Early prediction of upper limb functioning after stroke using clinical bedside assessments: a prospective longitudinal study.

Early and accurate prediction of recovery is needed to assist treatment planning and inform patient selection in clinical trials. This study aimed to develop a prediction algorithm using a set of simple early clinical bedside measures to predict upper limb capacity at 3-months post-stroke. A secondary analysis of Stroke Arm Longitudinal Study at Gothenburg University (SALGOT) included 94 adults (mean age 68 years) with upper limb impairment admitted to stroke unit). Cluster analysis was used to define the endpoint outcome strata according to the 3-months Action Research Arm Test (ARAT) scores. Modelling was carried out in a training (70%) and testing set (30%) using traditional logistic regression, random forest models. The final algorithm included 3 simple bedside tests performed 3-days post stroke: ability to grasp, to produce any measurable grip strength and abduct/elevate shoulder. An 86-94% model sensitivity, specificity and accuracy was reached for differentiation between poor, limited and good outcome. Additional measurement of grip strength at 4 weeks post-stroke and haemorrhagic stroke explained the underestimated classifications. External validation of the model is recommended. Simple bedside assessments have advantages over more lengthy and complex assessments and could thereby be integrated into routine clinical practice to aid therapy decisions, guide patient selection in clinical trials and used in data registries

Improving predictor selection for injury modelling methods in male footballers.

Objectives: This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football. Methods: Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team's 2015/2016 season. Results: The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of 'no-play' and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury. Conclusion: For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required