Pain, a UK smartphone-based study investigating the link between the weather and chronic pain. Methods Between 20th of January and 29th of February 2016, we recruited UK residents 17 years or older with chronic pain (≥3 months) who owned a smartphone. Participants received prompts from an app developed by uMotif, which they used to daily report the severity of ten pain-related symptoms. Of those who enrolled, those eligible for analysis provided sufficient baseline data to confirm they were ≥17 years old, and at least one symptom. The characteristics of those who were eligible were examined. Engagement per day was defined based on whether participants had completed any of the ten symptoms. Participants were then clustered by their engagement over time using a first-order hidden Markov models. Participant characteristics were then compared between the clusters. Results Of 7972 people who registered to participate, 6370 were eligible. 81% of participants were female, with a mean age of 49 years (SD 12.9). The most common diagnosis was arthritis (40% type unspecified, 19% rheumatoid arthritis), followed by fibromyalgia/chronic widespread pain (24%) and “other pain diagnosis” (23%). We identified four clusters of engagement: high (14%), moderate (22%), low (39%) and tourists (25%). Median days of data entry ranged from 1 (1–1) to 175 (IQR: 152–177) for the tourist and high engagement clusters respectively. Those in the high and moderate clusters (n=2249, 35%) engaged on at least 50% of days in the study (high: 79%; moderate: 50%). Highly engaged participants were older (median 56 (47–63)) when compared to those who were low engagers (47 (39–57)) or tourists (49 (40–58)). A lower proportion of tourists were women (76% (95% CI: 74–78), than in any other cluster (high: 82% (80–85), moderate: 84% (82–86), low: 81% (79–82)). Conclusions Cloudy with a Chance of Pain recruited a large sample of people with chronic pain, of whom over one in three participants engaged in smartphone-based symptom reporting for at least 50% of days in the first six months. Smartphone studies enable quick mass participation with sustained daily data entry, providing unprecedented volumes of daily data. While there may be selection bias towards older females in our study, younger men are also less likely to participate in studies using traditional data collection methods. Our study suggests that smartphones could provide a viable alternative to traditional data collection methods. Disclosure of Interest None declared

Background People with chronic pain commonly believe that their pain is affected by the weather. Despite a century9s worth of research, there is no scientific consensus on the existence of a relationship between weather and chronic pain. Objectives A systematic literature review to (1) gain an overview of existing research on the weather-pain relationship, and (2) summarise the methodologies, methodological rigour and risk of bias in published studies of patients with musculoskeletal conditions. Methods Systematic search strategies were developed for 5 databases (Medline, Embase, PsycInfo, Scopus, Web of Science) to retrieve studies that investigated the relationship between weather conditions and chronic pain. Original articles describing observational studies that related chronic pain (primary outcome) to weather conditions (exposure), were included. Study characteristics, methodology (e.g. measurements of pain and weather, aspects of statistical analysis) and data on risk of bias (validation of participants9 exposure and diagnosis, coverage of weather variation, correction for confounders) was extracted. Methodological rigour was summarised by mapping the methodological variability among studies and ranking these from least to most rigorous. Results The searches returned 16,081 articles. After removing 3090 duplicates and excluding 12726 articles during title screening, 265 abstracts were assessed for eligibility. Of 64 observational studies that met the inclusion criteria, 30 (47%) investigated pain associated with musculoskeletal conditions, 24 (38%) investigated headache and 10 (16%) investigated pain associated with other conditions such as sickle cell disease and dental pain. After full text assessment, 10 of 30 papers on musculoskeletal conditions were excluded because they investigated risk of acute pain episodes rather than chronic pain symptoms (n=6), or investigated multiple conditions (n=4). The 20 included studies investigated rheumatoid arthritis (6, 30%), osteoarthritis (6, 30%), fibromyalgia (6, 30%) and low back pain (2, 10%). A total of 15 studies (75%) reported some effect of weather on chronic pain, and this was clinically significant (consistent and sufficient size of effect) in 6. Participant numbers varied from 19 to 2491. 1 study was cross-sectional and 19 longitudinal with follow ups ranging from two weeks to 3.5 years, with participants scoring their pain once to daily for 12 months. Pain was measured with a Visual Analog Scale or Numeric Rating Scale in 17 (85%). Weather conditions were retrieved from local weather stations, with most studies assuming that participants stayed in the area/city (65%) or postcode area (20%) where they lived. In 12 (60%) studies, participants were blinded to the study hypothesis. Methods for correcting for confounders were highly variable, with 7 studies not addressing any, and the remainder addressing one or more of 32 confounding variables. Conclusions Methodological variability of studies investigating the relationship between weather and chronic musculoskeletal pain is high. This methodological review will inform best research practice for those investigating the relationship between the weather and chronic pain. Acknowledgements Mary Ingram, librarian Disclosure of Interest None declared

