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Background: Orthopaedic footwear can only be effective in preventing diabetic foot ulcers if worn by the patient. Robust data on long-term wearing time of orthopaedic footwear are not available, and needed to gain more insights into wearing patterns and associated factors (i.e. participants' demographic, disease-related characteristics, and footwear usability). We aimed to objectively assess long-term wearing patterns and identify factors associated with wearing orthopaedic footwear in people with diabetes at moderate-to-high risk of ulceration., Methods: People diagnosed with diabetes mellitus type 1 and 2 with loss of protective sensation and/or peripheral artery disease and prescribed with orthopaedic footwear were included and followed for 12 months. The primary outcome was mean daily wearing time, continuously measured using a temperature sensor inside the footwear (Orthotimer®). Adherence to wearing orthopaedic footwear was calculated as percentage of wearing time of a total assumed 16 h out-of-bed daytime, where adherence < 60% was a pre-determined non-adherent threshold. Wearing time patterns were assessed by calculating participants' wearing (in)consistency. One-way analyses of variance tested for wearing time differences between subgroups, weekdays, and weekend days. Factors potentially associated with wearing time were collected by questionnaires and medical files. Univariately associated factors were included in multivariate linear regression analysis., Results: Sixty one participants were included (mean (SD) age: 68.0 (7.4) years; females: n = 17; type 2 diabetes mellitus: n = 54). Mean (SD) overall daily wearing time was 8.3 (6.1) hours/day. A total of 40 (66%) participants were non-adherent. Participants with a consistent wearing pattern showed higher daily wearing times than participants with an inconsistent pattern. Mean (SD) wearing times were 12.7 (4.3) vs 3.6 (4.8) hours/day, respectively (P < 0.001). Mean (SD) wearing time was significantly higher (P < 0.010) during weekdays (8.7 (6.0) hours/day) compared to Saturday (8.0 (6.1) hours/day) and Sunday (6.9 (6.2) hours/day). In the multivariate model (R 2  = 0.28), "satisfaction with my wear of orthopaedic footwear" was positively associated (P < 0.001) with wearing time. The other seven multivariate model factors (four demographic variables and three footwear usability variables) were not associated with wearing time., Conclusions: Only one out of three people at moderate to high risk of foot ulceration were sufficiently adherent to wearing their orthopaedic footwear. Changing people's wearing behaviour to a more stable pattern seems a potential avenue to improve long-term adherence to wearing orthopaedic footwear. Investigated factors are not associated with daily wearing time. Based on these factors the daily wearing time cannot be estimated in daily practice., Trial Registration: Netherlands Trial Register NL7710. Registered: 6 May 2019., (© 2023. The College of Podiatry and the Australasian Podiatry Council.)

Objectives: There are numerous point-of-care tests (POCTs) available on the market, but many of these are not used. This study reviewed literature pertaining to the evaluation/usage of POCTs in primary care, to investigate whether outcomes being reported reflect aspects previously demonstrated to be important for general practitioners (GPs) in the decision to implement a POCT in practice., Methods: Scopus and Medline were searched to identify studies that evaluated a POCT in primary care. We identified abstracts and full-texts consisting of applied studies (eg trials, simulations, observational studies) and qualitative studies (eg interviews, surveys). Data were extracted from the included studies, such as the type of study, the extent to which manufacturers were involved in the study, and the biomarker/assay measured by the test(s). Studies were evaluated to summarise the extent to which they reported on, amongst others, clinical utility, user-friendliness, turnaround-time and technical performance (aspects previously identified as important)., Results: The initial search resulted in 1398 publications, of which 125 met the inclusion criteria. From these studies, 83 POCTs across several disease areas (including cardiovascular disease, venous thromboembolism and respiratory-tract-infections) were identified. There was an inconsistency between what is reported in the studies and what GPs consider important. GPs perceive clinical utility as the most important aspect, yet this was rarely included explicitly in test evaluations in the literature, with only 8% of evaluations incorporating it in their analysis/discussion., Conclusions: This review showed that, despite the growing market and development of new POCTs, studies evaluating such tests fail to report on aspects that GPs find important. To ensure that an evaluation of a POCT is useful to primary care clinicians, future evaluations should not only focus on the technical performance aspects of a test, but also report on the aspects relating to the clinical utility and risks., (© 2019 The Authors. International Journal of Clinical Practice published by John Wiley & Sons Ltd.)

