1
|
Burton LJ, Forster A, Johnson J, Crocker TF, Tyson SF, Clarke DJ. What influences provision of information about recovery on stroke units? A focused ethnographic case study. PATIENT EDUCATION AND COUNSELING 2024; 126:108331. [PMID: 38781751 DOI: 10.1016/j.pec.2024.108331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE Patients and carers frequently report dissatisfaction with post-stroke information provision. This study aimed to develop an in-depth understanding of the factors influencing provision of information about recovery in stroke units. METHODS Focused ethnographic case-studies in two UK stroke units, including non-participant observations and semi-structured interviews with professionals, patients and carers, and documentary analysis. A Framework approach to analysis was undertaken. RESULTS Twenty patients, 17 carers and 47 professionals participated. The unpredictable recovery trajectory led professionals to present prognostic estimates as uncertain possibilities. The need to maintain patients' motivation limited sharing of negative predictions, and generic information over-emphasised the importance of therapy in recovery. A structured multidisciplinary team approach to delivering information improved consistency. Complex clinical reasoning was required to identify and meet patients' needs. Hospital environments and routines restricted opportunities for dialogue, particularly with carers. CONCLUSIONS The process of providing information about post-stroke recovery is complex, requiring enhanced clinical reasoning and communication. The challenges faced by professionals are numerous and if not addressed can result in suboptimal provision. PRACTICE IMPLICATIONS Professionals should develop a co-ordinated multidisciplinary approach to information provision; and engage in dialogue to ensure a tailored approach to identifying and meeting patients' and carers' information needs.
Collapse
Affiliation(s)
- Louisa-Jane Burton
- Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK; Academic Unit for Ageing & Stroke Research, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK.
| | - Anne Forster
- Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK; Academic Unit for Ageing & Stroke Research, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Judith Johnson
- School of Psychology, University of Leeds, Leeds, UK; Yorkshire Quality and Safety Research Group, Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, UK; School of Public Health and Community Medicine, University of New South Wales, Australia
| | - Thomas F Crocker
- Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK; Academic Unit for Ageing & Stroke Research, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Sarah F Tyson
- Division of Nursing, Midwifery and Social Work, University of Manchester, Manchester, UK
| | - David J Clarke
- Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK; Academic Unit for Ageing & Stroke Research, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| |
Collapse
|
2
|
Boyles RH, Alexander CM, Belsi A, Strutton PH. Are Clinical Prediction Rules Used in Spinal Cord Injury Care? A Survey of Practice. Top Spinal Cord Inj Rehabil 2024; 30:45-58. [PMID: 38433737 PMCID: PMC10906376 DOI: 10.46292/sci23-00069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Background Accurate outcome prediction is desirable post spinal cord injury (SCI), reducing uncertainty for patients and supporting personalized treatments. Numerous attempts have been made to create clinical prediction rules that identify patients who are likely to recover function. It is unknown to what extent these rules are routinely used in clinical practice. Objectives To better understand knowledge of, and attitudes toward, clinical prediction rules amongst SCI clinicians in the United Kingdom. Methods An online survey was distributed via mailing lists of clinical special interest groups and relevant National Health Service Trusts. Respondents answered questions about their knowledge of existing clinical prediction rules and their general attitudes to using them. They also provided information about their level of experience with SCI patients. Results One hundred SCI clinicians completed the survey. The majority (71%) were unaware of clinical prediction rules for SCI; only 8% reported using them in clinical practice. Less experienced clinicians were less likely to be aware. Lack of familiarity with prediction rules was reported as being a barrier to their use. The importance of clinical expertise when making prognostic decisions was emphasized. All respondents reported interest in using clinical prediction rules in the future. Conclusion The results show widespread lack of awareness of clinical prediction rules amongst SCI clinicians in the United Kingdom. However, clinicians were positive about the potential for clinical prediction rules to support decision-making. More focus should be directed toward refining current rules and improving dissemination within the SCI community.