Objectives To identify prognostic factors and models for spinal and lower extremity injuries in adult professional/elite football players from medical screening and training load monitoring processes. Methods The MEDLINE, AMED, EMBASE, CINAHL Plus, SPORTDiscus electronic bibliographic databases and the Cochrane Database of Systematic Reviews were searched from inception to July 2016. Searches were limited to original research, published in peer reviewed journals of any language. The Quality in Prognostic Studies (QUIPS) tool was used for appraisal and the modified GRADE approach was used for synthesis. Prospective and retrospective cohort study designs of spinal and lower extremity injury incidence were found from populations of adult professional/elite football players, between 16 and 40 years. Non-football or mixed sports were excluded. Results 858 manuscripts were identified. Removing duplications left 551 studies, which were screened for eligibility by title and abstract. Of these, 531 studies were not eligible and were excluded. The full text of the remaining 20 studies were obtained; a further 10 studies were excluded. 10 studies were included for appraisal and analysis, for 3344 participants. Conclusions Due to the paucity and heterogeneity of the literature, and shortcomings in methodology and reporting, the evidence is of very low or low quality and therefore cannot be deemed robust enough to suggest conclusive prognostic factors for all lower limb musculoskeletal injury outcomes identified. No studies were identified that examined spinal injury outcomes or prognostic models.

Previous studies of glucocorticoid (GC) therapy and mortality have had inconsistent results and have not considered possible perimortal bias—a type of protopathic bias where illness in the latter stages of life influences GC exposure, and might affect the observed relationship between GC use and death. This study aimed to investigate all-cause and cause-specific mortality in association with GC therapy in patients with rheumatoid arthritis (RA), and explore possible perimortal bias. A retrospective cohort study using the primary care electronic medical records. Oral GC exposure was identified from prescriptions. Mortality data were obtained from the UK Office for National Statistics. Multivariable Cox proportional hazards regression models assessed the association between GC use models and death. Several methods to explore perimortal bias were examined. The cohort included 16,762 patients. For ever GC use there was an adjusted hazard ratio for all-cause mortality of 1.97 (95 % CI 1.81–2.15). Current GC dose of below 5 mg per day (prednisolone equivalent dose) was not associated with an increased risk of death, but a dose–response association was seen for higher dose categories. The association between ever GC use and all-cause mortality was partly explained by perimortal bias. GC therapy was associated with an increased risk of mortality for all specific causes considered, albeit to a lesser extent for cardiovascular causes. GC use was associated with an increased risk of death in RA, at least partially explained by perimortal bias. Importantly, GC doses below 5 mg were not associated with an increased risk of death. Electronic supplementary material The online version of this article (doi:10.1007/s10654-016-0167-1) contains supplementary material, which is available to authorized users.

