Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation.

Good data quality is vital for personalising plans in rehabilitation. Machine learning (ML) improves prognostics but integrating it with Multiple Imputation (MImp) for dealing missingness is an unexplored field. This work aims to provide post-stroke ambulation prognosis, integrating MImp with ML, and identify the prognostic influential factors. Stroke survivors in intensive rehabilitation were enrolled. Data on demographics, events, clinical, physiotherapy, and psycho-social assessment were collected. An independent ambulation at discharge, using the Functional Ambulation Category scale, was the outcome. After handling missingness using MImp, ML models were optimised, cross-validated, and tested. Interpretability techniques analysed predictor contributions. Pre-MImp, the dataset included 54.1% women, 79.2% ischaemic patients, median age 80.0 (interquartile range: 15.0). Post-MImp, 368 non-ambulatory patients on 10 imputed datasets were used for training, 80 for testing. The random forest (the validation best-performing algorithm) obtained 75.5% aggregated balanced accuracy on the test set. The main predictors included modified Barthel index, Fugl-Meyer assessment/motricity index, short physical performance battery, age, Charlson comorbidity index/cumulative illness rating scale, and trunk control test. This is among the first studies applying ML, together with MImp, to predict ambulation recovery in post-stroke rehabilitation. This pipeline reliably exploits the potential of incomplete datasets for healthcare prognosis, identifying relevant predictors.

Electrocorticography and navigated transcranial magnetic stimulation-tailored supratotal resection for epileptogenic low-grade gliomas.

OBJECTIVE: Epilepsy is commonly associated with low-grade gliomas (LGGs), impacting patients' well-being. While resection is the primary treatment, seizures can persist postoperatively in 27%-55% of cases. The authors aimed to evaluate an electrocorticography (ECoG) and navigated transcranial magnetic stimulation (nTMS)-tailored supratotal resection (ETT-SpTR) for LGG in controlling seizures, preserving neurological function, and enhancing treatment effectiveness. METHODS: The authors retrospectively analyzed a prospectively enrolled cohort of patients with LGG presenting with epileptic seizures with ictal/interictal activity on electroencephalography (EEG) who underwent resective surgery. The authors performed preoperative nTMS to identify functional cortical areas. ECoG was used to guide the removal of the high-risk epilepsy cortical areas (HREAs). Patients were divided into two groups: group I, the control group, underwent gross-total resection alone, whereas group II patients underwent removal of HREAs identified by ECoG (ETT-SpTR). Resection avoided functionally eloquent areas as identified on nTMS, checked with cortical mapping. Postoperative seizure outcome was assessed using the Engel classification. RESULTS: Fifteen patients who underwent LGG resection between January and July 2023 were included. Among 24 identified nTMS-positive points, none were included in the resection. Overall, 73.3% of patients (11/15) showed positive intraoperative ECoG, with better outcomes in group II (85.7% Engel class IA) than in group I (25% Engel class IA) at the follow-up (p = 0.02, OR 0.5 [95% CI 0.035-7.10], RR 0.19 [95% CI 0.03-1.2]). Seizure control was significantly better in group II, with no notable differences in postoperative transient neurological deficits between the two groups (p = 0.45). No permanent neurological deficits were observed during follow-up. Statistical analysis revealed significant differences between the two groups (p < 0.05). CONCLUSIONS: This preliminary study affirms the predictive value of TMS for postoperative neurological status and safety in epileptic patients. Intraoperative ECoG effectively identified peritumoral HREAs. ETT-SpTR significantly improved epileptic outcomes, preserving functions without permanent neurological worsening. Additional resection targets the HREAs in the temporal, frontal, and parietal lobes.

Stroke-related epilepsy in the rehabilitation setting: Insights from the inpatient post-stroke rehabilitation study - RIPS.