Collapse
Affiliation(s)
- Rowan H. Boyles
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Therapies, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Caroline M. Alexander
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Therapies, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Athina Belsi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Paul H. Strutton
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| |
Collapse
|
3
|
Ackerley S, Wilson N, Boland P, Read J, Connell L. Implementation of neurological group-based telerehabilitation within existing healthcare during the COVID-19 pandemic: a mixed methods evaluation. BMC Health Serv Res 2023; 23:671. [PMID: 37344774 DOI: 10.1186/s12913-023-09635-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 06/02/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND There is a need to evaluate if and how telerehabilitation approaches might co-exist within healthcare in the long-term. Our aim was to implement and evaluate a multidisciplinary group-based telerehabilitation approach for people engaging in neurological rehabilitation. METHODS NeuroRehabilitation OnLine (NROL) was adapted and implemented within an existing healthcare system as a programme of repeating six-week blocks. A robust evaluation was undertaken simultaneously using a convergent parallel design underpinned by implementation frameworks. This included service data, and patient and staff interviews. Implementation success was conceptualised using the outcomes of appropriateness, acceptability and sustainability. RESULTS Eight NROL blocks delivered 265 sessions with 1347 patient contacts, and NROL continues as part of standard practice. The approach was appropriate for varied demographics and had positive patient opinions and outcomes for many. Staff perceived NROL provided a compatible means to increase therapy and help meet targets, despite needing to mitigate some challenges when fitting the approach within the existing system. NROL was considered acceptable due to good attendance (68%), low drop-out (12%), and a good safety record (one non-injury fall). It was accepted as a new way of working across rehabilitation disciplines as an 'extra layer of therapy'. NROL had perceived advantages in terms of patient and staff resource (e.g. saving time, energy and travel). NROL provided staffing efficiencies (ratio 0.6) compared to one-to-one delivery. Technology difficulties and reluctance were surmountable with dedicated technology assistance. Leadership commitment was considered key to enable the efforts needed for implementation and sustained use. CONCLUSION Pragmatic implementation of group-based telerehabilitation was possible as an adjunct to neurological rehabilitation within an existing healthcare system. The compelling advantages reported of having NROL as part of rehabilitation supports the continued use of this telerehabilitation approach. This project provides an exemplar of how evaluation can be run concurrently with implementation, applying a data driven rather than anecdotal approach to implementation.
Collapse
Affiliation(s)
- Suzanne Ackerley
- School of Sport and Health Sciences, University of Central Lancashire, Preston, Lancashire, UK
- East Lancashire Hospitals NHS Trust, Burnley, Lancashire, UK
| | - Neil Wilson
- School of Sport and Health Sciences, University of Central Lancashire, Preston, Lancashire, UK
| | - Paul Boland
- School of Sport and Health Sciences, University of Central Lancashire, Preston, Lancashire, UK
| | - Jessica Read
- Lancashire & South Cumbria NHS Foundation Trust, Blackburn, Lancashire, UK
| | - Louise Connell
- School of Sport and Health Sciences, University of Central Lancashire, Preston, Lancashire, UK.
- East Lancashire Hospitals NHS Trust, Burnley, Lancashire, UK.
| |
Collapse
|
4
|
Goffredo M, Proietti S, Pournajaf S, Galafate D, Cioeta M, Le Pera D, Posteraro F, Franceschini M. Baseline robot-measured kinematic metrics predict discharge rehabilitation outcomes in individuals with subacute stroke. Front Bioeng Biotechnol 2022; 10:1012544. [PMID: 36561043 PMCID: PMC9763272 DOI: 10.3389/fbioe.2022.1012544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Background: The literature on upper limb robot-assisted therapy showed that robot-measured metrics can simultaneously predict registered clinical outcomes. However, only a limited number of studies correlated pre-treatment kinematics with discharge motor recovery. Given the importance of predicting rehabilitation outcomes for optimizing physical therapy, a predictive model for motor recovery that incorporates multidirectional indicators of a patient's upper limb abilities is needed. Objective: The aim of this study was to develop a predictive model for rehabilitation outcome at discharge (i.e., muscle strength assessed by the Motricity Index of the affected upper limb) based on multidirectional 2D robot-measured kinematics. Methods: Re-analysis of data from 66 subjects with subacute stroke who underwent upper limb robot-assisted therapy with an end-effector robot was performed. Two least squares error multiple linear regression models for outcome prediction were developed and differ in terms of validation procedure: the Split Sample Validation (SSV) model and the Leave-One-Out Cross-Validation (LOOCV) model. In both models, the outputs were the discharge Motricity Index of the affected upper limb and its sub-items assessing elbow flexion and shoulder abduction, while the inputs were the admission robot-measured metrics. Results: The extracted robot-measured features explained the 54% and 71% of the variance in clinical scores at discharge in the SSV and LOOCV validation procedures respectively. Normalized errors ranged from 22% to 35% in the SSV models and from 20% to 24% in the LOOCV models. In all models, the movement path error of the trajectories characterized by elbow flexion and shoulder extension was the significant predictor, and all correlations were significant. Conclusion: This study highlights that motor patterns assessed with multidirectional 2D robot-measured metrics are able to predict clinical evalutation of upper limb muscle strength and may be useful for clinicians to assess, manage, and program a more specific and appropriate rehabilitation in subacute stroke patients.
