1
|
Levy DF, Entrup JL, Schneck SM, Onuscheck CF, Rahman M, Kasdan A, Casilio M, Willey E, Davis LT, de Riesthal M, Kirshner HS, Wilson SM. Multivariate lesion symptom mapping for predicting trajectories of recovery from aphasia. Brain Commun 2024; 6:fcae024. [PMID: 38370445 PMCID: PMC10873140 DOI: 10.1093/braincomms/fcae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 12/05/2023] [Accepted: 01/30/2024] [Indexed: 02/20/2024] Open
Abstract
Individuals with post-stroke aphasia tend to recover their language to some extent; however, it remains challenging to reliably predict the nature and extent of recovery that will occur in the long term. The aim of this study was to quantitatively predict language outcomes in the first year of recovery from aphasia across multiple domains of language and at multiple timepoints post-stroke. We recruited 217 patients with aphasia following acute left hemisphere ischaemic or haemorrhagic stroke and evaluated their speech and language function using the Quick Aphasia Battery acutely and then acquired longitudinal follow-up data at up to three timepoints post-stroke: 1 month (n = 102), 3 months (n = 98) and 1 year (n = 74). We used support vector regression to predict language outcomes at each timepoint using acute clinical imaging data, demographic variables and initial aphasia severity as input. We found that ∼60% of the variance in long-term (1 year) aphasia severity could be predicted using these models, with detailed information about lesion location importantly contributing to these predictions. Predictions at the 1- and 3-month timepoints were somewhat less accurate based on lesion location alone, but reached comparable accuracy to predictions at the 1-year timepoint when initial aphasia severity was included in the models. Specific subdomains of language besides overall severity were predicted with varying but often similar degrees of accuracy. Our findings demonstrate the feasibility of using support vector regression models with leave-one-out cross-validation to make personalized predictions about long-term recovery from aphasia and provide a valuable neuroanatomical baseline upon which to build future models incorporating information beyond neuroanatomical and demographic predictors.
Collapse
Affiliation(s)
- Deborah F Levy
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jillian L Entrup
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sarah M Schneck
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Caitlin F Onuscheck
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Maysaa Rahman
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Anna Kasdan
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Marianne Casilio
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Emma Willey
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - L Taylor Davis
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Michael de Riesthal
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Howard S Kirshner
- Vanderbilt Stroke and Cerebrovascular Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Stephen M Wilson
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, QLD 4072, Australia
| |
Collapse
|
2
|
Roth R, Busby N, Wilmskoetter J, Schwen Blackett D, Gleichgerrcht E, Johnson L, Rorden C, Newman-Norlund R, Hillis AE, den Ouden DB, Fridriksson J, Bonilha L. Diabetes, brain health, and treatment gains in post-stroke aphasia. Cereb Cortex 2023; 33:8557-8564. [PMID: 37139636 PMCID: PMC10321080 DOI: 10.1093/cercor/bhad140] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/31/2023] [Accepted: 04/01/2023] [Indexed: 05/05/2023] Open
Abstract
In post-stroke aphasia, language improvements following speech therapy are variable and can only be partially explained by the lesion. Brain tissue integrity beyond the lesion (brain health) may influence language recovery and can be impacted by cardiovascular risk factors, notably diabetes. We examined the impact of diabetes on structural network integrity and language recovery. Seventy-eight participants with chronic post-stroke aphasia underwent six weeks of semantic and phonological language therapy. To quantify structural network integrity, we evaluated the ratio of long-to-short-range white matter fibers within each participant's whole brain connectome, as long-range fibers are more susceptible to vascular injury and have been linked to high level cognitive processing. We found that diabetes moderated the relationship between structural network integrity and naming improvement at 1 month post treatment. For participants without diabetes (n = 59), there was a positive relationship between structural network integrity and naming improvement (t = 2.19, p = 0.032). Among individuals with diabetes (n = 19), there were fewer treatment gains and virtually no association between structural network integrity and naming improvement. Our results indicate that structural network integrity is associated with treatment gains in aphasia for those without diabetes. These results highlight the importance of post-stroke structural white matter architectural integrity in aphasia recovery.