In stroke survivors, persistent seizure activity could be associated with poor functional outcomes. At the same time, antiepileptic over-treatment could hamper post-stroke recovery. We systematically investigated the occurrence of seizures, the prevalence of epileptic discharges, and delta slow waves on electroencephalogram (EEG) and anti-seizure medication (ASM) management in relation to clinical manifestations and EEG abnormalities. This was a multi-centre prospective study involving two intensive rehabilitation units (IRUs). Clinical and EEG data were acquired at admission to the IRU, discharge (T1), and six-month follow-up (T2). A total of 163 patients underwent EEG recording upon admission to the IRU, while 149 were available for analysis at discharge from the IRU. Eighteen patients were treated with ASMs upon IRU admission despite only five of these patients having early seizures. Among the 145 patients not treated upon admission to the IRU, eight had late seizures, of which six were during the IRU stay, while two were after discharge from the IRU. During IRU stay, ASMs were generally discontinued in patients with no early seizures reported and were started in patients with late seizures. Among the 18 patients treated with ASMs at admission to the IRU, only six maintained the therapy also at T2. Our results suggest that post-acute inpatient rehabilitation is a proper setting to observe patients treated with ASMs after stroke and provide personalized post-stroke epilepsy management.

Brain tumor surgery guided by navigated transcranial magnetic stimulation mapping for arithmetic calculation.

OBJECTIVE: The onco-functional balance represents the primary goal in neuro-oncology. The increasing use of navigated transcranial magnetic stimulation (nTMS) allows the noninvasive characterization of cortical functional anatomy, and its reliability for motor and language mapping has previously been validated. Calculation and arithmetic processing has not been studied with nTMS so far. In this study, the authors present their preliminary data concerning nTMS calculation. METHODS: The authors designed a monocentric prospective study, adopting an internal protocol to use nTMS for preoperative planning, including arithmetic processing. When awake surgery was possible, according to the patients' conditions, nTMS points were used to guide direct cortical stimulation (DCS), i.e., the gold standard for cortical mapping. Navigated TMS-based tractography was used for surgical planning. Statistical analyses on the nTMS and DCS points were performed. RESULTS: From February 2021 to October 2023, 61 procedures for nTMS calculation mapping were performed. The clinical evaluation, including pre- and postoperative evaluations (3 months after surgery), demonstrated a good clinical outcome with preservation of arithmetic function and recovery (92.8% of patients). Between the awake and asleep surgery groups, the postoperative clinical results were comparable at the 3-month follow-up, with > 90% of the patients achieving improved calculation function. The surgical strategy adopted was aimed at sparing nTMS positive points in asleep procedures, whereas nTMS and DCS positive points were not removed in awake procedures. Overall, 62% of the positive points for calculation functions were exposed by craniotomy and 85% were spared during surgery. None of the patients developed nTMS-related seizures. Diffusion tensor imaging fiber tracking based on nTMS positive points for calculation was used. The white matter fiber tracts involved in calculation functions were the arcuate fasciculus (56%) and frontal aslant tract (22%). When nTMS and DCS points were compared in awake surgery (n = 10 patients), a sensitivity of 31.71%, specificity of 85.76%, positive predictive value of 22.41%, negative predictive value of 90.64%, and accuracy of approximately 69% were achieved. CONCLUSIONS: Based on the authors' preliminary data, nTMS can be an advantageous tool to study cognitive functions, aimed at minimizing neurological impairment. The postoperative clinical outcome for patients who underwent operation with nTMS was very good. Considering these results, nTMS has proved to be a feasible method to map cognitive areas including those for calculation functions. Further analyses are needed to validate these data. Finally, other cognitive functions (e.g., visuospatial) may be explored with nTMS.

Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus.

OBJECTIVE: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS-). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. METHODS: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/- patients by enrolling 57 MCS patients (MCS-: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs' metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (α,θ,δ). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/-. RESULTS: A lower level of brain activity integration was found in the MCS- group in the α band. Instead, in the δ band MCS- group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/- through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of 79% (sensitivity and specificity of 74% and 85% respectively). CONCLUSION: Despite tackling the MCS+/- differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. SIGNIFICANCE: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

A neurophysiological profiling of the heartbeat-evoked potential in severe acquired brain injuries: A focus on unconsciousness.