Collapse
Affiliation(s)
- Michela Goffredo
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Stefania Proietti
- Unit of Clinical and Molecular Epidemiology, IRCCS San Raffaele Roma, Rome, Italy,Department of Human Sciences and Promotion of the Quality of Life, San Raffaele University, Rome, Italy
| | - Sanaz Pournajaf
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy,*Correspondence: Sanaz Pournajaf,
| | - Daniele Galafate
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Matteo Cioeta
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Domenica Le Pera
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | | | - Marco Franceschini
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy,Department of Human Sciences and Promotion of the Quality of Life, San Raffaele University, Rome, Italy
| |
Collapse
|
5
|
Veerbeek JM, Pohl J, Luft AR, Held JPO. External validation and extension of the Early Prediction of Functional Outcome after Stroke (EPOS) prediction model for upper limb outcome 3 months after stroke. PLoS One 2022; 17:e0272777. [PMID: 35939514 PMCID: PMC9359545 DOI: 10.1371/journal.pone.0272777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 07/27/2022] [Indexed: 11/23/2022] Open
Abstract
Objective The ‘Early Prediction of Functional Outcome after Stroke’ (EPOS) model was developed to predict the presence of at least some upper limb capacity (Action Research Am Test [ARAT] ≥10/57) at 6 months based on assessments on days 2, 5 and 9 after stroke. External validation of the model is the next step towards clinical implementation. The objective here is to externally validate the EPOS model for upper limb outcome 3 months poststroke in Switzerland and extend the model using an ARAT cut-off at 32 points. Methods Data from two prospective longitudinal cohort studies including first-ever stroke patients admitted to a Swiss stroke center were analyzed. The presence of finger extension and shoulder abduction was measured on days 1 and 8 poststroke in Cohort 1, and on days 3 and 9 in Cohort 2. Upper limb capacity was measured 3 months poststroke. Discrimination (area under the curve; AUC) and calibration obtained with the model were determined. Results In Cohort 1 (N = 39, median age 74 years), the AUC on day 1 was 0.78 (95%CI 0.61, 0.95) and 0.96 (95%CI 0.90, 1.00) on day 8, using the model of day 5. In Cohort 2 (N = 85, median age 69 years), the AUC was 0.96 (95%CI 0.93, 0.99) on day 3 and 0.89 (95% CI 0.80, 0.98) on day 9. Applying a 32-point ARAT cut-off resulted in an AUC ranging from 0.82 (95%CI 0.68, 0.95; Cohort 1, day 1) to 0.95 (95%CI 0.87, 1.00; Cohort 1, day 8). Conclusions The EPOS model was successfully validated in first-ever stroke patients with mild-to-moderate neurological impairments, who were independent before their stroke. Now, its impact on clinical practice should be investigated in this population. Testing the model’s performance in severe (recurrent) strokes and stratification of patients using the ARAT 32-point cut-off is required to enhance the model’s generalizability and potential clinical impact.
