Assessment Of Stroke Patients Research Articles

The Minimal assessment Protocol for Cerebral Stroke 2020 (PMIC2020): a multicenter feasibility study in post-stroke inpatient rehabilitation.

In 2008, a Working Group of the Italian Society of Physical and Rehabilitation Medicine (SIMFER) published the first minimum protocol for assessing stroke patients (PMIC) to define functional needs and outcomes. The recent PMIC revision (PMIC2020) introduces a document for all rehabilitation settings, incorporating updated measurement tools. The aim of this study was to investigate the PMIC2020 feasibility and administration time (AT) in post-stroke inpatients and to examine the influence of demographic and clinical variables on AT. Multicenter prospective observational study. Eight Italian rehabilitation centers for post-acute inpatients. Adult patients consecutively admitted to rehabilitation after ischemic/hemorrhagic stroke, reporting the first event or recurrence, with a modified Barthel Index (mBI)<75 points, without cognitive impairment and clinical instability. PMIC2020 was administered at admission (T0) and discharge (T1), recording AT of each section/ tool. A feasibility questionnaire was administered to assessors. Univariate and multivariate analyses were conducted to investigate the effect of demographics and clinical variables on AT. One hundred fifty-one subjects were enrolled at T0 and 139 at T1; the mean±SD AT (seconds) was 1634±401 at T0 and 1087±360 at T1 (P<0.001). National Institute of Health-Stroke Scale and Mini-Mental State Examination required the highest AT. All but two scales had significantly lower AT at T1 (P<0.05). Severe disability (as measured by mBI) was associated with higher AT than either complete or minimal/absent disability. The feasibility questionnaire showed good PMIC2020 appraisal by assessors without relevant critical issues. PMIC2020 was feasible in post-acute inpatient rehabilitation settings. No relevant critical issue was raised by users. Even though more comprehensive than PMIC, PMIC2020 required only slightly more AT (27 minutes at T0 and 18 minutes at T1, on average); more AT was needed to assess patients with severe disability. The study has immediate transferability for the National Health Service, as PMIC2020 can be routinely implemented in clinical practice and research to assess stroke patients' needs and outcomes. The updated measures allow more immediate comparisons with international data on stroke rehabilitation. Future research should investigate the PMIC2020 feasibility in other rehabilitation settings and its relevance in predicting stroke rehabilitation needs and outcomes.

Research on the precise recognition stage of cerebral microhemorrhage based on deep learning algorithm

Cerebral microbleeds (CMB) is an important type of cerebral microbleeds. In recent years, many studies have proved that CMB can not only cause vascular dementia, but also increase the risk of stroke. Therefore, detection of CMB is of great clinical significance for balancing antithrombotic therapy and risk assessment in stroke patients, and detection of CMB is of great value for diagnosis and prognosis of cranial injury. This paper mainly proposes a two-stage CMB detection framework based on deep learning, which includes the screening stage of brain microhemorrhagic candidate points and the recognition stage of brain microhemorrhagic points based on deep learning. Firstly, in the first stage, we screened CMB candidate points by combining rapid radial transformation and threshold segmentation, and excluded a large number of background regions and obvious non-CMB regions. Then, in the second stage, the two-channel images spliced by sensitivity weighted imaging (SWI) and phase diagram (Pha) were used for false positive judgment by 3D convolutional neural network to distinguish the true CMB from the CMB analog.

The clinical and imaging data fusion model for single-period cerebral CTA collateral circulation assessment.