Prediction models fitted with logistic regression often show poor performance when applied in populations other than the development population. Model updating may improve predictions. Previously suggested methods vary in their extensiveness of updating the model. We aim to define a strategy in selecting an appropriate update method that considers the balance between the amount of evidence for updating in the new patient sample and the danger of overfitting. We consider recalibration in the large (re-estimation of model intercept); recalibration (re-estimation of intercept and slope) and model revision (re-estimation of all coefficients) as update methods. We propose a closed testing procedure that allows the extensiveness of the updating to increase progressively from a minimum (the original model) to a maximum (a completely revised model). The procedure involves multiple testing with maintaining approximately the chosen type I error rate. We illustrate this approach with three clinical examples: patients with prostate cancer, traumatic brain injury and children presenting with fever. The need for updating the prostate cancer model was completely driven by a different model intercept in the update sample (adjustment: 2.58). Separate testing of model revision against the original model showed statistically significant results, but led to overfitting (calibration slope at internal validation = 0.86). The closed testing procedure selected recalibration in the large as update method, without overfitting. The advantage of the closed testing procedure was confirmed by the other two examples. We conclude that the proposed closed testing procedure may be useful in selecting appropriate update methods for previously developed prediction models. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Individual participant data meta-analyses (IPD-MA) are increasingly used for developing and validating multivariable (diagnostic or prognostic) risk prediction models. Unfortunately, some predictors or even outcomes may not have been measured in each study and are thus systematically missing in some individual studies of the IPD-MA. As a consequence, it is no longer possible to evaluate between-study heterogeneity and to estimate study-specific predictor effects, or to include all individual studies, which severely hampers the development and validation of prediction models. Here, we describe a novel approach for imputing systematically missing data and adopt a generalized linear mixed model to allow for between-study heterogeneity. This approach can be viewed as an extension of Resche-Rigon's method (Stat Med 2013), relaxing their assumptions regarding variance components and allowing imputation of linear and nonlinear predictors. We illustrate our approach using a case study with IPD-MA of 13 studies to develop and validate a diagnostic prediction model for the presence of deep venous thrombosis. We compare the results after applying four methods for dealing with systematically missing predictors in one or more individual studies: complete case analysis where studies with systematically missing predictors are removed, traditional multiple imputation ignoring heterogeneity across studies, stratified multiple imputation accounting for heterogeneity in predictor prevalence, and multilevel multiple imputation (MLMI) fully accounting for between-study heterogeneity. We conclude that MLMI may substantially improve the estimation of between-study heterogeneity parameters and allow for imputation of systematically missing predictors in IPD-MA aimed at the development and validation of prediction models., (Copyright © 2015 John Wiley & Sons, Ltd.)

Published clinical prediction models are often ignored during the development of novel prediction models despite similarities in populations and intended usage. The plethora of prediction models that arise from this practice may still perform poorly when applied in other populations. Incorporating prior evidence might improve the accuracy of prediction models and make them potentially better generalizable. Unfortunately, aggregation of prediction models is not straightforward, and methods to combine differently specified models are currently lacking. We propose two approaches for aggregating previously published prediction models when a validation dataset is available: model averaging and stacked regressions. These approaches yield user-friendly stand-alone models that are adjusted for the new validation data. Both approaches rely on weighting to account for model performance and between-study heterogeneity but adopt a different rationale (averaging versus combination) to combine the models. We illustrate their implementation in a clinical example and compare them with established methods for prediction modeling in a series of simulation studies. Results from the clinical datasets and simulation studies demonstrate that aggregation yields prediction models with better discrimination and calibration in a vast majority of scenarios, and results in equivalent performance (compared to developing a novel model from scratch) when validation datasets are relatively large. In conclusion, model aggregation is a promising strategy when several prediction models are available from the literature and a validation dataset is at hand. The aggregation methods do not require existing models to have similar predictors and can be applied when relatively few data are at hand., (Copyright © 2014 John Wiley & Sons, Ltd.)

The use of individual participant data (IPD) from multiple studies is an increasingly popular approach when developing a multivariable risk prediction model. Corresponding datasets, however, typically differ in important aspects, such as baseline risk. This has driven the adoption of meta-analytical approaches for appropriately dealing with heterogeneity between study populations. Although these approaches provide an averaged prediction model across all studies, little guidance exists about how to apply or validate this model to new individuals or study populations outside the derivation data. We consider several approaches to develop a multivariable logistic regression model from an IPD meta-analysis (IPD-MA) with potential between-study heterogeneity. We also propose strategies for choosing a valid model intercept for when the model is to be validated or applied to new individuals or study populations. These strategies can be implemented by the IPD-MA developers or future model validators. Finally, we show how model generalizability can be evaluated when external validation data are lacking using internal-external cross-validation and extend our framework to count and time-to-event data. In an empirical evaluation, our results show how stratified estimation allows study-specific model intercepts, which can then inform the intercept to be used when applying the model in practice, even to a population not represented by included studies. In summary, our framework allows the development (through stratified estimation), implementation in new individuals (through focused intercept choice), and evaluation (through internal-external validation) of a single, integrated prediction model from an IPD-MA in order to achieve improved model performance and generalizability., (Copyright © 2013 John Wiley & Sons, Ltd.)

During the recent decades, interest in prediction models has substantially increased, but approaches to synthesize evidence from previously developed models have failed to keep pace. This causes researchers to ignore potentially useful past evidence when developing a novel prediction model with individual participant data (IPD) from their population of interest. We aimed to evaluate approaches to aggregate previously published prediction models with new data. We consider the situation that models are reported in the literature with predictors similar to those available in an IPD dataset. We adopt a two-stage method and explore three approaches to calculate a synthesis model, hereby relying on the principles of multivariate meta-analysis. The former approach employs a naive pooling strategy, whereas the latter accounts for within-study and between-study covariance. These approaches are applied to a collection of 15 datasets of patients with traumatic brain injury, and to five previously published models for predicting deep venous thrombosis. Here, we illustrated how the generally unrealistic assumption of consistency in the availability of evidence across included studies can be relaxed. Results from the case studies demonstrate that aggregation yields prediction models with an improved discrimination and calibration in a vast majority of scenarios, and result in equivalent performance (compared with the standard approach) in a small minority of situations. The proposed aggregation approaches are particularly useful when few participant data are at hand. Assessing the degree of heterogeneity between IPD and literature findings remains crucial to determine the optimal approach in aggregating previous evidence into new prediction models., (Copyright © 2012 John Wiley & Sons, Ltd.)