The value of tibial mounted inertial measurement units to quantify running kinetics in elite football (soccer) players. A reliability and agreement study using a research orientated and a clinically orientated system

In elite football, measurement of running kinetics with inertial measurement units (IMUs) may be useful as a component of periodic health examination (PHE). This study determined the reliability of, and agreement between a research orientated IMU and clinically orientated IMU system for initial peak acceleration (IPA) and IPA symmetry index (SI) measurement during running in elite footballers. On consecutive days, 16 participants performed treadmill running at 14kmph and 18kmph. Both IMUs measured IPA and IPA SI concurrently. All measurements had good or excellent within-session reliability (intraclass correlation coefficient (ICC2,1) range = 0.79-0.96, IPA standard error of measurement (SEM) range = 0.19-0.62 g, IPA SI SEM range = 2.50-8.05%). Only the research orientated IMU demonstrated acceptable minimal detectable changes (MDCs) for IPA at 14kmph (range = 7.46-9.80%) and IPA SI at both speeds (range = 6.92-9.21%). Considering both systems, between-session IPA reliability ranged from fair to good (ICC2,1 range = 0.63-0.87, SEM range = 0.51-1.10 g) and poor to fair for IPA SI (ICC2,1 range = 0.32-0.65, SEM range = 8.07-11.18%). All MDCs were >10%. For IPA and SI, the 95% levels of agreement indicated poor between system agreement. Therefore, the use of IMUs to evaluate treadmill running kinetics cannot be recommended in this population as a PHE test to identify prognostic factors for injuries or for rehabilitation purposes

Patient and healthcare professional perspectives on which potential prognostic factors for failure of total elbow replacement should be investigated

Background: Total elbow replacement (TER) is an established treatment for the painful arthritic elbow; however, TER has higher failure rates than other joint replacements, such as hip and knee replacement. Understanding the prognostic factors associated with failure of TER is essential for informed decision-making between patients and clinicians, patient selection, and service planning. The aim of this study is to explore the views of patients and healthcare professionals on which potential prognostic factors should be investigated in relation to TER failure. Methods: This evaluation comprised of two Patient and Public Involvement (PPI) workshops and a survey. PPI workshop 1 consisted of five PPI participants who helped to develop a survey assessing the importance of potential prognostic factors to investigate. The survey was shared electronically with members of the British Elbow and Shoulder Society (BESS) and clinicians internationally. In PPI workshop 2, 15 PPI participants listed factors they thought important to investigate, and 12 completed the survey. Results: Patients and healthcare professionals agreed that most factors in the survey should be investigated. Although this is not a comparative study, more of the healthcare professionals disagreed that ethnicity (49% v 33%) and VTE prophylaxis (42% v none) were important enough to be investigated, whilst more of the patients disagreed that socioeconomic status is important to be investigated (54% v 17%). Patients and healthcare professionals also suggested other factors not listed in the survey. Conclusions: Patients and healthcare professionals agreed on the importance of investigating most prognostic factors, but some factors were favoured by only one group. The results of this evaluation could help researchers decide which prognostic factors to investigate and which to routinely collect

Prognostic factors associated with failure of total elbow replacement: a protocol for analysis of National Joint Registry data in England

Introduction Understanding the prognostic factors associated with the failure of total elbow replacement (TER) is crucial for informing patients about risks and enabling shared decision-making regarding TER as a definitive management option. This protocol outlines the planned analysis of National Joint Registry (NJR) data to investigate prognostic factors for TER failure.Methods and analysis The primary analysis will use the NJR elbow dataset, including all eligible patients who underwent TER surgery between April 2012 and December 2023. To incorporate ethnicity and comorbidities as potential prognostic factors, the NJR will be linked to the National Health Service (NHS) England Hospital Episode Statistics-Admitted Patient Care (HES-APC) data for a secondary analysis. The analysis will adhere to the REporting recommendations for tumour MARKer prognostic studies guidelines. The primary outcome under investigation is TER failure, defined as requiring revision surgery. Initially, the overall prognosis of TER will be examined using unadjusted net implant failure via the Kaplan-Meier method. The list of potential prognostic factors to be investigated in this study has been informed by a systematic review on this topic, input from patient and public involvement and engagement (PPIE) groups and a survey shared with healthcare professionals providing TER services. The relationship between each potential prognostic factor and failure will be assessed using univariable regression methods. Based on the findings from our systematic review, the univariable association will also be adjusted for age, sex and indication for TER surgery using multivariable regression methods. The extent of missing data will be reported, and the reasons for missing data will be explored. A very high degree of data completeness is expected, and a complete case analysis will be performed as the primary analysis. Multiple imputations will be considered as a sensitivity analysis.Ethics and dissemination The NJR research committee approved this analysis, and the NHS Health Research Authority tool guidance dictates that the secondary use of such data for research does not require approval from a research ethics committee. The results from this analysis will be published in a peer-reviewed journal and presented at scientific conferences

A review of the use of propensity score diagnostics in papers published in high-ranking medical journals.