Unconsciousness in severe acquired brain injury (sABI) patients occurs with different cognitive and neural profiles. Perturbational approaches, which enable the estimation of proxies for brain reorganization, have added a new avenue for investigating the non-behavioural diagnosis of consciousness. In this prospective observational study, we conducted a comparative analysis of the topological patterns of heartbeat-evoked potentials (HEP) between patients experiencing a prolonged disorder of consciousness (pDoC) and patients emerging from a minimally consciousness state (eMCS). A total of 219 sABI patients were enrolled, each undergoing a synchronous EEG-ECG resting-state recording, together with a standardized consciousness diagnosis. A number of graph metrics were computed before/after the HEP (Before/After) using the R-peak on the ECG signal. The peak value of the global field power of the HEP was found to be significantly higher in eMCS patients with no difference in latency. Power spectrum was not able to discriminate consciousness neither Before nor After. Node assortativity and global efficiency were found to vary with different trends at unconsciousness. Lastly, the Perturbational Complexity Index of the HEP was found to be significantly higher in eMCS patients compared with pDoC. Given that cortical elaboration of peripheral inputs may serve as a non-behavioural determinant of consciousness, we have devised a low-cost and translatable technique capable of estimating causal proxies of brain functionality with an endogenous, non-invasive stimulus. Thus, we present an effective means to enhance consciousness assessment by incorporating the interaction between the autonomic nervous system (ANS) and central nervous system (CNS) into the loop.

Implications of the Consciousness State on Decannulation in Patients With a Prolonged Disorder of Consciousness.

OBJECTIVE: To prospectively investigate the evolution of the consciousness state and the cannula-weaning progression in patients with prolonged disorders of consciousness. DESIGN: Nonconcurrent cohort study. SETTING: A rehabilitation unit. PARTICIPANTS: Adult patients (N=144) with prolonged disorders of consciousness after a severe acquired brain injury admitted between June 2020 and September 2022. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Consciousness state was assessed by repeated Coma Recovery Scale-Revised (CRS-R) questionnaire administration at admission and weekly afterward. The dates of the first improvement of consciousness state and the achievement of decannulation were recorded. Decannulation followed an internal protocol of multiprofessional rehabilitation. RESULTS: One hundred forty-four patients were included: age, 69 years; 64 (44.4%) with hemorrhagic etiology; time post onset, 40 days, CRS-R score at admission, 9, median length of stay, 90 days. Seventy-three (50.7%) patients were decannulated. They showed a significantly higher CRS-R (P<.001) and states of consciousness (P<.001) at admission, at the first improvement of the consciousness state (P=.003), and at discharge (P<.001); a lower severity in the Cumulative Illness Rating Scale at admission (P=.01); and a lower rate of pulmonary infections with recurrence (P=.021), compared with nondecannulated patients. Almost all decannulated patients (97.3%) improved their consciousness before decannulation. Consciousness states at decannulation were as follows: unresponsive wakefulness syndrome, 0 (0%); minimally conscious state (MCS) minus, 4 (5.5%); MCS plus, 7 (9.6%); and emergence from MCS, 62 (84.9%). Kaplan-Meier analysis showed a significant divergence between the curves with a higher probability of decannulation in patients who improved consciousness (P<.001). CONCLUSIONS: This study showed that the presence of signs of consciousness, even subtle, is a necessary condition for decannulation, suggesting that consciousness may influence some of the components implied in the decannulation process.

Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG.

The identification of EEG biomarkers to discriminate Subjective Cognitive Decline (SCD) from Mild Cognitive Impairment (MCI) conditions is a complex task which requires great clinical effort and expertise. We exploit the self-attention component of the Transformer architecture to obtain physiological explanations of the model's decisions in the discrimination of 56 SCD and 45 MCI patients using resting-state EEG. Specifically, an interpretability workflow leveraging attention scores and time-frequency analysis of EEG epochs through Continuous Wavelet Transform is proposed. In the classification framework, models are trained and validated with 5-fold cross-validation and evaluated on a test set obtained by selecting 20% of the total subjects. Ablation studies and hyperparameter tuning tests are conducted to identify the optimal model configuration. Results show that the best performing model, which achieves acceptable results both on epochs' and patients' classification, is capable of finding specific EEG patterns that highlight changes in the brain activity between the two conditions. We demonstrate the potential of attention weights as tools to guide experts in understanding which disease-relevant EEG features could be discriminative of SCD and MCI.