The Chinese population ranks among the highest globally in terms of stroke prevalence. In the clinical diagnostic process, radiologists utilize computed tomography angiography (CTA) images for diagnosis, enabling a precise assessment of collateral circulation in the brains of stroke patients. Recent studies frequently combine imaging and machine learning methods to develop computer-aided diagnostic algorithms. However, in studies concerning collateral circulation assessment, the extracted imaging features are primarily composed of manually designed statistical features, which exhibit significant limitations in their representational capacity. Accurately assessing collateral circulation using image features in brain CTA images still presents challenges. To tackle this issue, considering the scarcity of publicly accessible medical datasets, we combined clinical data with imaging data to establish a dataset named RadiomicsClinicCTA. Moreover, we devised two collateral circulation assessment models to exploit the synergistic potential of patients' clinical information and imaging data for a more accurate assessment of collateral circulation: data-level fusion and feature-level fusion. To remove redundant features from the dataset, we employed Levene's test and T-test methods for feature pre-screening. Subsequently, we performed feature dimensionality reduction using the LASSO and random forest algorithms and trained classification models with various machine learning algorithms on the data-level fusion dataset after feature engineering. Experimental results on the RadiomicsClinicCTA dataset demonstrate that the optimized data-level fusion model achieves an accuracy and AUC value exceeding 86% . Subsequently, we trained and assessed the performance of the feature-level fusion classification model. The results indicate the feature-level fusion classification model outperforms the optimized data-level fusion model. Comparative experiments show that the fused dataset better differentiates between good and bad side branch features relative to the pure radiomics dataset. Our study underscores the efficacy of integrating clinical and imaging data through fusion models, significantly enhancing the accuracy of collateral circulation assessment in stroke patients.

Assessment of Stroke Patients' Muscle Strength Following a Combination of Mirror Therapy and ROM (Range of Motion) Exercises Based on Self-Care Theory

Background: When blood flow problems kill off some brain cells, the result is a neurological deficiency known as a stroke. There have been 507 reported cases of stroke in East Java, while the overall stroke prevalence in Indonesia has jumped from 7% to 10.9% (Riskesdas, 2020). The researchers at RSI Darus Syifa's Surabaya set out to determine how much of an impact self-care theory-based mirror therapy and range-of-motion exercises had on the muscular strength of stroke patients admitted to the inpatient unit. Methods: A true experimental with a pretest-posttest control group design was utilised in this type of research. One hundred and ten stroke patients made up the study's population. There were a total of 34 participants in this study, with 17 serving as control subjects and 17 as interventionists. Sampling was based on probability sampling using simple random sampling. The Medical Research Council (MRC) scale was utilised by the device to measure muscular strength. Mann-Whitney and the Wilcoxon Signet Rank Test were used to examine the collected data. Results: In the inpatient ward of the Darus Syifa Surabaya Hospital, stroke patients who undergo a combination of mirror therapy and range of motion exercises based on self-care theory show a significant improvement in muscle strength, according to the Mann-Whitney test (p &lt; 0.05). Conclusion: The study found that stroke patients' muscle strength improved with a combination of mirror therapy and range of motion exercises grounded in self-care theory.

Exploring Stroke Patients' Needs: A Cultural Adaptation and Validation of the Modified Needs Assessment Questionnaire in a Greek Context.

Stroke survivors often face diverse unmet needs highlighting the significance of identifying and addressing these needs to enhance rehabilitation outcomes and overall quality of life. This study aimed to validate the modified Needs Assessment Questionnaire (mNAQ) as a reliable and valid tool for assessing the needs of stroke patients in the Greek context. Additionally the research sought to identify potential differences in the assessment of stroke patients' needs based on their stroke phase and National Institutes of Health Stroke Scale (NIHSS) scores. A sample of 71 adult stroke survivors adhering to World Health Organization guidelines and providing autonomous consent participated in the study. The mNAQ comprising 141 items across 12 domains was utilized to evaluate stroke patients' needs. The NIHSS and Barthel Index (BI) were employed for functional independence and mobility assessment. Data analysis incorporated confirmatory factor analysis, exploratory factor analysis and Cronbach's reliability analysis to establish construct validity and internal consistency. Concurrent and known-groups validity analyses were conducted; and Spearman's rho correlation explored the relationship between mNAQ and BI scores. Non-parametric analyses were applied to identify differences based on stroke phase and NIHSS scores. The study revealed that the mNAQ initially lacked satisfactory psychometric properties in the Greek context. Subsequent modifications guided by confirmatory and exploratory factor analyses resulted in a refined three-factor scale encompassing 31 items in the domains of communication, mobility, and social functioning needs. This adapted measure effectively differentiated between acute and chronic stroke patients and those with minor and moderate strokes. In conclusion, the validated 31-item Greek mNAQ emerges as a crucial tool for comprehensively assessing the needs of stroke patients. Its application holds promise for optimizing post-stroke care improving functional outcomes and ultimately enhancing the overall well-being and quality of life for stroke survivors.

