Search

Objectives To examine associations between PsA and psoriasis vs lifestyle factors and comorbidities by triangulating observational and genetic evidence. Methods We analysed cross-sectional data from the UK Biobank (1836 PsA, 8995 psoriasis, 36 000 controls) to describe the association between psoriatic disease and lifestyle factors (including BMI and smoking) and 15 comorbidities [including diabetes and coronary artery disease (CAD)] using logistic models adjusted for age, sex and lifestyle factors. We applied bidirectional Mendelian randomization (MR) to genome-wide association data (3609 PsA and 7804 psoriasis cases, up to 1.2 million individuals for lifestyle factors and 757 601 for comorbidities) to examine causal direction, using the inverse-variance weighted method. Results BMI was cross-sectionally associated with risk of PsA (OR 1.31 per 5 kg/m2 increase; 95% CI 1.26, 1.37) and psoriasis (OR 1.23; 1.20, 1.26), with consistent MR estimates (PsA OR 1.38; 1.14, 1.67; psoriasis OR 1.36; 1.18, 1.58). In both designs, smoking was more strongly associated with psoriasis than PsA. PsA and psoriasis were cross-sectionally associated with diabetes (OR 1.35 and 1.39, respectively) and CAD (OR 1.56 and 1.38, respective). Genetically predicted glycated haemoglobin (surrogate for diabetes) increased PsA risk (OR 1.18 per 6.7 mmol/mol increase; 1.02, 1.36) but not psoriasis. Genetic liability to PsA (OR 1.05; 1.003, 1.09) and psoriasis (OR 1.03; 1.001, 1.06) were associated with increased risk of CAD. Conclusion Observational and genetic evidence converge to suggest that BMI and glycaemic control are associated with increased psoriatic disease risk, while psoriatic disease is associated with increased CAD risk. Further research is needed to understand the mechanism of these associations.

IntroductionThis study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis.MethodsWe considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring.ResultsDiscrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual-outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors.DiscussionWe recommend the dual-outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.

Sensitivity analysis.

NICE guidelines.

In the United Kingdom, women at moderate and high risk of breast cancer between the ages of 40 and 49 years are eligible for annual mammographic screening and preventive therapy with tamoxifen. Here, we estimate the numbers of women in a population eligible for this service and the proportion of breast cancers detected in this group compared with the whole population. Women N = 384), but the numbers of cancers in this group were not appreciably increased (N = 8). Systematic assessment of family history in primary care or through population-based screening will identify appreciable numbers of women in their forties, eligible for additional surveillance and chemoprevention Cancer Prev Res; 7(10); 993–1001. ©2014 AACR.

Background/Aims As recommended by the NICE guidelines, methotrexate (MTX) is typically prescribed as the first line therapy for patients with rheumatoid arthritis (RA). However, around 40% of patients do not respond to MTX at 6 months and around 80% experience adverse events (AEs). Our previous systematic review identified that clinical prediction models of MTX outcomes suffered from methodological limitations, including a lack of validation, suboptimal handling of missing data, and no consideration of competing risks, such as patients discontinuing due to adverse events (AEs). These shortcomings resulted in high risk of bias and should be addressed to aid the implementation of outcome prediction in clinical practice. Therefore, this study aimed to (i) develop a multinomial prediction model for estimating an individual’s risks of not achieving low disease activity (LDA) and discontinuing due to AEs at 6 months after starting MTX, using information observable at the starting point of treatment, (ii) update prognosis at 3 months, and (iii) internally validate these models to assess performance. Methods Data came from the national multi-centre (n = 38) Rheumatoid Arthritis Medication Study (RAMS), which recruited patients with a diagnosis of RA or undifferentiated polyarthritis starting MTX for the first time. We conducted a Patient and Public Involvement focus group with past and current MTX users, which informed our outcome choice of LDA (DAS28 ≤3.2). A multinomial logistic regression (MLR) was used to develop a prediction model that estimated the probabilities of three outcomes: 1) not in LDA, 2) achieved LDA, and 3) discontinued due to AEs. The updated model at 3 months was conditional on patients not having achieved LDA. Missing data was handled using multiple imputation and models were internally validated through bootstrapping. Calibration, discrimination, and variance explained was assessed to quantify model performance. Results A total of 1632 patients were included in the analysis. At 6 months, 756 (46%) patients achieved LDA, 730 (45%) were not in LDA, and 146 (9%) discontinued due to AEs. The updated model at 3 months included 1179 (72%) patients as they had not achieved LDA. For the LDA outcome pair, the c-statistic was 0.73 (0.70, 0.75), while it was 0.54 (0.49, 0.59) for the AEs outcome. The calibration slope was 0.99 (0.85, 1.12) and 0.74 (0.23, 1.23), respectively. Predictive performance at 3 months was similar to baseline. Conclusion Our MLR models handled outcomes of disease activity and AEs simultaneously. We were able to predict outcomes of LDA more accurately than AEs and performance was better in terms of calibration, which is crucial for the clinical utility of a prediction model. This modelling approach could provide more clinically useful and realistic predictions of MTX outcomes, as competing risks such as AEs have previously been ignored. Disclosure C.K. Gehringer: None. G.P. Martin: None. K.L. Hyrich: Honoraria; Abbvie. Grants/research support; BMS and Pfizer. S.M.M. Verstappen: None. J.C. Sergeant: None.