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings.

INTRODUCTION: Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS: We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS: Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION: Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings. Highlights: Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.

Quantitative EEG and prognosis for recovery in post-stroke patients: The effect of lesion laterality.

OBJECTIVE: There is emerging confidence that quantitative EEG (qEEG) has the potential to inform clinical decision-making and guide individualized rehabilitation after stroke, but consensus on the best EEG biomarkers is needed for translation to clinical practice. This study investigates the spatial qEEG spectral and symmetry distribution in patients with a left/right hemispheric stroke, to evaluate their side-specific prognostic power in post-acute rehabilitation outcome. METHODS: Resting-state 19-channel EEG recordings were collected with clinical information on admission to intensive inpatient rehabilitation (within 30 days post stroke), and six months post stroke. After preprocessing, spectral (Delta-to-Alpha Ratio, DAR) and symmetry (pairwise and hemispheric Brain Symmetry Index) features were extracted. Patients were divided into Affected Right and Left (AR/AL) groups, according to the location of their lesion. Within each group, DAR was compared between homologous electrode pairs and the pairwise difference between pairs was compared across pairs in the scalp. Then, the prognostic power of qEEG admission metrics was evaluated by performing correlations between admission metrics and discharge mBI values. RESULTS: Fifty-two patients with hemorrhagic or ischemic stroke (20 females, 38.5 %, median age 76 years [IQR = 22]) were included in the study. DAR was significantly higher in the affected hemisphere for both AR and AL groups, and, a higher frontal (to posterior) asymmetry was found independent of the side of the lesion. DAR was found to be a prognostic marker of 6-months modified Barthel Index (mBI) only for the AL group, while hemispheric asymmetry did not correlate with follow-up outcomes in either group. DISCUSSION: While the presence of EEG abnormalities in the affected hemisphere of a stroke is well recognized, we have shown that the extent of DAR abnormalities seen correlates with disability at 6 months post stroke, but only for left hemispheric lesions. Routine prognostic evaluation, in addition to motor and functional scales, can add information concerning neuro-prognostication and reveal neurophysiological abnormalities to be assessed during rehabilitation.

The Italian version of Edinburgh Handedness Inventory: Translation, transcultural adaptation, and validation in healthy subjects.

Lateralization is a key aspect of brain architecture and handedness is its primary manifestation. The Edinburgh Handedness Inventory (EHI) and the laterality quotient (LQ) assess the direction and consistency of handedness and require translation and cross-cultural adaptation to guarantee construct validity. We developed a standardized Italian EHI version. The developed Italian version was tested on 202 Italian subjects, classified into three hand types based on their LQs: right, mixed, and left. The frequency of left-handedness in Italians and other populations was compared to previous data. LQs from the twenty- and the ten-item original inventories were also compared. We conducted a factorial analysis. Mcdonald's Omega tested internal consistency. The prevalence of left-handedness was 6.4%, consistent with prior findings in Italian samples and other EHI translations. Age was the only socio-demographic variable that significantly affected the LQ. The internal consistency of the Italian EHI was excellent. Handedness is a feature of several cognitive functions and some neuropsychological diseases; it is influenced by socio-demographic and cultural factors and the instrument used to assess it. To provide a consistent and comparable evaluation of the construct, we recommend using this validated Italian translation of the EHI.

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.

Depressive Symptoms Moderate the Association Between Functional Level at Admission to Intensive Post-Stroke Rehabilitation and Effectiveness of the Intervention.