A Chinese version of the Stroke Illness Perception Questionnaire-Revised: Reliability and validity assessment in stroke patients.

Illness perceptions are an important factor affecting the prognosis of stroke patients. Evaluating the illness perceptions of stroke patients is of great importance for predicting their health behaviour and rehabilitation outcomes. However, there is no specific tool for assessing illness perceptions in stroke patients in China. The objective of this study is to translate the Stroke Illness Perception Questionnaire-Revised (SIPQ-R) into Chinese and to psychometrically test the Chinese version of the scale in the population of Chinese stroke patients. This was a methodological study. We investigated 593 stroke patients in the neurology department of a hospital in China from March to September 2021. We translated the SIPQ-R and adapted it to the cultural context, after which we evaluated the reliability and validity of the Chinese version of SIPQ-R. Exploratory factor analysis identified eight common factors that accounted for 71.74% of the total variance, and the factor loadings ranged from 0.530 to 0.933. Confirmatory factor analysis confirmed the eight-factor structure (χ2/df =1.765, root mean square error of approximation =0.053, incremental fit index =0.906, comparative fit index =0.905 and Tucker-Lewis index =0.900). Internal consistency was confirmed by a Cronbach's alpha coefficient of 0.982. The test-retest reliability was 0.762. The results showed good content validity (the scale level content validity index was 0.940, and the item level content validity index values ranged from 0.860 to 0.960). There were no missing responses and floor or ceiling effects. The standard error of measurement and the smallest detectable change for the SIPQ-R were 45.49 and 126.10, respectively. The results of this study provide empirical evidence for the reliability and validity of the Chinese version of the SIPQ-R for stroke patients.

Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion–exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

Interrater reliability of the Fugl-Meyer Motor assessment in stroke patients: a quality management project within the ESTREL study.

The Fugl-Meyer Motor Assessment (FMMA) is recommended for evaluating stroke motor recovery in clinical practice and research. However, its widespread use requires refined reliability data, particularly across different health professions. We therefore investigated the interrater reliability of the FMMA scored by a physical therapist and a physician using video recordings of stroke patients. The FMMA videos of 50 individuals 3 months post stroke (28 females, mean age 71.64 years, median National Institutes of Health Stroke Scale score 3.00) participating in the ESTREL trial (Enhancement of Stroke Rehabilitation with Levodopa: a randomized placebo-controlled trial) were independently scored by two experienced assessors (i.e., a physical therapist and a physician) with specific training to ensure consistency. As primary endpoint, the interrater reliability was calculated for the total scores of the entire FMMA and the total scores of the FMMA for the upper and lower extremities using intraclass correlation coefficients (ICC). In addition, Spearman's rank order correlation coefficients (Spearman's rho) were calculated for the total score and subscale levels. Secondary endpoints included the FMMA item scores using percentage agreement, weighted Cohen's kappa coefficients, and Gwet's AC1/AC2 coefficients. ICCs were 0.98 (95% confidence intervals (CI) 0.96-0.99) for the total scores of the entire FMMA, 0.98 (95% CI 0.96-0.99) for the total scores of the FMMA for the upper extremity, and 0.85 (95% CI 0.70-0.92) for the total scores of the FMMA for the lower extremity. Spearman's rho ranged from 0.61 to 0.94 for total and subscale scores. The interrater reliability at the item level of the FMMA showed (i) percentage agreement values with a median of 77% (range 44-100%), (ii) weighted Cohen's kappa coefficients with a median of 0.69 (range 0.00-0.98) and (iii) Gwet's AC1/AC2 coefficients with a median of 0.84 (range 0.42-0.98). The FMMA appears to be a highly reliable measuring instrument at the overall score level for assessors from different health professions. The FMMA total scores seem to be suitable for the quantitative measurement of stroke recovery in both clinical practice and research, although there is potential for improvement at the item level.

In-hospital stroke protocol outcomes before and after the implementation of neurological assessments by telemedicine: an observational case-control study.