Collapse
Affiliation(s)
- Janne M. Veerbeek
- Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- * E-mail:
| | - Johannes Pohl
- Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andreas R. Luft
- Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- cereneo, Center for Neurology and Rehabilitation, Vitznau, Switzerland
| | - Jeremia P. O. Held
- Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| |
Collapse
|
6
|
Motor Control: A Conceptual Framework for Rehabilitation. Motor Control 2022; 26:497-517. [PMID: 35894963 DOI: 10.1123/mc.2022-0026] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/09/2022] [Accepted: 05/02/2022] [Indexed: 11/18/2022]
Abstract
There is a lack of conceptual and theoretical clarity among clinicians and researchers regarding the control of motor actions based on the use of the term "motor control." It is important to differentiate control processes from observations of motor output to improve communication and to make progress in understanding motor disorders and their remediation. This article clarifies terminology related to theoretical concepts underlying the control of motor actions, emphasizing how the term "motor control" is applied in neurorehabilitation. Two major opposing theoretical frameworks are described (i.e., direct and indirect), and their strengths and pitfalls are discussed. Then, based on the proposition that sensorimotor rehabilitation should be predicated on one comprehensive theory instead of an eclectic mix of theories and models, several solutions are offered about how to address controversies in motor learning, optimality, and adaptability of movement.
Collapse
|
7
|
Veerbeek JM, Pohl J, Held JPO, Luft AR. External Validation of the Early Prediction of Functional Outcome After Stroke Prediction Model for Independent Gait at 3 Months After Stroke. Front Neurol 2022; 13:797791. [PMID: 35585839 PMCID: PMC9108182 DOI: 10.3389/fneur.2022.797791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionThe Early Prediction of Functional Outcome after Stroke (EPOS) model for independent gait is a tool to predict between days 2 and 9 poststroke whether patients will regain independent gait 6 months after stroke. External validation of the model is important to determine its clinical applicability and generalizability by testing its performance in an independent cohort. Therefore, this study aimed to perform a temporal and geographical external validation of the EPOS prediction model for independent gait after stroke but with the endpoint being 3 months instead of the original 6 months poststroke.MethodsTwo prospective longitudinal cohort studies consisting of patients with first-ever stroke admitted to a Swiss hospital stroke unit. Sitting balance and strength of the paretic leg were tested at days 1 and 8 post-stroke in Cohort I and at days 3 and 9 in Cohort II. Independent gait was assessed 3 months after symptom onset. The performance of the model in terms of discrimination (area under the receiver operator characteristic (ROC) curve; AUC), classification, and calibration was assessed.ResultsIn Cohort I [N = 39, median age: 74 years, 33% women, median National Institutes of Health Stroke Scale (NIHSS) 9], the AUC (95% confidence interval (CI)] was 0.675 (0.510, 0.841) on day 1 and 0.921 (0.811, 1.000) on day 8. For Cohort II (N = 78, median age: 69 years, 37% women, median NIHSS 8), this was 0.801 (0.684, 0.918) on day 3 and 0.846 (0.741, 0.951) on day 9.Discussion and ConclusionExternal validation of the EPOS prediction model for independent gait 3 months after stroke resulted in an acceptable performance from day 3 onward in mild-to-moderately affected patients with first-ever stroke without severe prestroke disability. The impact of applying this model in clinical practice should be investigated within this subgroup of patients with stroke. To improve the generalizability of patients with recurrent stroke and those with more severe, neurological comorbidities, the performance of the EPOS model within these patients should be determined across different geographical areas.