INTRODUCTION: Previous studies showed that depression acts as an independent factor in functional recovery after stroke. In a prospective cohort of patients admitted to intensive inpatient rehabilitation after a stroke, we aimed to test depression as a moderator of the relationship between the functional level at admission and the effectiveness of rehabilitation at discharge. METHODS: All patients admitted to within 30 days from an ischemic or hemorrhagic stroke to 4 intensive rehabilitation units were prospectively screened for eligibility to a multicenter prospective observational study. Enrolled patients underwent an evidence-based rehabilitation pathway. We used clinical data collected at admission (T0) and discharge (T1). The outcome was the effectiveness of recovery at T1 on the modified Barthel Index (proportion of achieved over potential functional improvement). Moderation analysis was performed by using the PROCESS macro for SPSS using the bootstrapping procedure. RESULTS: Of 278 evaluated patients, 234 were eligible and consented to enrolment; 81 patients were able to answer to the Hospital Anxiety and Depression Scale (HADS) and were included in this analysis. The relationship between the functional status at admission and rehabilitation effectiveness was significant only in persons with fewer depressive symptoms; depression (HADS cut-off score: 5.9) moderated this relationship (P = .047), independent from age and neurological impairment. CONCLUSIONS: Our results suggest that depression moderates between the functional status at admission and the functional recovery after post-stroke rehabilitation. This approach facilitates the identification of subgroups of individuals who may respond differently to stroke rehabilitation based on depression.

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.

Heads-Up Micronavigation Reliability of Preoperative Transcranial Magnetic Stimulation Maps for the Motor Function: Comparison With Direct Cortical Stimulation.

BACKGROUND AND OBJECTIVES: We aimed to assess the reliability of preoperative navigated transcranial magnetic stimulation (nTMS) maps for motor function as visualized intraoperatively with augmented reality heads-up display and to assess its accuracy via direct point-by-point comparison with the gold-standard direct cortical stimulation (DCS). METHODS: From January 2022 to January 2023, candidates for surgical removal of lesions involving the motor pathways underwent preoperative nTMS assessment to obtain cortical maps of motor function. Intraoperatively and before tumor removal, nTMS maps were superimposed on the cortical surface, and DCS was performed on positive points with increasing current intensity until obtaining a positive response at 16 mA. The outcome of each stimulation was recorded to obtain discrimination metrics. RESULTS: Twelve patients were enrolled (5 females [42%] vs 7 males [58%], mean age 62.9 ± 12.8 years), for a total of 304 investigated points. Agreement between nTMS and DCS was moderate (κ = 0.43, P < .005), with 0.66 (0.53-0.78) sensitivity, 0.87 (0.82-0.90) specificity, 0.50 (0.39-0.62) positive predictive values, 0.93 (0.89-0.95) negative predictive value, and 0.83 (0.79-0.87) accuracy. A loss of accuracy was observed with higher DCS current intensities. CONCLUSION: We performed a point-by-point validation of preoperative nTMS maps for motor function using augmented reality visualization. The high negative predictive value and low positive predictive values highlight nTMS reliability to visualize safe cortical zones but not to identify critical functional areas, confirming previous findings of nTMS maps for the language function and suggesting the need for combined use of nTMS maps and DCS for optimal maximal safe resection.

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.

Is the Patient State Index a reliable parameter as guide to anaesthesiology in cranial neurosurgery? A first intraoperative study and a literature review.

BACKGROUND: Patient State Index (PSI) and Suppression Ratio (SR) are two indices calculated by quantitative analysis of EEG used to estimate the depth of anaesthesia but their validation in neurosurgery must be done. Our aim was to investigate the congruity PSI and SR with raw EEG monitoring in neurosurgery. METHODS: We included 34 patients undergoing elective cranial neurosurgery. Each patient was monitored by a SedLine device (PSI and SR) and by raw EEG. To appraise the agreement between PSI, SR and EEG Suppr%, Bland-Altman analysis was used. We also correlated the PSI and SR recorded at different times during surgery to the degree of suppression of the raw EEG data by Spearman's rank correlation coefficient. For a comparison with previous data we made an international literature review according to PRISMA protocol. RESULTS: At all recording times, we found that there is a strong agreement between PSI and raw EEG. We also found a significant correlation for both PSI and SR with the EEG suppression percentage (p < 0.05), but with a broad dispersion of the individual values within the confidence interval. CONCLUSION: The Masimo SedLine processed EEG monitoring system can be used as a guide in the anaesthetic management of patients during elective cranial neurosurgery, but the anaesthesiologist must be aware that previous correlations between PSI and SR with the suppression percentage may not always be valid in all individual patients. The use of an extended visual raw EEG evaluated by an expert electroencephalographer might help to provide better guidance.