Stroke is the second leading cause of global adult mortality and the primary cause of disability. A rapid assessment by a neurologist for general and reperfusion treatments in ischemic strokes is linked to decreased mortality and disability. Telestroke assessment is a strategy that allows for neurological consultations with experienced professionals, even in remote emergency contexts. No randomized studies have compared face-to-face neurological care outcomes with telestroke care. Whether neurologists in an institution achieve better results remotely than in person is also unknown. This study aimed to compare mortality and other outcomes commonly measured in stroke protocols for stroke patients assessed by a neurologist via face-to-face evaluations and telestroke assessment. Observational single-center retrospective study from August/2009 to February/2022, enrolling 2,689 patients with ischemic stroke, subarachnoid hemorrhage, and intracerebral hemorrhage. Group 1 (G1) comprised 2,437 patients with in-person neurological assessments, and Telemedicine Group 2 (G2) included 252 patients. The in-person group had higher admission NIHSS scores (G1, 3 (0; 36) vs. G2, 2 (0; 26), p < 0.001). The door-to-groin puncture time was lower in the in-person group than in the telestroke group (G1, 103 (42; 310) vs. G2, 151 (109; 340), p < 0.001). The telestroke group showed superior metrics for door-to-imaging time, symptomatic hemorrhagic transformation rate in ischemic stroke patients treated with intravenous thrombolysis, hospital stay duration, higher rates of intravenous thrombolysis and mechanical thrombectomy, and lower mortality. Symptomatic hemorrhagic transformation rate was smaller in the group evaluated via telestroke (G1, 5.1% vs. G2, 1.1%, p = 0.016). Intravenous thrombolysis and mechanical thrombectomy rates were significantly higher in telestroke group: (G1, 8.6% vs. G2, 18.2%, p < 0.001 and G1, 5.1% vs. G2, 10.4%, p = 0.002, respectively). Mortality was lower in the telestroke group than in the in-person group (G1, 11.1% vs. G2, 6.7%, p = 0.001). The percentage of patients with an mRS score of 0-2 at discharge was similar in both groups when adjusting for NIHSS score and age. The same neurological emergency team may assess stroke patients in-person or by telemedicine, with excellent outcome metrics. This study reaffirms telestroke as a safe tool in acute stroke care.

Abstract WP51: Bridging the Gap - Impact of Advanced Practice Nurse in the Community Hospital in Singapore

Background: Stroke is a debilitating condition and patients with acute stroke usually stays about a week in the acute hospital. Those patients with impaired physical function may be transferred to a Community Hospital (CH) for further rehabilitation to optimize their function. In this pilot project, we aimed to incorporate the Stroke Advanced Practice Nurses (APNs) in one of the Community Hospital (OCH) so that stroke care gaps can be identified and to optimize stroke care. Methodology: The Stroke APN obtained their clinical privileges to work in CH. The APN would identify stroke patients admitting to CH for further rehabilitation. APN performed a weekly consultation round to review stroke patients identified in CH. During the consultation round, the APN assessed stroke patients’ neurological function and stroke complications. She also reviewed patient’s vital signs, fluid balances, pending blood results, medications and management. With the information, the APN would recommend change in management accordingly after communicating with the neurology team from the original transferring acute hospital. Results: There were total of 51 issues identified from Aug 2020 to Aug 2021 from 125 patients were reviewed by the Stroke APNs. The Stroke APNs identified 27.5% (n= 14) medical related issues e.g. change in stroke etiology, lack of deep vein thrombosis prophylaxis. There were pharmacological changes in 21.5% (n= 11) of the patients reviewed. Drug changes were usually anti-hypertensives or anti-thrombotics. Post-stroke psychological issue was common and 21.5% (n= 11) of patients developed low mood. To empower patients with their own health, the Stroke APN had to reinforce stroke education in 13.7% (n= 7) of the patients and 7.8% (n= 4) required reinforcement of driving advice. 7 patients (13.7%) requested Information about community resources for them to transit back into the community. Outpatient appointments were streamlined and 7.8% (n= 4) had their appointment has been amended or cancelled. Conclusion: There were high incidence of issues detected during the Stroke APN visiting consultation. The Stroke APN played an important role to manage post-stroke patients appropriately in the CH.

Early identification of stroke through deep learning with multi-modal human speech and movement data.