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

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review

Background Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome. Methods We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size. Results A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63–82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A similar number of studies did not meet the ≥ 10EPV criteria (75%, 95% CI: 66–84%). The median deficit of the number of events used to develop a model was 75 [IQR: 234 lower to 7 higher]) which reduced to 63 if the total available data (before any data splitting) was used [IQR:225 lower to 7 higher]. Studies that met the minimum required sample size had a median c-statistic of 0.84 (IQR:0.80 to 0.9) and studies where the minimum sample size was not met had a median c-statistic of 0.83 (IQR: 0.75 to 0.9). Studies that met the ≥ 10 EPP criteria had a median c-statistic of 0.80 (IQR: 0.73 to 0.84). Conclusions Prediction models are often developed with no sample size calculation, as a consequence many are too small to precisely estimate the overall risk. We encourage researchers to justify, perform and report sample size calculations when developing a prediction model

Clinicians approaches to management of background treatment in patients with SLE in clinical remission:results of an international observational survey

Objective: The definition of remission in SLE remains unclear, especially how background therapy should be interpreted. We aimed to determine preferences in how clinicians caring for SLE patients manage background therapy in patients in clinical remission and to assess how previous severity, duration of remission and serology influences therapy alterations. Methods: We undertook an internet-based survey of clinicians managing SLE patients. Case scenarios were constructed to reflect different remission states, previous organ involvement, serological abnormalities, duration of remission and current therapy (hydroxychloroquine [HCQ], steroids and/or immunosuppressives [ISS]). Results: 130 clinicians from 30 countries were surveyed. The median (range) duration of practice and number of SLE patients seen per month was 13 (2, 42) years and 30 (2, 200) respectively. There was variation in management decisions across all scenarios with increasing caution on therapy reduction with shorter duration of remission, extent of serological abnormalities and previous disease severity. Even with mild disease, normal serology and a 5-year clinical remission, 113 (86.7%) clinicians continue HCQ. Persistent abnormal serology in any scenario led to a reluctance to reduce or discontinue medications. Prescribing in remission scenarios varied significantly according to geographic location, particularly with regard to steroids and HCQ.Conclusions:Clinicians preferences in withdrawing or reducing therapy in SLE patients in clinical remission varies substantially. Serological abnormalities, previous disease severity and duration of remission all influence the decision to reduce treatments. It is unusual for clinicians to withdraw HCQ even after prolonged periods of clinical remission. Any definition(s) of remission need to take into consideration such evidence on how maintenance therapies are managed.<br/

How to develop, externally validate, and update multinomial prediction models

Multinomial prediction models (MPMs) have a range of potential applications across healthcare where the primary outcome of interest has multiple nominal or ordinal categories. However, the application of MPMs is scarce, which may be due to the added methodological complexities that they bring. This article provides a guide of how to develop, externally validate, and update MPMs. Using a previously developed and validated MPM for treatment outcomes in rheumatoid arthritis as an example, we outline guidance and recommendations for producing a clinical prediction model using multinomial logistic regression. This article is intended to supplement existing general guidance on prediction model research. This guide is split into three parts: 1) Outcome definition and variable selection, 2) Model development, and 3) Model evaluation (including performance assessment, internal and external validation, and model recalibration). We outline how to evaluate and interpret the predictive performance of MPMs. R code is provided. We recommend the application of MPMs in clinical settings where the prediction of a nominal polytomous outcome is of interest. Future methodological research could focus on MPM-specific considerations for variable selection and sample size criteria for external validation

Symptoms in first degree relatives of patients with rheumatoid arthritis:evaluation of cross-sectional data from the symptoms in persons at risk of rheumatoid arthritis (SPARRA) questionnaire in the PRe-clinical EValuation of Novel Targets in RA (PREVeNT-RA) Cohort

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. 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: 870 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.<br/

Symptoms in first degree relatives of patients with rheumatoid arthritis:evaluation of cross-sectional data from the symptoms in persons at risk of rheumatoid arthritis (SPARRA) questionnaire in the PRe-clinical EValuation of Novel Targets in RA (PREVeNT-RA) Cohort

Abstract 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

Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques

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