Comparing the effects of augmented virtual reality treadmill training versus conventional treadmill training in patients with stage II-III Parkinson's disease: the VIRTREAD-PD randomized controlled trial protocol.

Background: Intensive treadmill training (TT) has been documented to improve gait parameters and functional independence in Parkinson's Disease (PD), but the optimal intervention protocol and the criteria for tailoring the intervention to patients' performances are lacking. TT may be integrated with augmented virtual reality (AVR), however, evidence of the effectiveness of this combined treatment is still limited. Moreover, prognostic biomarkers of rehabilitation, potentially useful to customize the treatment, are currently missing. The primary aim of this study is to compare the effects on gait performances of TT + AVR versus TT alone in II-III stage PD patients with gait disturbance. Secondary aims are to assess the effects on balance, gait parameters and other motor and non-motor symptoms, and patient's satisfaction and adherence to the treatment. As an exploratory aim, the study attempts to identify biomarkers of neuroplasticity detecting changes in Neurofilament Light Chain concentration T0-T1 and to identify prognostic biomarkers associated to blood-derived Extracellular Vesicles. Methods: Single-center, randomized controlled single-blind trial comparing TT + AVR vs. TT in II-III stage PD patients with gait disturbances. Assessment will be performed at baseline (T0), end of training (T1), 3 (T2) and 6 months (T3, phone interview) from T1. The primary outcome is difference in gait performance assessed with the Tinetti Performance-Oriented Mobility Assessment gait scale at T1. Secondary outcomes are differences in gait performance at T2, in balance and spatial-temporal gait parameters at T1 and T2, patients' satisfaction and adherence. Changes in falls, functional mobility, functional autonomy, cognition, mood, and quality of life will be also assessed at different timepoints. The G*Power software was used to estimate a sample size of 20 subjects per group (power 0.95, α < 0.05), raised to 24 per group to compensate for potential drop-outs. Both interventions will be customized and progressive, based on the participant's performance, according to a predefined protocol. Conclusion: This study will provide data on the possible superiority of AVR-associated TT over conventional TT in improving gait and other motor and non-motor symptoms in persons with PD and gait disturbances. Results of the exploratory analysis could add information in the field of biomarker research in PD rehabilitation.

Which information derived from the Coma Recovery Scale-Revised provides the most reliable prediction of clinical diagnosis and recovery of consciousness? A comparative study using machine learning techniques.

BACKGROUND: The Coma Recovery Scale-Revised (CRS-R) is the most recommended clinical tool to examine the neurobehavioral condition of individuals with disorders of consciousness (DOCs). Different studies have investigated the prognostic value of the information provided by the conventional administration of the scale, while other measures derived from the scale have been proposed to improve the prognosis of DOCs. However, the heterogeneity of the data used in the different studies prevents a reliable comparison of the identified predictors and measures. AIM: This study investigates which information derived from the CRS-R provides the most reliable prediction of both the clinical diagnosis and recovery of consciousness at the discharge of a long-term neurorehabilitation program. DESIGN: Retrospective observational multisite study. SETTING: The enrollment was performed in three neurorehabilitation facilities of the same hospital network. POPULATION: A total of 171 individuals with DOCs admitted to an inpatient neurorehabilitation program for a minimum of 3 months were enrolled. METHODS: Machine learning classifiers were trained to predict the clinical diagnosis and recovery of consciousness at discharge using clinical confounders and different metrics extracted from the CRS-R scale. RESULTS: Results showed that the neurobehavioral state at discharge was predicted with acceptable and comparable predictive value with all the indices and measures derived from the CRS-R, but for the clinical diagnosis and the Consciousness Domain Index, and the recovery of consciousness was predicted with higher accuracy and similarly by all the investigated measures, with the exception of initial clinical diagnosis. CONCLUSIONS: Interestingly, the total score in the CRS-R and, especially, the total score in its subscales provided the best overall results, in contrast to the clinical diagnosis, which could indicate that a comprehensive measure of the clinical diagnosis rather than the condition of the individuals could provide a more reliable prediction of the neurobehavioral progress of individuals with prolonged DOC. CLINICAL REHABILITATION IMPACT: The results of this work have important implications in clinical practice, offering a more accurate prognosis of patients and thus giving the possibility to personalize and optimize the rehabilitation plan of patients with DoC using low-cost and easily collectable information.

MoCA Domain-Specific Pattern of Cognitive Impairment in Stroke Patients Attending Intensive Inpatient Rehabilitation: A Prospective Study.

A domain-specific perspective to cognitive functioning in stroke patients may predict their cognitive recovery over time and target stroke rehabilitation intervention. However, data about domain-specific cognitive impairment after stroke are still scarce. This study prospectively investigated the domain-specific pattern of cognitive impairments, using the classification proposed by the Montreal Cognitive Assessment (MoCA), in a cohort of 49 stroke patients at admission (T0), discharge (T1), and six-month follow-up (T2) from subacute intensive rehabilitation. The predictive value of T0 cognitive domains cognitive impairment at T1 and T2 was also investigated. Patients' cognitive functioning at T0, T1, and T2 was assessed through the MoCA domains for executive functioning, attention, language, visuospatial, orientation, and memory. Different evolutionary trends of cognitive domain impairments emerged across time-points. Patients' impairments in all domains decreased from T0 to T1. Attention and executive impairments decreased from T0 to T2 (42.9% and 26.5% to 10.2% and 18.4%, respectively). Conversely, altered visuospatial, language, and orientation increased between T1 and T2 (16.3%, 36.7%, and 40.8%, respectively). Additionally, patients' global cognitive functioning at T1 was predicted by the language and executive domains in a subacute phase (p = 0.031 and p = 0.001, respectively), while in the long term, only attention (p = 0.043) and executive (p = 0.019) domains intervened. Overall, these results confirm the importance of a domain-specific approach to target cognitive recovery across time in stroke patients.

Trunk Control Test as a Main Predictor of the Modified Barthel Index Score at Discharge From Intensive Post-acute Stroke Rehabilitation: Results From a Multicenter Italian Study.

OBJECTIVES: To verify whether trunk control test (TCT) upon admission to intensive inpatient post-stroke rehabilitation, combined with other confounding variables, is independently associated with discharge mBI. DESIGN: Multicentric retrospective observational cohort study. SETTING: Two Italian inpatient rehabilitation units. PARTICIPANTS: A total of 220 post-stroke adult patients, within 30 days from the acute event, were consecutively enrolled. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: The outcome measure considered was the modified Barthel Index (mBI), one of the most widely recommended tools for assessing stroke rehabilitation functional outcomes. RESULTS: All variables collected at admission and significantly associated with mBI at discharge in the univariate analysis (TCT, mBI at admission, pre-stroke modified Rankin Scale [mRS], sex, age, communication ability, time from the event, Cumulative Illness Rating Scale, bladder catheter, and pressure ulcers) entered the multivariate analysis. TCT, mBI at admission, premorbid disability (mRS), communication ability and pressure ulcers (P<.001) independently predicted discharge mBI (adjusted R2=68.5%). Concerning the role of TCT, the model with all covariates and without TCT presented an R2 of 65.1%. On the other side, the model with the TCT only presented an R2 of 53.1%. Finally, with the inclusion of both TCT and all covariates, the model showed an R2 increase up to 68.5%. CONCLUSIONS: TCT, with other features suggesting functional/clinical complexity, collected upon admission to post-acute intensive inpatient stroke rehabilitation, independently predicted discharge mBI.

Predicting the functional outcome of intensive inpatient rehabilitation after stroke: results from the RIPS Study.

BACKGROUND: The complexity of stroke sequelae, the heterogeneity of outcome measures and rehabilitation pathways, and the lack of extensively validated prediction models represent a challenge in predicting stroke rehabilitation outcomes. AIM: To prospectively investigate a multidimensional set of variables collected at admission to inpatient post-stroke rehabilitation as potential predictors of the functional level at discharge. DESIGN: Multicentric prospective observational study. SETTING: Patients were enrolled in four Intensive Rehabilitation Units (IRUs). POPULATION: Patients were consecutively recruited in the period December 2019-December 2020 with the following inclusion criteria: aged 18+, with ischemic/haemorrhagic stroke, and undergoing inpatient rehabilitation within 30 days from stroke. METHODS: This is a multicentric prospective observational study. The rehabilitation pathway was reproducible and evidence-based. The functional outcome was disability in activities of daily living, measured by the modified Barthel Index (mBI) at discharge. Potential multidimensional predictors, assessed at admission, included demographics, event description, clinical assessment, functional and cognitive profile, and psycho-social domains. The variables statistically associated with the outcome in the univariate analysis were fed into a multivariable model using multiple linear regression. RESULTS: A total of 220 patients were included (median [IQR] age: 80 [15], 112 women, 175 ischemic). Median mBI was 26 (43) at admission and 62.5 (52) at discharge. In the multivariable analysis younger age, along with better functioning, fewer comorbidities, higher cognitive abilities, reduced stroke severity, and higher motor functions at admission, remained independently associated with higher discharge mBI. The final model allowed a reliable prediction of discharge functional outcome (adjusted R2=77.2%). CONCLUSIONS: The model presented in this study, based on easily collectable, reliable admission variables, could help clinicians and researchers to predict the discharge scores of the global functional outcome for persons enrolled in an evidence-based inpatient stroke rehabilitation program. CLINICAL REHABILITATION IMPACT: A reliable outcome prediction derived from standardized assessment measures and validated treatment protocols could guide clinicians in the management of patients in the subacute phase of stroke and help improve the planning of the rehabilitation individualized project.

Brain microstate spatio-temporal dynamics as a candidate endotype of consciousness.

Consciousness can be defined as a phenomenological experience continuously evolving. Current research showed how conscious mental activity can be subdivided into a series of atomic brain states converging to a discrete spatiotemporal pattern of global neuronal firing. Using the high temporal resolution of EEG recordings in patients with a severe Acquired Brain Injury (sABI) admitted to an Intensive Rehabilitation Unit (IRU), we detected a novel endotype of consciousness from the spatiotemporal brain dynamics identified via microstate analysis. Also, we investigated whether microstate features were associated with common neurophysiological alterations. Finally, the prognostic information comprised in such descriptors was analysed in a sub-cohort of patients with prolonged Disorder of Consciousness (pDoC). Occurrence of frontally-oriented microstates (C microstate), likelihood of maintaining such brain state or transitioning to the C topography and complexity were found to be indicators of consciousness presence and levels. Features of left-right asymmetric microstates and transitions toward them were found to be negatively correlated with antero-posterior brain reorganization and EEG symmetry. Substantial differences in microstates' sequence complexity and presence of C topography were found between groups of patients with alpha dominant background, cortical reactivity and antero-posterior gradient. Also, transitioning from left-right to antero-posterior microstates was found to be an independent predictor of consciousness recovery, stronger than consciousness levels at IRU's admission. In conclusions, global brain dynamics measured with scale-free estimators can be considered an indicator of consciousness presence and a candidate marker of short-term recovery in patients with a pDoC.

Estimation of Spatiotemporal Gait Parameters in Walking on a Photoelectric System: Validation on Healthy Children by Standard Gait Analysis.

The use of stereophotogrammetry systems is challenging when targeting children's gait analysis due to the time required and the need to keep physical markers in place. For this reason, marker-less photoelectric systems appear to be a solution for accurate and fast gait analysis in youth. The aim of this study is to validate a photoelectric system and its configurations (LED filter setting) on healthy children, comparing the kinematic gait parameters with those obtained from a three-dimensional stereophotogrammetry system. Twenty-seven healthy children were enrolled. Three LED filter settings for the OptoGait were compared to the BTS P6000. The analysis included the non-parametric 80% limits of agreement and the intraclass correlation coefficient (ICC). Additionally, normalised limits of agreement and bias (NLoAs and Nbias) were compared to the clinical experience of physical therapists (i.e., assuming an error lower than 5% is acceptable). ICCs showed excellent consistency for most of the parameters and filter settings; NLoAs varied between 1.39% and 12.62%. An inverse association between the number of LEDs for filter setting and the bias values was also observed. Observations confirm the validity of the OptoGait system for the evaluation of spatiotemporal gait parameters in children.

Artificial intelligence for the recognition of benign lesions of vocal folds from audio recordings.

Objective: The diagnosis of benign lesions of the vocal fold (BLVF) is still challenging. The analysis of the acoustic signals through the implementation of machine learning models can be a viable solution aimed at offering support for clinical diagnosis. Materials and methods: In this study, a support vector machine was trained and cross-validated (10-fold cross-validation) using 138 features extracted from the acoustic signals of 418 patients with polyps, nodules, oedema, and cysts. The model's performance was presented as accuracy and average F1-score. The results were also analysed in male (M) and female (F) subgroups. Results: The validation accuracy was 55%, 80%, and 54% on the overall cohort, and in M and F, respectively. Better performances were observed in the detection of cysts and nodules (58% and 62%, respectively) vs polyps and oedema (47% and 53%, respectively). The results on each lesion and the different patterns of the model on M and F are in line with clinical observations, obtaining better results on F and detection of sensitive polyps in M. Conclusions: This study showed moderately accurate detection of four types of BLVF using acoustic signals. The analysis of the diagnostic results on gender subgroups highlights different behaviours of the diagnostic model.

Heart rate variability for the evaluation of patients with disorders of consciousness.

OBJECTIVE: Clinical responsiveness of patients with a Disorder of Consciousness (DoC) correlates to sympathetic/parasympathetic homeostatic balance. Heart Rate Variability (HRV) metrics result in non-invasive proxies of modulation capabilities of visceral states. In this work, our aim was to evaluate whether HRV measures could improve the differential diagnosis between Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS) with respect to multivariate models based on standard clinical electroencephalography (EEG) labeling only in a rehabilitation setting. METHODS: A prospective observational study was performed consecutively enrolling 82 DoC patients. Polygraphic recordings were performed. HRV-metrics and EEG descriptors derived from the American Clinical Neurophysiology Society's Standardized Critical Care terminology were included. Descriptors entered univariate and then multivariate logistic regressions with the target set to the UWS/MCS diagnosis. RESULTS: HRV measures resulted significantly different between UWS and MCS patients, with higher values being associated with better consciousness levels. Specifically, adding HRV-related metrics to ACNS EEG descriptors increased the Nagelkerke R2 from 0.350 (only EEG descriptors) to 0.565 (HRV-EEG combination) with the outcome set to the consciousness diagnosis. CONCLUSIONS: HRV changes across the lowest states of consciousness. Rapid changes in heart rate, occurring in better consciousness levels, confirm the mutual correlation between visceral state functioning patterns and consciousness alterations. SIGNIFICANCE: Quantitative analysis of heart rate in patients with a DoC paves the way for the implementation of low-cost pipelines supporting medical decisions within multimodal consciousness assessments.

Correlates of participation six months after stroke in an Italian population: results from the RIPS (Post-Stroke Intensive Rehabilitation) Study.

BACKGROUND: Stroke survivors report physical, cognitive, and psychological impairments, with a consequent limitation of participation. Participation is the most context-related dimension of functioning, but the literature on participation in Italian stroke patients is scant. AIM: This study aimed to describe the recovery of participation six months after stroke with a validated Italian version of the Frenchay Activity Index (FAI) and to investigate potential correlates with higher participation scores. DESIGN: The study is a prospective observational study. SETTING: The cohort of patients was enrolled in four intensive inpatient rehabilitation units of IRCCS Fondazione Don Carlo Gnocchi Onlus, Florence, Italy. POPULATION: Adults addressing postacute intensive inpatient rehabilitation after an ischemic or hemorrhagic stroke occurred within 30 days from recruitment were prospectively enrolled. METHODS: Data were collected at admission to intensive inpatient rehabilitation, and a six-month follow-up. The primary outcome was participation, measured by a validated Italian version of the FAI; only patients whose data included both anamnestic FAI and FAI at six months follow-up were included in this analysis. The data were analyzed by univariate and multivariate linear regressions. RESULTS: A cohort of 105 patients (median age 78 years [interquartile range, IQR=21]; 46.7% males) with completed FAI at follow-up were included in this study. The sample reported a FAI median score of 28 (IQR=8) at admission (referred to the participation in the 3-6 months before the stroke) and 13 (IQR=20) at follow-up. All items were significantly affected, with the exception of reading and making trips. The multivariate regression for all patients with good participation before the stroke (N.=101), showed that 6 months after the stroke a higher FAI Score was independently associated with better functioning in activities of daily living (modified Barthel Index) (B=0.133; P=0.015), and absence of cognitive impairment (B=4.755; P=0.027); a lower stroke severity in the postacute phase (NIHSS B=-0.832; P=0.001) and a higher prestroke FAI Score (B=0.410; P=0.028) were also independently related to follow-up FAI Score. CONCLUSIONS: In our cohort of patients addressing postacute stroke rehabilitation, prestroke participation levels were on average good, while they were severely reduced six months after stroke for all the considered items except reading and making trips. Higher FAI at follow-up was independently associated with a higher functional level and no cognitive impairment at follow-up, with lower stroke severity in the postacute phase, as well as a higher anamnestic participation score. CLINICAL REHABILITATION IMPACT: Our results suggest that investigating prestroke participation may be highly relevant to predict, and possibly address, participation recovery after stroke.

Factors influencing trunk control recovery after intensive rehabilitation in post-stroke patients: a multicentre prospective study.

BACKGROUND: Trunk control plays a crucial role in the stroke rehabilitation, but it is unclear which factors could influence the trunk control after an intensive rehabilitation treatment. OBJECTIVES: To study which demographic, clinical and functional variables could predict the recovery of trunk control after intensive post-stroke inpatient rehabilitation. METHODS: Subjects with acute, first-ever stroke were enrolled and clinical and data were collected at admission and discharge. The primary outcome was considered the trunk control measured by the Trunk Control Test (TCT). The data were analyzed by a univariate and multivariate logistic regressions. RESULTS: Two hundred forty-one post-stroke patients were included. All baseline variables significantly associated to TCT at discharge in the univariate analysis (i.e. gender, NIHSS neglect item at admission, presence of several complexity markers, TCT total score at admission, NIHSS total score, pre-stroke modified Rankin Scale, Fugl-Meyer Assessment motor and sensitivity score) were entered in the multivariate analysis. The multivariate regression showed that age (p = .003), admission NIHSS total score (p = .001), admission TCT total score (p < .001) and presence of depression (p = .027) independently influenced the TCT total score at discharge (R2 = 61.2%). CONCLUSIONS: Age, admission neurological impairment (NIHSS total score), trunk control at the admission (TCT total score), and presence of depression independently influenced the TCT at discharge. These factors should be carefully assessed at the baseline to plan a tailoring rehabilitation treatment achieving the best trunk control performance at discharge.

Intra- and inter-rater reliability of the Italian Fugl-Meyer assessment of upper and lower extremity.

PURPOSE: To assess the intra- and inter-rater reliability motor and sensory functioning, balance, joint range of motion and joint pain subscales of the Italian Fugl-Meyer Assessment (FMA) Upper Extremity (FMA-UE) and Lower Extremity (FMA-LE) at the item- subtotal- and total-level in patients with sub-acute stroke. MATERIALS AND METHODS: The FMA was administered to 60 patients with sub-acute stroke (mean age ± SD = 75.4 ± 10.7 years; 58.3% men) and independently rated by two physiotherapists on two consecutive days. Intra- and inter-reliability was studied by a rank-based statistical method for paired ordinal data to detect any systematic or random disagreement. RESULTS: The item-level intra- and inter-rater reliability was satisfactory (>70%). Reliability level >70% was achieved at subscale and total score level when one- or two-points difference was considered. Systematic disagreements were reported for five items of the FMA-UE, but not for FMA-LE. CONCLUSIONS: The Italian version of the FMA showed to be a reliable instrument that can therefore be recommended for clinical and research purposes.Implications for rehabilitationThe FMA is the gold standard for assessing stroke patients' sensorimotor impairment worldwide.The Italian Fugl-Meyer Assessment of Upper Extremity (FMA-UE) and Lower Extremity (FMA-LE) is substantially reliable within and between two raters at the item, subtotal, and total score level in patients with sub-acute stroke.The use of FMA in the Italian context will provide an opportunity for international comparisons and research collaborations.

Functional and Clinical Characteristics of Individuals Attending Pulmonary Rehabilitation After Severe COVID-19.

BACKGROUND: A proportion of patients with COVID-19 need hospitalization due to severe respiratory symptoms. We sought to analyze characteristics of survivors of severe COVID-19 subsequently admitted to in-patient pulmonary rehabilitation and identify their rehabilitation needs. METHODS: From the COVID-19 Registry of Fondazione Don Gnocchi, we extracted 203 subjects admitted for in-patient pulmonary rehabilitation after severe COVID-19 from April 2020-September 2021. Specific information on acute-hospital stay and clinical and functional characteristics on admission to rehabilitation units were collected. RESULTS: During the acute phase of disease, 168 subjects received mechanical ventilation for 26 d; 85 experienced delirium during their stay in ICU. On admission to rehabilitation units, 20 subjects were still on mechanical ventilation; 57 had tracheostomy; 142 were on oxygen therapy; 49 were diagnosed critical illness neuropathy; 162 showed modified Barthel Index < 75; only 51 were able to perform a 6-min walk test; 32 of 90 scored abnormal at Montreal Cognitive Assessment; 43 of 88 scored abnormal at Hospital Anxiety and Depression Scale; 65 scored ≥ 2 at Malnutrition Universal Screening Tool, and 95 showed dysphagia needing logopedic treatment. CONCLUSIONS: Our analysis shows that subjects admitted for in-patient pulmonary rehabilitation after severe COVID-19 represent an extraordinarily multifaceted and clinically complex patient population who need customized, comprehensive rehabilitation programs carried out by teams with different professional skills. The need for step-down facilities, such as sub-intensive rehabilitation units, is also highlighted.

EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review.

OBJECTIVE: Disorders of consciousness (DoC) are acquired conditions of severely altered consciousness. Electroencephalography (EEG)-derived biomarkers have been studied as clinical predictors of consciousness recovery. Therefore, this study aimed to systematically review the methods, features, and models used to derive prognostic EEG markers in patients with DoC in a rehabilitation setting. METHODS: We conducted a systematic literature search of EEG-based strategies for consciousness recovery prognosis in five electronic databases. RESULTS: The search resulted in 2964 papers. After screening, 15 studies were included in the review. Our analyses revealed that simpler experimental settings and similar filtering cut-off frequencies are preferred. The results of studies were categorised by extracting qualitative and quantitative features. The quantitative features were further classified into evoked/event-related potentials, spectral measures, entropy measures, and graph-theory measures. Despite the variety of methods, features from all categories, including qualitative ones, exhibited significant correlations with DoC prognosis. Moreover, no agreement was found on the optimal set of EEG-based features for the multivariate prognosis of patients with DoC, which limits the computational methods applied for outcome prediction and correlation analysis to classical ones. Nevertheless, alpha power, reactivity, and higher complexity metrics were often found to be predictive of consciousness recovery. CONCLUSIONS: This study's findings confirm the essential role of qualitative EEG and suggest an important role for quantitative EEG. Their joint use could compensate for their reciprocal limitations. SIGNIFICANCE: This study emphasises the need for further efforts toward guidelines on standardised EEG analysis pipeline, given the already proven role of EEG markers in the recovery prognosis of patients with DoC.

Design and implementation of a Stroke Rehabilitation Registry for the systematic assessment of processes and outcomes and the development of data-driven prediction models: The STRATEGY study protocol.

Background: Stroke represents the second preventable cause of death after cardiovascular disease and the third global cause of disability. In countries where national registries of the clinical quality of stroke care have been established, the publication and sharing of the collected data have led to an improvement in the quality of care and survival of patients. However, information on rehabilitation processes and outcomes is often lacking, and predictors of functional outcomes remain poorly explored. This paper describes a multicenter study protocol to implement a Stroke rehabilitation Registry, mainly based on a multidimensional assessment proposed by the Italian Society of Physical and Rehabilitation Medicine (PMIC2020), in a pilot Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation, to provide a systematic assessment of processes and outcomes and develop data-driven prediction models of functional outcomes. Methods: All patients with a diagnosis of ischemic or haemorrhagic stroke confirmed by clinical assessment, admitted to intensive rehabilitation units within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled. Measures will be taken at admission (T0), at discharge (T1), and at follow-up, 3 months (T2) and 6 months (T3) after the stroke. Assessment variables include anamnestic data, clinical and nursing complexity information and measures of body structures and function, activity and participation (PMIC2020), rehabilitation interventions, adverse events and discharge data. The modified Barthel Index will be our primary outcome. In addition to classical biostatistical analysis, learning algorithms will be cross-validated to achieve data-driven prognosis prediction models. Conclusions: This study will test the feasibility of a stroke rehabilitation registry in the Italian health context and provide a systematic assessment of processes and outcomes for quality assessment and benchmarking. By the development of data-driven prediction models in stroke rehabilitation, this study will pave the way for the development of decision support tools for patient-oriented therapy planning and rehabilitation outcomes maximization. Clinical tial registration: The registration on ClinicalTrials.gov is ongoing and under review. The identification number will be provided when the review process will be completed.

Cross-validation of predictive models for functional recovery after post-stroke rehabilitation.

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.

Cross-Validation of Machine Learning Models for the Functional Outcome Prediction after Post-Stroke Robot-Assisted Rehabilitation.

The state of the art is still lacking an extensive analysis of which clinical characteristics are leading to better outcomes after robot-assisted rehabilitation on post-stroke patients. Prognostic machine learning-based models could promote the identification of predictive factors and be exploited as Clinical Decision Support Systems (CDSS). For this reason, the aim of this work was to set the first steps toward the development of a CDSS, by the development of machine learning models for the functional outcome prediction of post-stroke patients after upper-limb robotic rehabilitation. Four different regression algorithms were trained and cross-validated using a nested 5×10-fold cross-validation. The performances of each model on the test set were provided through the Median Average Error (MAE) and interquartile range. Additionally, interpretability analyses were performed, to evaluate the contribution of the features to the prediction. The results on the two best performing models showed a MAE of 13.6 [13.4] and 13.3 [14.8] on the Modified Barthel Index score (MBI). The interpretability analyses highlighted the Fugl-Meyer Assessment, MBI, and age as the most relevant features for the prediction of the outcome. This work showed promising results in terms of outcome prognosis after robot-assisted treatment. Further research should be planned for the development, validation and translation into clinical practice of CDSS in rehabilitation. Clinical relevance- This work establishes the premises for the development of data-driven tools able to support the clinical decision for the selection and optimisation of the robotic rehabilitation treatment.

Effects of control strategies on gait in robot-assisted post-stroke lower limb rehabilitation: a systematic review.

BACKGROUND: Stroke related motor function deficits affect patients' likelihood of returning to professional activities, limit their participation in society and functionality in daily living. Hence, robot-aided gait rehabilitation needs to be fruitful and effective from a motor learning perspective. For this reason, optimal human-robot interaction strategies are necessary to foster neuroplastic shaping during therapy. Therefore, we performed a systematic search on the effects of different control algorithms on quantitative objective gait parameters of post-acute stroke patients. METHODS: We conducted a systematic search on four electronic databases using the Population Intervention Comparison and Outcome format. The heterogeneity of performance assessment, study designs and patients' numerosity prevented the possibility to conduct a rigorous meta-analysis, thus, the results were presented through narrative synthesis. RESULTS: A total of 31 studies (out of 1036) met the inclusion criteria, without applying any temporal constraints. No controller preference with respect to gait parameters improvements was found. However, preferred solutions were encountered in the implementation of force control strategies mostly on rigid devices in therapeutic scenarios. Conversely, soft devices, which were all position-controlled, were found to be more commonly used in assistive scenarios. The effect of different controllers on gait could not be evaluated since conspicuous heterogeneity was found for both performance metrics and study designs. CONCLUSIONS: Overall, due to the impossibility of performing a meta-analysis, this systematic review calls for an outcome standardisation in the evaluation of robot-aided gait rehabilitation. This could allow for the comparison of adaptive and human-dependent controllers with conventional ones, identifying the most suitable control strategies for specific pathologic gait patterns. This latter aspect could bolster individualized and personalized choices of control strategies during the therapeutic or assistive path.

Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.

BACKGROUND: Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with new trends in personalised medical therapies, is expected to change clinical practice dramatically. The emerging field of Rehabilomics is only possible if methodologies are based on biomedical data collection and analysis. In this framework, the objective of this work is to develop a systematic review of machine learning algorithms as solutions to predict motor functional recovery of post-stroke patients after treatment. METHODS: We conducted a comprehensive search of five electronic databases using the Patient, Intervention, Comparison and Outcome (PICO) format. We extracted health conditions, population characteristics, outcome assessed, the method for feature extraction and selection, the algorithm used, and the validation approach. The methodological quality of included studies was assessed using the prediction model risk of bias assessment tool (PROBAST). A qualitative description of the characteristics of the included studies as well as a narrative data synthesis was performed. RESULTS: A total of 19 primary studies were included. The predictors most frequently used belonged to the areas of demographic characteristics and stroke assessment through clinical examination. Regarding the methods, linear and logistic regressions were the most frequently used and cross-validation was the preferred validation approach. CONCLUSIONS: We identified several methodological limitations: small sample sizes, a limited number of external validation approaches, and high heterogeneity among input and output variables. Although these elements prevented a quantitative comparison across models, we defined the most frequently used models given a specific outcome, providing useful indications for the application of more complex machine learning algorithms in rehabilitation medicine.

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction.

Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical data-based and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 [IQR = 4.56], 3.39 [IQR = 4.36], 3.16 [IQR = 4.13] for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Data-driven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decision-making process the practitioners must face.