JOURNAL/nrgr/04.03/01300535-202501000-00031/figure1/v/2024-05-14T021156Z/r/image-tiff Early identification and treatment of stroke can greatly improve patient outcomes and quality of life. Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale (CPSS) and the Face Arm Speech Test (FAST) are commonly used for stroke screening, accurate administration is dependent on specialized training. In this study, we proposed a novel multimodal deep learning approach, based on the FAST, for assessing suspected stroke patients exhibiting symptoms such as limb weakness, facial paresis, and speech disorders in acute settings. We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements, facial expressions, and speech tests based on the FAST. We compared the constructed deep learning model, which was designed to process multi-modal datasets, with six prior models that achieved good action classification performance, including the I3D, SlowFast, X3D, TPN, TimeSformer, and MViT. We found that the findings of our deep learning model had a higher clinical value compared with the other approaches. Moreover, the multi-modal model outperformed its single-module variants, highlighting the benefit of utilizing multiple types of patient data, such as action videos and speech audio. These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke, thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Outcome Measures for Shoulder Joint Assessment in Patients with Stroke: A Narrative Review

Objective: Understanding the influence of evidence-based practice on rehabilitating patients with stroke can be achieved through the use of suitable outcome measures. Aim: The purpose of this study is to consolidate the existing outcome measures related to the Shoulder Joint assessment in patients with stroke. This review aims to provide education to neurologists, physiotherapists, academics, and researchers regarding available resources for diagnosing, prognosing, and rehabilitating shoulder joint in stroke patients. Materials and Methods: Electronic searches were performed on databases including Medline, PubMed, Cochrane, Physiotherapy Evidence Database (PEDro) and Google Scholar using terms related to outcome measures utilised in the diagnosis, prognosis, and rehabilitation of shoulder joint in stroke patients. Results: Total seven outcome measures {Stroke Specific Shoulder Disability Index (SSSDI), Fugl Meyer Assessment of Upper Extremity, Motor Assessment Scale, Chedoke-McMaster Stroke Assessment, Orpington Prognostic Scale, Motricity Index, and Stroke Rehabilitation Assessment of Movement} were included in this study used in the diagnosis, prognosis, and rehabilitation of upper extremity in shoulder joints having shoulder domains were identified. SSSDI and Fugl Meyer Assessment of Upper Extremity (FMA-UE) have excellent reliability and good validity among them. But, FMA-UE does not measure the functional assessment of shoulder joint. Currently, among the scales used for shoulder assessment in stroke patients, only the SSSDI covers all domains comprehensively, setting it apart from other scales. Conclusion: The SSSDI is a single tool available for assessing the shoulder domains in stroke patients having excellent reliability

The cultural and linguistic adaptation of the Oxford Cognitive Screen to Tamil.

The Oxford Cognitive Screen (OCS) is a screening tool to assess stroke patients for deficits in attention, executive functions, language, praxis, numeric cognition, and memory. In this study, the OCS was culturally and linguistically adapted to Tamil, for use in India (OCS TA), considering the differences between formal and spoken versions of Tamil and consideration of its phonetic complexity. We adopted two-parallel form versions of the OCS and generated normative data for them. We recruited 181 healthy controls (Mean = 39.27 years, SD 16.52) (141 completed version A, 40 completed version B, 33 completed version A and B) and compared the data with the original UK normative sample. In addition, 28 native Tamil-speaking patients who had a stroke in the past three years (Mean = 62.76 years, SD 9.14) were assessed. Convergent validity was assessed with subtasks from Addenbrooke's Cognitive Examination III (ACE-III). We found significant differences between the UK normative group and the OCS TA normative group in age and education. Tamil-specific norms were used to adapt the cutoffs for the memory, gesture imitation, and executive function tasks. When domain-specific scores on the ACE-III were compared, OCS TA exhibited strong convergent validity. The OCS TA has shown the potential to be a useful screening tool for stroke survivors among Tamil speakers with the two-parallel forms demonstrating good equivalence. Further empirical evidence from larger studies is required to establish their psychometric performance and clinical validity.

Artificial intelligence applied in acute ischemic stroke: from child to elderly.

