Background: Rehabilitation treatments and services are essential for the recovery of post-stroke patients’ functions; however, the increasing number of available therapies and the lack of consensus among outcome measures compromises the possibility to determine an appropriate level of evidence. Machine learning techniques for prognostic applications offer accurate and interpretable predictions, supporting the clinical decision for personalised treatment. The aim of this study is to develop and cross-validate predictive models for the functional prognosis of patients, highlighting the contributions of each predictor. Methods: A dataset of 278 post-stroke patients was used for the prediction of the class transition, obtained from the modified Barthel Index. Four classification algorithms were cross-validated and compared. On the best performing model on the validation set, an analysis of predictors contribution was conducted. Results: The Random Forest obtained the best overall results on the accuracy (76.2%), balanced accuracy (74.3%), sensitivity (0.80), and specificity (0.68). The combination of all the classification results on the test set, by weighted voting, reached 80.2% accuracy. The predictors analysis applied on the Support Vector Machine, showed that a good trunk control and communication level, and the absence of bedsores retain the major contribution in the prediction of a good functional outcome. Conclusions: Despite a more comprehensive assessment of the patients is needed, this work paves the way for the implementation of solutions for clinical decision support in the rehabilitation of post-stroke patients. Indeed, offering good prognostic accuracies for class transition and patient-wise view of the predictors contributions, it might help in a personalised optimisation of the patients’ rehabilitation path.

Previous analyses demonstrated a lack of unidimensionality, item redundancy, and substantial administrative burden for the Brain Injury Rehabilitation Trust Personality Questionnaires (BIRT-PQs). T...

Objective: To assess the internal construct validity (ICV) of the five Brain Injury Rehabilitation Trust Personality Questionnaires (BIRT-PQ) with Classical Test Theory methods. Methods: Multicenter cross-sectional study involving 11 Italian rehabilitation centers. BIRT-PQs were administered to patients with severe Acquired Brain Injury and their respective caregivers. ICV was assessed by the mean of an internal consistency analysis (ICA) and a Confirmatory Factor Analysis (CFA). Results: Data from 154 patients and their respective caregivers were pooled, giving a total sample of 308 subjects. Despite good overall values (alphas ranging from 0.811 to 0.937), the ICA revealed that several items within each scale did not contribute as expected to the total score. This result was confirmed by the CFA, which showed the misfit of the data to a unidimensional model (RMSEA ranging from 0.077 to 0.097). However, after accounting for local dependency found within the data, fitness to a unidimensional model improved significantly (RMSEA ranging from 0.050 to 0.062). Conclusion: Despite some limitations, our analyses demonstrated the lack of ICV for the BIRT-PQ total scores. It is envisaged that a more comprehensive ICV analysis will be performed with Rasch analysis, aiming to improve both the measurement properties and the administrative burden of each BIRT-PQ.

Background The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) represents the most recent severe pandemic resulting in coronavirus disease 2019 (COVID-19). COVID-19 can damage the central nervous system, requiring admission to intensive care units (ICU) and aggressive treatments (long-term ventilatory assistance and sedation) to stabilize vitals. Most post-COVID-19 patients experience cognitive impairments and mood or stress disorders. We aimed to study the frequency of cognitive deficits in COVID-19 survivors, the relationship between clinical factors in the acute phase and cognitive outcomes, affective states, and quality of life. We explored cognitive reserve (CR) role, as a post-COVID-19 resilience factor. Methods Twenty-nine COVID-19 inpatients were assessed using a neuropsychological battery, mood scales, quality of life, and social integration questionnaires. Twenty-five were retained through telephone follow-up to monitor cognitive sequelae, affective states, and reintegration levels roughly 8 months after hospital discharge. We administered the Cognitive Reserve Index questionnaire. Results We found most patients display no cognitive deficits. When they did, multi‐domain impairment occurred most frequently, especially involving executive functions. Results revealed a significant correlation between depression levels and the interval between ICU admission and tracheal tube removal. We found increased levels of depression and anxiety at follow-up, a significant relationship between resuming daily life activities, high CR, and executive functions. Conclusions These findings suggest the importance of psychological support in the long term and the modulating role of cognitive reserve in quality of life after infection.