Collapse
Affiliation(s)
- Rebecca Roth
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| | - Natalie Busby
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29208, USA
| | - Janina Wilmskoetter
- Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Deena Schwen Blackett
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Ezequiel Gleichgerrcht
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Lisa Johnson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29208, USA
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC 29208, USA
| | | | - Argye E Hillis
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD 21218 USA
| | - Dirk B den Ouden
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29208, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC 29208, USA
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| |
Collapse
|
3
|
Salvalaggio S, Boccuni L, Turolla A. Patient's assessment and prediction of recovery after stroke: a roadmap for clinicians. Arch Physiother 2023; 13:13. [PMID: 37337288 DOI: 10.1186/s40945-023-00167-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/26/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND AND PURPOSE In neurorehabilitation clinical practice, assessment is usually more oriented to evaluate patient's present status, than to plan interventions according to predicted outcomes. Therefore, we conducted an extensive review of current prognostic models available in the literature for recovery prediction of many functions and constructs, after stroke. We reported results in the form of a practical guide for clinicians, with the aim of promoting the culture of early clinical assessment for patient stratification, according to expected outcome. To define a roadmap for clinicians, a stepwise sequence of five actions has been developed, from collecting information of past medical history to the adoption of validated prediction tools. Furthermore, a clinically-oriented organization of available prediction tools for recovery after stroke have been proposed for motor, language, physiological and independency functions. Finally, biomarkers and online resources with prognostic value have been reviewed, to give the most updated state of the art on prediction tools after stroke. RECOMMENDATIONS FOR CLINICAL PRACTICE Clinical assessment should be directed both towards the objective evaluation of the present health status, and to the prediction of expected recovery. The use of specific outcome measures with predictive value is recommended to help clinicians with the definition of sound therapeutic goals.
Collapse
Affiliation(s)
- Silvia Salvalaggio
- Laboratory of Computational Neuroimaging, IRCCS San Camillo Hospital, Venezia, Italy
- Padova Neuroscience Center, Università Degli Studi Di Padova, Via Orus 2/B, 35131, Padova, Italy
| | - Leonardo Boccuni
- Institut Guttmann, Institut Universitari de Neurorehabilitació Adscrit a La UAB, Badalona, Barcelona, Spain
- Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain
- Fundació Institut d'Investigació en Ciències de La Salut Germans Trias I Pujol, Badalona, Barcelona, Spain
| | - Andrea Turolla
- Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater University of Bologna, Via Massarenti, 9, 40138, Bologna, Italy.
- Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, Bologna, Italy.
| |
Collapse
|
4
|
O'Dell MW. Stroke Rehabilitation and Motor Recovery. Continuum (Minneap Minn) 2023; 29:605-627. [PMID: 37039412 DOI: 10.1212/con.0000000000001218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
OBJECTIVE Up to 50% of the nearly 800,000 patients who experience a new or recurrent stroke each year in the United States fail to achieve full independence afterward. More effective approaches to enhance motor recovery following stroke are needed. This article reviews the rehabilitative principles and strategies that can be used to maximize post-stroke recovery. LATEST DEVELOPMENTS Evidence dictates that mobilization should not begin prior to 24 hours following stroke, but detailed guidelines beyond this are lacking. Specific classes of potentially detrimental medications should be avoided in the early days poststroke. Patients with stroke who are unable to return home should be referred for evaluation to an inpatient rehabilitation facility. Research suggests that a substantial increase in both the dose and intensity of upper and lower extremity exercise is beneficial. A clinical trial supports vagus nerve stimulation as an adjunct to occupational therapy for motor recovery in the upper extremity. The data remain somewhat mixed as to whether robotics, transcranial magnetic stimulation, functional electrical stimulation, and transcranial direct current stimulation are better than dose-matched traditional exercise. No current drug therapy has been proven to augment exercise poststroke to enhance motor recovery. ESSENTIAL POINTS Neurologists will collaborate with rehabilitation professionals for several months following a patient's stroke. Many questions still remain about the ideal exercise regimen to maximize motor recovery in patients poststroke. The next several years will likely bring a host of new research studies exploring the latest strategies to enhance motor recovery using poststroke exercise.