This review will summarize artificial intelligence developments in acute ischemic stroke in recent years and forecasts for the future. Stroke is a major healthcare concern due to its effects on the patient's quality of life and its dependence on the timing of the identification as well as the treatment. In recent years, attention increased on the use of artificial intelligence (AI) systems to help categorize, prognosis, and to channel these patients toward the right therapeutic procedure. Machine learning (ML) and in particular deep learning (DL) systems using convoluted neural networks (CNN) are becoming increasingly popular. Various studies over the years evaluated the use of these methods of analysis and prediction in the assessment of stroke patients, and at the same time, several applications and software have been developed to support the neuroradiologists and the stroke team to improve patient outcomes.

Nutritional Assessment in Stroke Patients

Stroke is a common acute neurological disorder that leads to significant morbidity and mortality worldwide. Malnutrition is a frequent problem among stroke patients, with a prevalence of approximately 20% at the time of hospital admission.

Test-retest reliability of fNIRS in resting-state cortical activity and brain network assessment in stroke patients.

Resting-state functional near infrared spectroscopy (fNIRS) scanning has attracted considerable attention in stroke rehabilitation research in recent years. The aim of this study was to quantify the reliability of fNIRS in cortical activity intensity and brain network metrics among resting-state stroke patients, and to comprehensively evaluate the effects of frequency selection, scanning duration, analysis and preprocessing strategies on test-retest reliability. Nineteen patients with stroke underwent two resting fNIRS scanning sessions with an interval of 24 hours. The haemoglobin signals were preprocessed by principal component analysis, common average reference and haemodynamic modality separation (HMS) algorithm respectively. The cortical activity, functional connectivity level, local network metrics (degree, betweenness and local efficiency) and global network metrics were calculated at 25 frequency scales × 16 time windows. The test-retest reliability of each fNIRS metric was quantified by the intraclass correlation coefficient. The results show that (1) the high-frequency band has higher ICC values than the low-frequency band, and the fNIRS metric is more reliable than at the individual channel level when averaged within the brain region channel, (2) the ICC values of the low-frequency band above the 4-minute scan time are generally higher than 0.5, the local efficiency and global network metrics reach high and excellent reliability levels after 4 min (0.5 < ICC < 0.9), with moderate or even poor reliability for degree and betweenness (ICC < 0.5), (3) HMS algorithm performs best in improving the low-frequency band ICC values. The results indicate that a scanning duration of more than 4 minutes can lead to high reliability of most fNIRS metrics when assessing low-frequency resting brain function in stroke patients. It is recommended to use the global correction method of HMS, and the reporting of degree, betweenness and single channel level should be performed with caution. This paper provides the first comprehensive reference for resting-state experimental design and analysis strategies for fNIRS in stroke rehabilitation.

Application of Prognostic Nutritional Index in the Predicting of Prognosis in Young Adults with Acute Ischemic Stroke

The incidence of ischemic stroke in young adults (18-45years old) is increasing gradually. However, performing nutritional assessment in stroke patients is often challenging due to the lack of an accepted standard for nutritional assessment. Two hundred sixty young stroke patients were recruited in this study and 144 cases in the good prognosis group and 116 cases in the poor prognosis group were scored according to the modified Rankin scale 90days after treatment. The National Institutes of Health Stroke Scale was performed on admission and discharge of patients. Serum interleukin 6 and high-sensitivity C-reactive protein were detected at patient presentation. The Prognostic Nutritional Index (PNI) was assessed on admission. Calculation formula of PNI score: serum albumin (g/L)+5× total lymphocyte count (109/L). The PNI at admission of young stroke patients with poor prognosis was higher than that of those with good prognosis. PNI at admission was significantly negatively correlated with National Institutes of Health Stroke Scale at discharge and modified Rankin scale score after 90days in young stroke patients. PNI at admission was also significantly negatively correlated with serum levels of high-sensitivity C-reactive protein and interleukin -6 at admission. PNI has a statistically predictive value for the 90-day prognosis of young stroke patients.