Collapse
Affiliation(s)
- Janne M. Veerbeek
- Department of Neurology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Neurocenter, Luzerner Kantonsspital, Lucerne, Switzerland
- *Correspondence: Janne M. Veerbeek
| | - Johannes Pohl
- Department of Neurology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Department of Rehabilitation Sciences, KU Leuven – University of Leuven, Leuven, Belgium
| | - Jeremia P. O. Held
- Department of Neurology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Rehabilitation Center Triemli Zurich, Valens Clinics, Zurich, Switzerland
| | - Andreas R. Luft
- Department of Neurology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Cereneo, Center for Neurology and Rehabilitation, Vitznau, Switzerland
| |
Collapse
|
8
|
Saltão da Silva MA, Baune NA, Belagaje S, Borich MR. Clinical Imaging-Derived Metrics of Corticospinal Tract Structural Integrity Are Associated With Post-stroke Motor Outcomes: A Retrospective Study. Front Neurol 2022; 13:804133. [PMID: 35250812 PMCID: PMC8893034 DOI: 10.3389/fneur.2022.804133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveThe primary objective of this study was to retrospectively investigate associations between clinical magnetic resonance imaging-based (MRI) metrics of corticospinal tract (CST) status and paretic upper extremity (PUE) motor recovery in patients that completed acute inpatient rehabilitation (AR) post-stroke.MethodsWe conducted a longitudinal chart review of patients post-stroke who received care in the Emory University Hospital system during acute hospitalization, AR, and outpatient therapy. We extracted demographic information, stroke characteristics, and longitudinal documentation of post-stroke motor function from institutional electronic medical records. Serial assessments of paretic shoulder abduction and finger extension were estimated (E-SAFE) and an estimated Action Research Arm Test (E-ARAT) score was used to quantify 3-month PUE motor function outcome. Clinically-diagnostic MRI were used to create lesion masks that were spatially normalized and overlaid onto a white matter tract atlas delineating CST contributions emanating from six cortical seed regions to obtain the percentage of CST lesion overlap. Metric associations were investigated with correlation and cluster analyses, Kruskal-Wallis tests, classification and regression tree analysis.ResultsThirty-four patients met study eligibility criteria. All CST overlap percentages were correlated with E-ARAT however, ventral premotor tract (PMv) overlap was the only tract that remained significantly correlated after multiple comparisons adjustment. Lesion overlap percentage in CST contributions from all seed regions was significantly different between outcome categories. Using MRI metrics alone, dorsal premotor (PMd) and PMv tracts classified recovery outcome category with 79.4% accuracy. When clinical and MRI metrics were combined, AR E-SAFE, patient age, and overall CST lesion overlap classified patients with 88.2% accuracy.ConclusionsStudy findings revealed clinical MRI-derived CST lesion overlap was associated with PUE motor outcome post-stroke and that cortical projections within the CST, particularly those emanating from non-M1 cortical areas, prominently ventral premotor (PMv) and dorsal premotor (PMd) cortices, distinguished between PUE outcome groups. Exploratory predictive models using clinical MRI metrics, either alone or in combination with clinical measures, were able to accurately identify recovery outcome category for the study cohort during both the acute and early subacute phases of post-stroke recovery. Prospective studies are recommended to determine the predictive utility of including clinical imaging-based biomarkers of white matter tract structural integrity in predictive models of post-stroke recovery.
Collapse
Affiliation(s)
- Mary Alice Saltão da Silva
- Neural Plasticity Research Laboratory, Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States
| | - Nathan Allen Baune
- Neural Plasticity Research Laboratory, Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Samir Belagaje
- Departments of Neurology and Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Michael R. Borich
- Neural Plasticity Research Laboratory, Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
- *Correspondence: Michael R. Borich
| |
Collapse
|
9
|
Srivastava A, Kumar P, Prasad M, Das A, Vibha D, Garg A, Goyal V. Utility of transcranial magnetic stimulation and diffusion tensor imaging for prediction of upper-limb motor recovery in acute ischemic stroke patients. Ann Indian Acad Neurol 2022; 25:54-59. [PMID: 35342270 PMCID: PMC8954333 DOI: 10.4103/aian.aian_254_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/15/2021] [Accepted: 06/09/2021] [Indexed: 11/24/2022] Open
Abstract
Background: The recovery of the upper-limb (UL) motor function after ischemic stroke (IS) remains a major scientific, clinical, and patient concern and it is hard to predict alone from the clinical symptoms. Objective: To determine the accuracy of the prediction of the recovery of UL motor function in patients with acute ischemic middle cerebral artery (MCA) stroke using individual clinical, transcranial magnetic stimulation (TMS) or diffusion tensor imaging (DTI) parameters or their combination. Methods and Material: The first-ever acute ischemic MCA stroke patients within 7 days of the stroke onset who had an obvious UL motor deficit underwent TMS for the presence of motor-evoked potential (MEP) and DTI to evaluate the integrity of corticospinal tracts. Multivariate logistic regression analysis was done to test for the accuracy of the prediction of the recovery of UL motor function. Results: Twenty-nine acute ischemic MCA stroke patients (21 males and 8 females) with a mean age of 51.45 ± 14.26 years were recruited. Model-I included clinical scales (Fugl-Meyer Assessment [FMA] + Motricity Index [MI]) + TMS (MEP) + DTI (fractional anisotropy [FA]) were found to be the most accurate predictive model, with the overall predictive ability (93.3%; 95% confidence interval [CI]: 0.87–0.99) and sensitivity: 94.9% (95% CI: 0.87–1.0) and specificity: 95.8% (95% CI: 0.89–1.0); respectively. Conclusion: The accuracy of UL motor recovery can be predicted through the clinical battery and their elements as well as TMS (MEP) and DTI (FA) parameters. Further, well-designed prospective studies are needed to confirm our findings.