Collapse
|
5
|
Schevenels K, Gerrits R, Lemmens R, De Smedt B, Zink I, Vandermosten M. Early white matter connectivity and plasticity in post stroke aphasia recovery. Neuroimage Clin 2022; 36:103271. [PMID: 36510409 PMCID: PMC9723316 DOI: 10.1016/j.nicl.2022.103271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/30/2022] [Accepted: 11/17/2022] [Indexed: 11/21/2022]
Abstract
A disruption of white matter connectivity is negatively associated with language (recovery) in patients with aphasia after stroke, and behavioral gains have been shown to coincide with white matter neuroplasticity. However, most brain-behavior studies have been carried out in the chronic phase after stroke, with limited generalizability to earlier phases. Furthermore, few studies have investigated neuroplasticity patterns during spontaneous recovery (i.e., not related to a specific treatment) in the first months after stroke, hindering the investigation of potential early compensatory mechanisms. Finally, the majority of previous research has focused on damaged left hemisphere pathways, while neglecting the potential protective value of their right hemisphere counterparts for language recovery. To address these outstanding issues, we present a longitudinal study of thirty-two patients with aphasia (21 males and 11 females, M = 69.47 years, SD = 10.60 years) who were followed up for a period of 1 year with test moments in the acute (1-2 weeks), subacute (3-6 months) and chronic phase (9-12 months) after stroke. Constrained Spherical Deconvolution-based tractography was performed in the acute and subacute phase to measure Fiber Bundle Capacity (FBC), a quantitative connectivity measure that is valid in crossing fiber regions, in the bilateral dorsal arcuate fasciculus (AF) and the bilateral ventral inferior fronto-occipital fasciculus (IFOF). First, concurrent analyses revealed positive associations between the left AF and phonology, and between the bilateral IFOF and semantics in the acute - but not subacute - phase, supporting the dual-stream language model. Second, neuroplasticity analyses revealed a decrease in connection density of the bilateral AF - but not the IFOF - from the acute to the subacute phase, possibly reflecting post stroke white matter degeneration in areas adjacent to the lesion. Third, predictive analyses revealed no contribution of acute FBC measures to the prediction of later language outcomes over and above the initial language scores, suggesting no added value ofthe diffusion measures for languageprediction. Our study provides new insights on (changes in) connectivity of damaged and undamaged language pathways in patients with aphasia in the first months after stroke, as well as if/how such measures are related to language outcomes at different stages of recovery. Individual results are discussed in the light of current frameworks of language processing and aphasia recovery.
Collapse
Affiliation(s)
- Klara Schevenels
- Research Group Experimental Oto-Rhino-Laryngology, Department of Neurosciences, KU Leuven, Onderwijs en Navorsing 2 (O&N2), Herestraat 49 box 721, 3000 Leuven, Belgium,Leuven Brain Institute, KU Leuven, Onderwijs en Navorsing 5 (O&N 5), Herestraat 49 box 1020, 3000 Leuven, Belgium
| | - Robin Gerrits
- Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium
| | - Robin Lemmens
- Department of Neurology, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium,Research Group Experimental Neurology, Department of Neurosciences, KU Leuven, Herestraat 49 box 7003, 3000 Leuven, Belgium,Laboratory of Neurobiology, VIB Center for Brain & Disease Research, Onderwijs en Navorsing 5 (O&N 5), Herestraat 49 box 602, 3000 Leuven, Belgium,Leuven Brain Institute, KU Leuven, Onderwijs en Navorsing 5 (O&N 5), Herestraat 49 box 1020, 3000 Leuven, Belgium
| | - Bert De Smedt
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leopold Vanderkelenstraat 32 box 3765, 3000 Leuven, Belgium,Leuven Brain Institute, KU Leuven, Onderwijs en Navorsing 5 (O&N 5), Herestraat 49 box 1020, 3000 Leuven, Belgium
| | - Inge Zink
- Research Group Experimental Oto-Rhino-Laryngology, Department of Neurosciences, KU Leuven, Onderwijs en Navorsing 2 (O&N2), Herestraat 49 box 721, 3000 Leuven, Belgium,Leuven Brain Institute, KU Leuven, Onderwijs en Navorsing 5 (O&N 5), Herestraat 49 box 1020, 3000 Leuven, Belgium
| | - Maaike Vandermosten
- Research Group Experimental Oto-Rhino-Laryngology, Department of Neurosciences, KU Leuven, Onderwijs en Navorsing 2 (O&N2), Herestraat 49 box 721, 3000 Leuven, Belgium,Leuven Brain Institute, KU Leuven, Onderwijs en Navorsing 5 (O&N 5), Herestraat 49 box 1020, 3000 Leuven, Belgium,Corresponding author at: Research Group Experimental Oto-Rhino-Laryngology, Department of Neurosciences, KU Leuven, Onderwijs en Navorsing 2 (O&N2), Herestraat 49 box 721, 3000 Leuven, Belgium.
| |
Collapse
|
6
|
Goldsmith J, Kitago T, Garcia de la Garza A, Kundert R, Luft A, Stinear C, Byblow WD, Kwakkel G, Krakauer JW. Arguments for the biological and predictive relevance of the proportional recovery rule. eLife 2022; 11:e80458. [PMID: 36255057 PMCID: PMC9648971 DOI: 10.7554/elife.80458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 10/17/2022] [Indexed: 11/13/2022] Open
Abstract
The proportional recovery rule (PRR) posits that most stroke survivors can expect to reduce a fixed proportion of their motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values - an approach that arises in many scientific domains but has the potential to introduce artifacts and flawed conclusions. We describe approaches that can assess associations between baseline and changes from baseline while avoiding artifacts due either to mathematical coupling or to regression to the mean. We also describe methods that can compare different biological models of recovery. Across several real datasets in stroke recovery, we find evidence for non-artifactual associations between baseline and change, and support for the PRR compared to alternative models. We also introduce a statistical perspective that can be used to assess future models. We conclude that the PRR remains a biologically relevant model of stroke recovery.
Collapse
Affiliation(s)
- Jeff Goldsmith
- Department of Biostatistics, Columbia Mailman School of Public HealthNew YorkUnited States
| | - Tomoko Kitago
- Burke Neurological InstituteWhite PlainsUnited States
- Weill Cornell MedicineNew YorkUnited States
| | | | - Robinson Kundert
- Cereneo, Center for Neurology and RehabilitationVitznauSwitzerland
| | - Andreas Luft
- Cereneo, Center for Neurology and RehabilitationVitznauSwitzerland
- Vascular Neurology and Neurorehabilitation, Department of Neurology, University Hospital Zurich, University of ZurichZurichSwitzerland
| | - Cathy Stinear
- Department of Medicine, University of AucklandAucklandNew Zealand
| | - Winston D Byblow
- Department of Exercise Sciences, University of AucklandAucklandNew Zealand
| | - Gert Kwakkel
- Rehabilitation Research Centre, ReadeAmsterdamNetherlands
- Rehabilitation Medicine, Amsterdam UMC - Location VUMC, Amsterdam Movement SciencesAmsterdamNetherlands
| | - John W Krakauer
- Department of Neurology, Johns Hopkins UniversityBaltimoreUnited States
- Department of Neuroscience, Johns Hopkins UniversityBaltimoreUnited States
- Department of Physical Medicine and RehabilitationBaltimoreUnited States
- Santa Fe InstituteSanta Fe NMUnited States
| |
Collapse
|
7
|
Scott SH, Lowrey CR, Brown IE, Dukelow SP. Assessment of Neurological Impairment and Recovery Using Statistical Models of Neurologically Healthy Behavior. Neurorehabil Neural Repair 2022:15459683221115413. [PMID: 35932111 DOI: 10.1177/15459683221115413] [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] [Indexed: 11/15/2022]
Abstract
While many areas of medicine have benefited from the development of objective assessment tools and biomarkers, there have been comparatively few improvements in techniques used to assess brain function and dysfunction. Brain functions such as perception, cognition, and motor control are commonly measured using criteria-based, ordinal scales which can be coarse, have floor/ceiling effects, and often lack the precision to detect change. There is growing recognition that kinematic and kinetic-based measures are needed to quantify impairments following neurological injury such as stroke, in particular for clinical research and clinical trials. This paper will first consider the challenges with using criteria-based ordinal scales to quantify impairment and recovery. We then describe how kinematic-based measures can overcome many of these challenges and highlight a statistical approach to quantify kinematic measures of behavior based on performance of neurologically healthy individuals. We illustrate this approach with a visually-guided reaching task to highlight measures of impairment for individuals following stroke. Finally, there has been considerable controversy about the calculation of motor recovery following stroke. Here, we highlight how our statistical-based approach can provide an effective estimate of impairment and recovery.