Stroke mimics in a Brazilian stroke center: a retrospective cohort in a Brazilian stroke unit

This study assessed stroke patients in a Brazilian unit to describe the prevalence, clinical presentation, etiology, and treatment outcomes of cases initially diagnosed as stroke mimics (SM). SM refers to conditions that mimic stroke but are not caused by cerebrovascular events. Misdiagnosis can lead to unnecessary and potentially harmful treatments. The study examined patients admitted between October 2012 and September 2013. Among patients with symptoms onset within four hours, 7.1% (10 patients) were classified as SM. Six of these patients received intravenous thrombolysis, a treatment for acute ischemic stroke. The main clinical presentations among SM cases were motor symptoms (90%), dysarthria, and sensory symptoms (60%). The etiology of SM cases varied, with functional disorders being the most common (60%). The findings reveal a significant percentage of patients admitted to the stroke unit were ultimately diagnosed as SM, highlighting the need for improved diagnostic strategies to differentiate between stroke and SM. The study underscores the importance of developing well-defined guidelines to assess SM risk and utilize imaging methods for distinguishing between stroke and SM. Further research is necessary to address these issues and enhance stroke diagnosis accuracy.

Computed tomography derived cervical fat-free muscle fraction as an imaging-based outcome marker in patients with acute ischemic stroke: a pilot study

BackgroundOutcome assessment in stroke patients is essential for evidence-based stroke care planning. Computed tomography (CT) is the mainstay of diagnosis in acute stroke. This study aimed to investigate whether CT-derived cervical fat-free muscle fraction (FFMF) as a biomarker of muscle quality is associated with outcome parameters after acute ischemic stroke.MethodsIn this retrospective study, 66 patients (mean age: 76 ± 13 years, 30 female) with acute ischemic stroke in the anterior circulation who underwent CT, including CT-angiography, and endovascular mechanical thrombectomy of the middle cerebral artery between August 2016 and January 2020 were identified. Based on densitometric thresholds, cervical paraspinal muscles covered on CT-angiography were separated into areas of fatty and lean muscle and FFMF was calculated. The study cohort was binarized based on median FFMF (cutoff value: < 71.6%) to compare clinical variables and outcome data between two groups. Unpaired t test and Mann-Whitney U test were used for statistical analysis.ResultsNational Institute of Health Stroke Scale (NIHSS) (12.2 ± 4.4 vs. 13.6 ± 4.5, P = 0.297) and modified Rankin scale (mRS) (4.3 ± 0.9 vs. 4.4 ± 0.9, P = 0.475) at admission, and pre-stroke mRS (1 ± 1.3 vs. 0.9 ± 1.4, P = 0.489) were similar between groups with high and low FFMF. NIHSS and mRS at discharge were significantly better in patients with high FFMF compared to patients with low FFMF (NIHSS: 4.5 ± 4.4 vs. 9.5 ± 6.7; P = 0.004 and mRS: 2.9 ± 2.1 vs.3.9 ± 1.8; P = 0.049). 90-day mRS was significantly better in patients with high FFMF compared to patients with low FFMF (3.3 ± 2.2 vs. 4.3 ± 1.9, P = 0.045).ConclusionCervical FFMF obtained from routine clinical CT might be a new imaging-based muscle quality biomarker for outcome prediction in stroke patients.

Direction-dependency of the kinematic indices in upper extremities motor assessment of stroke patients.

Kinematic indices (KIs) are frequently used as objective measures to assess the upper extremities motor performance in post stroke patients. The clinimetric analysis of these indices has been mostly limited to their averaged values over different directions of reaching movements. Recent studies indicate direction dependencies of such motor performances due to neural and/or biomechanical causes. The direction dependencies of such indices and their clinimetric parameters remains to be investigated. An apparatus was built to perform and measure planar point-to-point reaching tasks in 8 directions using a virtual reality environment. 24 stroke and 18 healthy individuals participated in the study. 24 kinematic indices were calculated. Reliability (ICC), construct validity (Spearman correlation), and responsiveness (paired t-test pre and post intervention) were analyzed in each direction. The clinimetric parameters were found highly direction dependent. The reliability of the indices were strongest when moving away and towards the body. The validity (Spearman&gt;0.75) and responsiveness (p&lt;0.05) were most pronounced when moving in the NW-SE direction. These findings are in compliance with some previous neuro-musculoskeletal observations. While smoothness parameters are relatively uniform in all directions, speed and accuracy are direction dependent. The clinimetrics of the kinematic indices also depend on the direction and show stronger values in the NW-SE direction which is therefore proposed as the most accurate and responsive direction for kinematic assessment in stroke patients.