Background: Observational studies indicate that oophorectomy at about age 40 reduces breast cancer risk by approximately a half in high risk women. Widespread use of risk reducing oophorectomy is unlikely to be acceptable to these women. We explored the feasibility of giving goserelin to produce reversible ovarian suppression together with raloxifene to maintain bone mineral density (BMD). Objectives: The primary study objective was adherence to treatment. Secondary objectives were uptake of randomisation, side effects/quality of life and measures of effect on bone and in serum. Methods: Recruitment was from 3 UK Family History Clinics. Consenting women at ≥ 1 in 3 lifetime risk of breast cancer were randomised to control or monthly subcutaneous goserelin 3.6 mg and raloxifene 60 mg/d orally for two years. Questionnaires (Endocrine Symptom Checklist, Trait & State Anxiety, Sexual Activity & Cancer Worry) measuring toxicity/quality of life were administered by nurses. Dual energy X-ray absorptiometry (DXA) BMD measurements were performed in the treatment arm annually. Lipids and collagen breakdown products were measured by standard methods. Results: 75 of 511 (14.7%) women approached agreed to randomisation (38 to treatment and 37 to control). The major reason for non-entry was fear of side effects (85%). Median age was 37 and 35 years, for the experimental (A) and control arm (B), respectively. Median follow up is 8.8 years. 20/38 in arm A and 27/37 of controls completed the 24 m study. 18/38 women in arm A withdrew (13 [34%] because of side effects) and 10/37 in arm B for various reasons including the desire for risk reducing surgery (n = 4). No significant differences were seen in the Endocrine Symptom Sub-scale, State or Trait anxiety or Cancer Worry. However, Hot flushes, night and cold sweats (together p Conclusions: Uptake and adherence to treatment was relatively low in this group of women at high risk. The major reason for low uptake was fear of side effects and these were the major reason for drop out from treatment. Raloxifene did not maintain BMD. This approach to breast cancer prevention induced significant symptoms and bone loss, thus methods to ameliorate these need to be developed if ovarian suppression is to play a role in breast cancer prevention. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P1-09-05.

Breast cancer is not only increasing in the west but also particularly rapidly in eastern countries where traditionally the incidence has been low. The rise in incidence is mainly related to changes in reproductive patterns and lifestyle. These trends could potentially be reversed by defining women at greatest risk and offering appropriate preventive measures. A model for this approach was the establishment of Family History Clinics (FHCs), which have resulted in improved survival in younger women at high risk. New predictive models of risk that include reproductive and lifestyle factors, mammographic density and measurement of risk-associated single nucleotide polymorphisms (SNPs) may give more precise information concerning risk and enable better targeting for mammographic screening programmes and of preventive measures. Endocrine prevention using anti-oestrogens and aromatase inhibitors is effective, and observational studies suggest lifestyle modification may also be effective. However, referral to FHCs is opportunistic and predominantly includes younger women. A better approach for identifying older women at risk may be to use national breast screening programmes. Here were described pilot studies to assess whether the routine assessment of breast cancer risk is feasible within a population-based screening programme, whether the feedback and advice on risk-reducing interventions would be welcomed and taken up, and to consider whether the screening interval should be modified according to breast cancer risk.

Background: Currently there are no real attempts internationally to tailor breast screening programmes to individual risk Methods: We have assessed the feasibility of collecting breast cancer risk information during routine mammographic screening in the National Health Service Breast Screening Programme (NHSBSP) in England, in order to consider, ultimately, adapting the screening interval to risk of breast cancer and introducing preventive strategies in women at high risk. The study Predicting Risk Of Cancer At Screening (PROCAS) aims to recruit 60,000 women over 3 years. Results: 26,000 women (June 8th 2011) have so far given consent to join the study. Thirty six percent of the first 20,000 women in nineteen screening sites in Manchester consented to enter the study and completed a risk factor questionnaire. The median 10 year breast cancer risk was 2.65%, with 926 (9.26%) of the first 10,000 women having a 10 year risk of ≥5% and 92 (0.92%) having a 10 year risk of ≥8% (Tyrer-Cuzick), IQR:1.35. 832 (8.32%) women had a mammographic density of 60% or greater (Visual Analogue Scale). We collected saliva samples from 1019 women for genetic analysis and will extend this to 18% of participants. Of those who agreed to participate in the study, 94% indicated that they wished to know their breast cancer risk. Women with a 10-year risk of ≥8%, and women with a 10-year risk of ≥5% and mammographic density ≥60% were invited to attend or be telephoned to be counselled. To date 138 have accepted with 135, so far, having received risk counselling. Nineteen percent of the high-risk women identified subsequently decided to enter a randomised breast cancer prevention study with either a dietary or drug intervention (IBIS2, anastrazole vs placebo). Results from the first 1,000 women who provided DNA samples suggest that the risk information from the 18 validated SNPS may enhance existing risk models. Conclusion: This study demonstrates that it is feasible to determine individual breast cancer risk and offer women appropriate risk-reducing interventions within the context of a population-based mammographic screening programme. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P4-11-07.