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Introduction Poststroke spasticity (PSS) affects up to 40% of patients who had a stroke. Botulinum neurotoxin type A (BoNT-A) has been shown to improve spasticity, but the optimal timing of its application remains unclear. While several predictors of upper limb PSS are known, their utility in clinical practice in relation to BoNT-A treatment has yet to be fully elucidated. The COLOSSEO-BoNT study aims to investigate predictors of PSS and the effects of BoNT-A timing on spasticity-related metrics in a real-world setting.Methods and analysis The recruitment will involve approximately 960 patients who have recently experienced an ischaemic stroke (within 10 days, V0) and will follow them up for 24 months. Parameters will be gathered at specific intervals: (V1) 4, (V2) 8, (V3) 12, (V4) 18 months and (V5) 24 months following enrolment. Patients will be monitored throughout their rehabilitation and outpatient clinic journeys and will be compared based on their BoNT-A treatment status—distinguishing between patients receiving treatment at different timings and those who undergo rehabilitation without treatment. Potential predictors will encompass the Fugl-Meyer assessment, the National Institute of Health Stroke Scale (NIHSS), stroke radiological characteristics, performance status, therapies and access to patient care pathways. Outcomes will evaluate muscle stiffness using the modified Ashworth scale and passive range of motion, along with measures of quality of life, pain, and functionality.Ethics and dissemination This study underwent review and approval by the Ethics Committee of the Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy. Regardless of the outcome, the findings will be disseminated through publication in peer-reviewed journals and presentations at national and international conferences.Trial registration number NCT05379413.

BackgroundHandwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders.Materials and MethodsOne-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm.ResultsStroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83).DiscussionHandwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms.