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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients With Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study

Background

Due to continuous advances in intensive care technology and neurosurgical procedures, the number of survivors from severe acquired brain injuries (sABIs) has increased considerably, raising several delicate ethical issues. The heterogeneity and complex nature of the neurological damage of sABIs make the detection of predictive factors of a better outcome very challenging. Identifying the profile of those patients with better prospects of recovery will facilitate clinical and family choices and allow to personalize rehabilitation. This paper describes a multicenter prospective study protocol, to investigate outcomes and baseline predictors or biomarkers of functional recovery, on a large Italian cohort of sABI survivors undergoing postacute rehabilitation.

Methods

All patients with a diagnosis of sABI admitted to four intensive rehabilitation units (IRUs) within 4 months from the acute event, aged above 18, and providing informed consent, will be enrolled. No additional exclusion criteria will be considered. Measures will be taken at admission (T0), at three (T1) and 6 months (T2) from T0, and follow-up at 12 and 24 months from onset, including clinical and functional data, neurophysiological results, and analysis of neurogenetic biomarkers.

Statistics

Advanced machine learning algorithms will be cross validated to achieve data-driven prediction models. To assess the clinical applicability of the solutions obtained, the prediction of recovery milestones will be compared to the evaluation of a multiprofessional, interdisciplinary rehabilitation team, performed within 2 weeks from admission.

Discussion

Identifying the profiles of patients with a favorable prognosis would allow customization of rehabilitation strategies, to provide accurate information to the caregivers and, possibly, to optimize rehabilitation outcomes.

Conclusions

The application and validation of machine learning algorithms on a comprehensive pool of clinical, genetic, and neurophysiological data can pave the way toward the implementation of tools in support of the clinical prognosis for the rehabilitation pathways of patients after sABI.

Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution

COVID-19 cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the duration of the infection by means of information available at hospital admission. A total of 222 patients were enrolled in our observational study. Anagraphical and anamnestic data, COVID-19 signs and symptoms, COVID-19 therapy, hematochemical test results, and prior therapies administered to patients are used as predictors. A set of 55 features, all of which can be taken in the first hours of the patient’s hospitalization, was considered. Different solutions were compared achieving the best performance with a sequential convolutional neural network-based model merged in an ensemble with two different meta-learners linked in cascade. We obtained a median absolute error of 2.7 days (IQR = 3.0) in predicting the duration of the infection; the error was equally distributed in the infection duration range. This tool could preemptively give an outlook of the COVID-19 patients’ expected path and the associated hospitalization effort. The proposed solution could be viable in tackling the huge burden and the logistics complexity of hospitals or rehabilitation centers during the pandemic waves.

Critical issue on the extinction and inattention subtest of NIHSS scale: an analysis on post-acute stroke patients attending inpatient rehabilitation

Objectives

This study aims to evaluate the diagnostic performance of NIHSS extinction and inattention item, compared to the results of the Oxford Cognitive Screen (OCS) heart subtest. Additionally, the possible role of the NIHSS visual field subtest on the NIHSS extinction and inattention subtest performance is explored and discussed.

Methods

We analysed scores on NIHSS extinction and inattention subtest, NIHSS visual field subtest, and OCS heart subtest on a sample of 118 post-stroke patients.

Results

Compared to OCS heart subtest, the results on NIHSS extinction and inattention subtest showed an accuracy of 72.9% and a moderate agreement level (Cohen’s kappa = 0.404). Furthermore, a decrease in NIHSS accuracy detecting neglect (61.1%) was observed in patients with pathological scores in NIHSS visual field item.

Conclusions

Extreme caution is recommended for the diagnostic performance of extinction and inattention item of NIHSS. Signs of neglect may not be detected by NIHSS, and may be confused with visual field impairment.

Trial registration

This study refers to an observational study protocol submitted to ClinicalTrials.gov with identifier: NCT03968627. The name of the registry is “Development of a National Protocol for Stroke Rehabilitation in a Multicenter Italian Institution” and the date of the registration is the 30th May 2019.

Mortality and characteristics of older people dying with COVID-19 in Lombardy nursing homes, Italy: An observational cohort study

BACKGROUND

The aim of the study was to describe the epidemiological characteristics of Nursing Homes (NHs) residents infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and to compute the related case-fatality rate.

MATERIALS AND METHODS

The outcomes were mortality and case-fatality rate with related epidemiological characteristics (age, sex, comorbidity, and frailty).

RESULTS

During the COVID-19 outbreak lasted from March 1 to May 7, 2020, 330 residents died in Fondazione Don Gnocchi NHs bringing the mortality rate to 27% with a dramatic increase compared to the same period of 2019, when it was 7.5%. Naso/oropharyngeal swabs resulted positive for COVID-19 in 315 (71%) of the 441of the symptomatic/exposed residents tested. The COVID-19 population was 75% female, with a 17% overall fatality rate and sex-specific fatality rates of 19% and 13% for females and males, respectively. Fifty-six percent of deaths presented SARS-CoV-2-associated pneumonia, 15% cardiovascular, and 29% miscellaneous pathologies.

CONCLUSION

Patients' complexity and frailty might influence SARS-CoV-2 infection case-fatality rate estimates. A COVID-19 register is needed to study COVID-19 frail patients' epidemiology and characteristics.

Data-driven prediction of decannulation probability and timing in patients with severe acquired brain injury

BACKGROUND AND OBJECTIVES

From a rehabilitation perspective, removal of tracheostomy in patients with severe acquired brain injuries (sABI) is a crucial step. Predictive parameters for a successful decannulation are currently still a focus of the research for sABI patients, especially for those presenting a disorder of consciousness. For this reason, we adopted a data-driven approach predicting decannulation probability and timing using ensemble learning models in patients in intensive rehabilitation units.

METHODS

327 patients, 186 of which were successfully decannulated during their intensive rehabilitative stay, were recruited in a non-concurrent retrospective study. Decannulation probability and timing were predicted using data available within one week from admission at the rehabilitation unit. Two predictive models were trained and cross-validated independently, with the first being an ensemble of a Support Vector Machine and Random Forests and the second an Adaptive Boosting with a Support Vector Regression as weak learner. Confusion matrix, accuracy and AUC were considered as evaluation metrics for the classifier and median absolute error was considered for the regressor. To quantify the advantages in the clinical practice of using the latter prediction, we compared timing estimation with a timing guess (median) calculated on available data. The comparison was based on a Wilcoxon signed rank test.

RESULTS

Decannulation probability was successfully predicted with an accuracy of 84.8% (AUC = 0.85) and timing with a median absolute error of 25.7 days [IQR = 25.6]. This resulted in a significant improvement with respect to the weaning time guess (p<0.05) with an effect size of 71.7%. Furthermore, dichotomizing the regression prediction with a threshold (3 months from the event), resulted in a prediction accuracy of 77.5% (AUC = 0.82) on the test set.

DISCUSSIONS

A model capable of providing a prediction on decannulation probability and timing was developed and cross-validated, built on data taken at admission to the intensive rehabilitation unit. Translated in clinical practice, this information can support the clinical decision process and provide a mean to improve both in-hospital and domiciliary care organization.

Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol

Background: The complex nature of stroke sequelae, the heterogeneity in rehabilitation pathways, and the lack of validated prediction models of rehabilitation outcomes challenge stroke rehabilitation quality assessment and clinical research. An integrated care pathway (ICP), defining a reproducible rehabilitation assessment and process, may provide a structured frame within investigated outcomes and individual predictors of response to treatment, including neurophysiological and neurogenetic biomarkers. Predictors may differ for different interventions, suggesting clues to personalize and optimize rehabilitation. To date, a large representative Italian cohort study focusing on individual variability of response to an evidence-based ICP is lacking, and predictors of individual response to rehabilitation are largely unexplored. This paper describes a multicenter study protocol to prospectively investigate outcomes and predictors of response to an evidence-based ICP in a large Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation.

Methods: All patients with diagnosis of ischemic or hemorrhagic stroke confirmed both by clinical and brain imaging evaluation, admitted to four intensive rehabilitation units (adopting the same stroke rehabilitation ICP) within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled (expected sample: 270 patients). Measures will be taken at admission (T0), at discharge (T1), and at follow-up 6 months after a stroke (T2), including clinical data, nutritional, functional, neurological, and neuropsychological measures, electroencephalography and motor evoked potentials, and analysis of neurogenetic biomarkers.

Statistics: In addition to classical multivariate logistic regression analysis, advanced machine learning algorithms will be cross-validated to achieve data-driven prognosis prediction models.

Discussion: By identifying data-driven prognosis prediction models in stroke rehabilitation, this study might contribute to the development of patient-oriented therapy and to optimize rehabilitation outcomes.

Clinical Trial Registration:ClinicalTrials.gov, NCT03968627. https://www.clinicaltrials.gov/ct2/show/NCT03968627?term=Cecchi&cond=Stroke&draw=2&rank=2.

Fluidic Haptic Interface for Mechano-Tactile Feedback

Notable advancements have been achieved in providing amputees with sensation through invasive and non-invasive haptic feedback systems such as mechano-, vibro-, electro-tactile and hybrid systems. Purely mechanical-driven feedback approaches, however, have been little explored. In this paper, we now created a haptic feedback system that does not require any external power source (such as batteries) or other electronic components (see Fig. 1 ). The system is low-cost, lightweight, adaptable and robust against external impact (such as water). Hence, it will be sustainable in many aspects. We have made use of latest multi-material 3D printing technology (Stratasys Objet500 Connex3) being able to fabricate a soft sensor and a mechano-tactile feedback actuator made of a rubber (TangoBlack Plus) and plastic (VeroClear) material. When forces are applied to the fingertip sensor, fluidic pressure inside the system acts on the membrane of the feedback actuator resulting in mechano-tactile sensation. Our [Formula: see text] feedback actuator is able to transmit a force range between 0.2 N (the median touch threshold) and 2.1 N (the maximum force transmitted by the feedback actuator at a 3 mm indentation) corresponding to force range exerted to the fingertip sensor of 1.2-18.49 N.