BACKGROUND: The search for reliable techniques to assess gait in stroke patients is crucial for the design and follow-up of rehabilitation programs. OBJECTIVE: To assess the reproducibility of kinematic and kinetic gait parameters in chronic stroke patients using a three-dimensional gait analysis system. METHODS: Ten chronic stroke patients were assessed while walking along a 20 m walkway at their natural speed, using a gait analysis system of six infrared cameras and two force plates. Each patient performed 10 gait trials on 2 separate days. Inter-measurement agreement was assessed with the Coefficient of Multiple Correlation, while Root Mean Square Differences were used to quantify the variability of the trials. RESULTS: The majority of kinetics and kinematics showed excellent reproducibility in all patients. Joints’ power seemed to be more reliable compared with joints’ angle and moment. Most parameters presented greater variability in non-paretic than the paretic leg, while they were less variable in the sagittal compared with the non-sagittal planes. Less than 10 trials were sufficient to obtain excellent reproducibility for most kinematic and kinetic parameters. CONCLUSIONS: The reproducibility of movement assessment through three-dimensional gait analysis appears excellent in chronic stroke patients.

Predicting functional outcome following stroke is considered to be of key importance in an attempt to optimize overall stroke care. Although clinical prognostic tools have been widely implemented, optimal blood biomarkers might be able to yield additional information regarding each stroke survivor’s propensity for recovery. Copeptin seems to have interesting prognostic potential poststroke. The present review aims to explore the prognostic significance of copeptin in stroke patients. Literature research of two databases (MEDLINE and Scopus) was conducted to trace all relevant studies published between 16 February 2012 and 16 February 2022 that focused on the utility of copeptin as a prognostic marker in acute stroke setting. 25 studies have been identified and included in the present review. The predictive ability of copeptin regarding both functional outcome and mortality appears to be in the range of established clinical variables, thus highlighting the added value of copeptin evaluation in stroke management. Apart from acute ischemic stroke, the discriminatory accuracy of the biomarker was also demonstrated among patients with transient ischemic attack, intracerebral hemorrhage, and subarachnoid hemorrhage. Overall, copeptin represents a powerful prognostic tool, the clinical implementation of which is expected to significantly facilitate the individualized management of stroke patients.

Research of post-stroke locomotion via musculoskeletal (MSK) modeling has offered an unprecedented insight into pathological muscle function and its interplay with skeletal geometry and external stimuli. Advances in solving the dynamical system of post-stroke effort and the generic MSK models used have triggered noticeable improvements in simulating muscle activation dynamics of stroke populations. However, a review of these advancements to inform the scientific community has yet to be made.: PubMed and Scopus databases were used to perform a thorough literature search to identify relevant articles since 2010. Here, we review MSK methods and practices—developed in the last ten years—that have been utilized to explore post-stroke locomotion and examine how their outcomes can inform clinical practice.: Out of the 44 articles that were initially found, 19 were reviewed. The articles were categorized with respect to the type of assessment the MSK methods were used for.: This review notes the considerable competence of existing methods to address post-stroke motion deficits. However, the drawbacks in the implementation of such methods by non-experts due to the high skill demand and the lack of mature software technology for further dissemination of practices and outcomes remain non-trivial.

This study aims to evaluate the consistency of musculoskeletal modelling outcomes during walking in chronic post-stroke patients, focusing on both affected and unaffected sides. Understanding the specific muscle forces involved is crucial for designing targeted rehabilitation strategies to improve balance and mobility after a stroke. Musculoskeletal modelling provides valuable insights into muscle and joint loading, aiding clinicians in analysing essential biomarkers and enhancing patients’ functional outcomes. However, the repeatability of these modelling outcomes in stroke gait has not been thoroughly explored until now. Twelve post-stroke, hemiparetic survivors were included in the study, which consisted of a gait analysis protocol to capture kinematic and kinetic variables. Two generic full body MSK models—Hamner (Ham) and Rajagopal (Raj)—were used to compute joint angles and muscle forces during walking, with combinations of two muscle force estimation algorithms (Static Optimisation (SO) and Computed Muscle Control (CMC)) and different joint degrees-of-freedoms (DOF). The multiple correlation coefficient (MCCoef) was used to compute repeatability for all forces, grouped based on anatomical function. Regardless of models and DOFs, the mean minimum (0.75) and maximum (0.94) MCCoefs denote moderate-to-excellent repeatability for all muscle groups. The combination of the Ham model and SO provided the most repeatable muscle force estimations of all the muscle groups except for the hip flexors, adductors and internal rotators. DOF configuration did not generally affect muscle force repeatability in the Ham–SO case, although the 311 seemed to relate to the highest values. Lastly, the DOF setting had a significant effect on some muscle groups’ force output, with the highest magnitudes reported for the 321 and 322 of non-paretic and paretic hip adductors and extensors, knee flexors and ankle dorsiflexors and paretic knee flexors. The primary findings of our study can assist users in selecting the most suitable modelling workflow and encourage the widespread adoption of MSK modelling in clinical practice.