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.

Supplementary Table 1. DXA scan measurements (Baseline, 1-yr and 2-yr.)

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.

In the management of rheumatoid arthritis (RA), there is a clinical need to identify which patients are at high-risk of not responding to methotrexate (MTX), or experiencing adverse events (AEs), to enable earlier alternative treatments. Many clinical prediction models (CPMs) have previously been developed, but a summary of such models and their methodological quality is lacking. This systematic review aimed to (i) identify and summarize previously published CPMs of MTX outcomes in biologic-naïve RA patients, and (ii) critically appraise their methodological properties.Medline and Embase were searched to identify studies developing or validating CPMs of MTX outcomes in RA patients. The risk of bias (ROB) was assessed using PROBAST (prediction model risk of bias assessment tool). A fixed effects meta-analysis summarised discrimination for models with multiple external validations.The systematic review identified 20 CPMs across 13 studies, and 4 validation studies. Three outcome types were used: a state of disease activity (n = 14, 70%); EULAR response criteria (n = 4, 20%); or discontinuation due to AEs (n = 2, 10%). Only one model accounted for potential competing risks, and nine (45%) were internally validated. Eight (40%) models used multiple imputation for missing data, others were often limited to complete case analysis. There was overall high ROB. The meta-analysis summarised c-statistics for two models with multiple external validations was 0.77 (95% CI: 0.69, 0.84) and 0.68 (0.64, 0.71).This review highlights several methodological shortcomings that should be addressed in future model development to increase potential for implementation into practice.

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.

ObjectiveTo assess the feasibility of using smartwatches in people with knee osteoarthritis (OA) to determine the day-to-day variability of pain and the relationship between daily pain and step count.DesignObservational, feasibility study.SettingIn July 2017, the study was advertised in newspapers, magazines and, on social media. Participants had to be living/willing to travel to Manchester. Recruitment was in September 2017 and data collection was completed in January 2018.Participants26 participants aged>50 years with self-diagnosed symptomatic knee OA were recruited.Outcome measuresParticipants were provided with a consumer cellular smartwatch with a bespoke app that triggered a series of daily questions including two times per day questions about level of knee pain and one time per month question from the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire. The smartwatch also recorded daily step counts.ResultsOf the 25 participants, 13 were men and their mean age was 65 years (standard deviation (SD) 8 years). The smartwatch app was successful in simultaneously assessing and recording data on knee pain and step count in real time. Knee pain was categorised into sustained high/low or fluctuating levels, but there was considerable day-to-day variation within these categories. Levels of knee pain in general correlated with pain assessed by KOOS. Those with sustained high/low levels of pain had a similar daily step count average (mean 3754 (SD 2524)/4307 (SD 2992)), but those with fluctuating pain had much lower step count levels (mean 2064 (SD 1716)).ConclusionsSmartwatches can be used to assess pain and physical activity in knee OA. Larger studies may help inform a better understanding of causal links between physical activity patterns and pain. In time, this could inform development of personalised physical activity recommendations for people with knee OA.