Collapse
|
10
|
Cecchini G, Scaglione A, Allegra Mascaro AL, Checcucci C, Conti E, Adam I, Fanelli D, Livi R, Pavone FS, Kreuz T. Cortical propagation tracks functional recovery after stroke. PLoS Comput Biol 2021; 17:e1008963. [PMID: 33999967 PMCID: PMC8159272 DOI: 10.1371/journal.pcbi.1008963] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/27/2021] [Accepted: 04/13/2021] [Indexed: 12/04/2022] Open
Abstract
Stroke is a debilitating condition affecting millions of people worldwide. The development of improved rehabilitation therapies rests on finding biomarkers suitable for tracking functional damage and recovery. To achieve this goal, we perform a spatiotemporal analysis of cortical activity obtained by wide-field calcium images in mice before and after stroke. We compare spontaneous recovery with three different post-stroke rehabilitation paradigms, motor training alone, pharmacological contralesional inactivation and both combined. We identify three novel indicators that are able to track how movement-evoked global activation patterns are impaired by stroke and evolve during rehabilitation: the duration, the smoothness, and the angle of individual propagation events. Results show that, compared to pre-stroke conditions, propagation of cortical activity in the subacute phase right after stroke is slowed down and more irregular. When comparing rehabilitation paradigms, we find that mice treated with both motor training and pharmacological intervention, the only group associated with generalized recovery, manifest new propagation patterns, that are even faster and smoother than before the stroke. In conclusion, our new spatiotemporal propagation indicators could represent promising biomarkers that are able to uncover neural correlates not only of motor deficits caused by stroke but also of functional recovery during rehabilitation. In turn, these insights could pave the way towards more targeted post-stroke therapies. Millions of people worldwide suffer from long-lasting motor deficits caused by stroke. Very recently, the two basic therapeutic approaches, motor training and pharmacological intervention, have been combined in order to achieve a more efficient functional recovery. In this study, we analyze the neurophysiological activity in the brain of mice observed with in vivo calcium imaging before and after the induction of a stroke. We use a newly developed universal approach based on the temporal sequence of local activation in different brain regions to quantify three properties of global propagation patterns: duration, smoothness and angle. These innovative spatiotemporal propagation indicators allow us to track damage and functional recovery following stroke and to quantify the relative success of motor training, pharmacological inactivation, and a combination of both, compared to spontaneous recovery. We show that all three treatments reverse the alterations observed during the subacute phase right after stroke. We also find that combining motor training and pharmacological intervention does not restore pre-stroke features but rather leads to the emergence of new propagation patterns that, surprisingly, are even faster and smoother than the pre-stroke patterns.
Collapse
Affiliation(s)
- Gloria Cecchini
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- CSDC, University of Florence, Sesto Fiorentino, Italy
- * E-mail:
| | - Alessandro Scaglione
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
| | - Anna Letizia Allegra Mascaro
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
- Neuroscience Institute, National Research Council, Pisa, Italy
| | - Curzio Checcucci
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
| | - Emilia Conti
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
- Neuroscience Institute, National Research Council, Pisa, Italy
| | - Ihusan Adam
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- CSDC, University of Florence, Sesto Fiorentino, Italy
- Department of Information Engineering, University of Florence, Sesto Fiorentino, Italy
| | - Duccio Fanelli
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- CSDC, University of Florence, Sesto Fiorentino, Italy
- INFN, Florence Section, Sesto Fiorentino, Italy
| | - Roberto Livi
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- CSDC, University of Florence, Sesto Fiorentino, Italy
- INFN, Florence Section, Sesto Fiorentino, Italy
| | - Francesco Saverio Pavone
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
| | - Thomas Kreuz
- Institute for Complex Systems (ISC), National Research Council (CNR), Sesto Fiorentino, Italy
| |
Collapse
|