BackgroundIn elite football, periodic health examination (PHE) may be useful for injury risk prediction.ObjectiveTo explore whether PHE-derived variables are prognostic factors for indirect muscle injuries (IMIs) in elite players.DesignRetrospective cohort study.SettingAn English Premier League football club.Participants134 outfield elite male players, over 5 seasons (1 July 2013–19 May 2018).Outcome and analysisThe outcome was any time-loss, lower extremity index IMI (I-IMI). Prognostic associations were estimated using odds ratios (ORs) and corresponding statistical significance for 36 variables, derived from univariable and multivariable logistic regression models. Missing data were handled using multiple imputation. Non-linear associations were explored using fractional polynomials.ResultsDuring 317 participant-seasons, 138 I-IMIs were recorded. Univariable associations were determined for previous calf IMI frequency (OR 1.80, 95% 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 12 months but ConclusionPHE has limited use for injury risk prediction. Most variables did not add prognostic value over and above age, other than if a player experienced a hamstring IMI >12 months but Trial registration numberNCT03782389.

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.

SummaryBackgroundClinical environments can be so stressful to medical students as to be detrimental to their learning and well‐being. Our intervention, Clinical Debrief, integrates learning through clinical experience with the development of positive coping strategies. Students shared cases and experiences during weekly small group classroom discussions, facilitated by general practitioners (from outside their current hospital placement), throughout two consecutive 12‐week blocks of their first clinical year. Alongside enquiry‐based and clinical reasoning learning, we gave students a safe space to reflect on their affect. Our aim was to critically examine students’ views in Clinical Debrief.MethodAnonymised quantitative and qualitative evaluation data were collected over 3 years using online questionnaires on completion of each 12‐week block. The data relating to psychological supervision were analysed independently and in parallel, using thematic analysis for qualitative data.We aim to help students develop positive coping mechanisms, promoting empathy,self‐awarenessand wellbeingResultsA total of 1857 evaluations were extracted (response rate 67%). The median (interquartile range) overall rating for Clinical Debrief sessions was 9 (8–10), where 10 indicates ‘excellent’ and 1 indicates ‘significant improvement needed’. The rating for the supervisory aspects of the sessions and free‐text comments were positive. Students appreciated safe environments, the session structure, facilitator role modelling, transitional support and processing of emotional experiences.DiscussionMandatory integrated longitudinal supervision, using trained clinician facilitators, was positively received by students in transition to clinical placements. Normalising the emotional impact of medical work destigmatises distress. Linking clinical reasoning with affective state awareness to contextualise case management, following Mezirow's transformative learning theory, brings added benefit to learning and well‐being. Student demand for the expansion of Clinical Debrief is evidence of success.

Sports medicine clinicians are expected to make accurate diagnoses, estimate prognoses and identify athletes at risk of sustaining an injury.1 These complex decisions are dependent on clinical reasoning, which is informed by, and often biased toward, a practitioner’s scientific knowledge and experience. Clinical prediction models are developed by researchers to help facilitate such decisions in practice2; data for multiple predictor variables are combined to estimate an individual’s risk of a health outcome either being present (diagnosis) or whether it will occur in future (prognosis).3 Despite being employed widely in clinical medicine, clinical prediction models are uncommon in sports medicine. Clinical prediction models can offer benefits to both practitioners and athletes, but only if they are developed and validated using rigorous methods and transparently reported so that potential users can judge their accuracy and usefulness. Therefore, the purpose of this editorial is to describe the recommended steps for clinical prediction development and validation and to guide practitioners using and interpreting prediction models in sports medicine. The first step in developing a prediction model is to identify its clinical need, the target population and how and when it would fit into the clinical workflow. Models should predict outcomes that are relevant to sport stakeholders, and be clearly defined, including how and when assessed.4 Next is to identify any existing models that could be evaluated or updated. If not, …

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.

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