Collapse
Affiliation(s)
- Stephen H Scott
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Catherine R Lowrey
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Ian E Brown
- Kinarm, BKIN Technologies Ltd. Kingston, ON, Canada
| | - Sean P Dukelow
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
8
|
Lee HH, Sohn MK, Kim DY, Shin YI, Oh GJ, Lee YS, Joo MC, Lee SY, Song MK, Han J, Ahn J, Lee YH, Chang WH, Choi SM, Lee SK, Lee J, Kim YH. Understanding of the Lower Extremity Motor Recovery After First-Ever Ischemic Stroke. Stroke 2022; 53:3164-3172. [PMID: 35713003 DOI: 10.1161/strokeaha.121.038196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND We aimed to verify the validity of the proportional recovery model for the lower extremity. METHODS We reviewed clinical data of patients enrolled in the Korean Stroke Cohort for Functioning and Rehabilitation between August 2012 and May 2015. Recovery proportion was calculated as the amount of motor recovery over initial motor impairment, measured as the Fugl-Meyer Assessment of Lower Extremity score. We used the logistic regression method to model the probability of achieving the full Fugl-Meyer Assessment of Lower Extremity score, whereby we considered the ceiling effect of the score. To show the difference in the prevalence of achieving the full Fugl-Meyer Assessment of Lower Extremity score between 3 and 6 months poststroke, we constructed a marginal model through the generalized estimating equation method. We also performed the propensity score matching analysis to show the dependency of recovery proportion on the initial motor deficit at 3 and 6 months poststroke. RESULTS We evaluated 1085 patients. The recovery proportions at 3 and 6 months poststroke were 0.67±0.42 and 0.75±0.39, respectively. A 1-unit decrease in the initial neurological impairment and the age at stroke onset increased the probability of achieving the full Fugl-Meyer Assessment of Lower Extremity score, which occurred at both 3 and 6 months poststroke. The prevalence of those who reach full lower limb motor recovery differs significantly between 3 and 6 months poststroke. We also found out that the recovery proportion at both 3 and 6 months poststroke is determined by the initial motor deficits of the lower limb. These results are not consistent with the proportional recovery model. CONCLUSIONS Our results demonstrated that the proportional recovery model for the lower limb is invalid.
Collapse
Affiliation(s)
- Hyun Haeng Lee
- Department of Rehabilitation Medicine, Konkuk University Medical Center and Konkuk University School of Medicine, Seoul, South Korea (H.H.L., J.L.)
| | - Min Kyun Sohn
- Department of Rehabilitation Medicine, Chungnam National University School of Medicine, Daejeon, South Korea (M.K.S.)
| | - Deog Young Kim
- Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, South Korea (D.Y.K.)
| | - Yong-Il Shin
- Department of Rehabilitation Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, South Korea (Y.-I.S.)
| | - Gyung-Jae Oh
- Department of Preventive Medicine, Wonkwang University School of Medicine, Iksan, South Korea. (G.-J.O., Y.-H.L.)
| | - Yang-Soo Lee
- Department of Rehabilitation Medicine, Kyungpook National University School of Medicine, Kyungpook National University Hospital, Daegu, South Korea (Y.-S.L.)
| | - Min Cheol Joo
- Department of Rehabilitation Medicine, Wonkwang University School of Medicine, Iksan, South Korea. (M.C.J.)
| | - So Young Lee
- Department of Rehabilitation Medicine, Jeju National University Hospital, Jeju National University School of Medicine, South Korea (S.Y.L.)
| | - Min-Keun Song
- Department of Physical and Rehabilitation Medicine, Chonnam National University Medical School, Gwangju, South Korea (M.K.S.)
| | - Junhee Han
- Department of Statistics and Institute of Statistics, Hallym University, Chuncheon, South Korea (J.H.)
| | - Jeonghoon Ahn
- Department of Health Convergence, Ewha Womans University, Seoul, South Korea (J.A.)
| | - Young-Hoon Lee
- Department of Preventive Medicine, Wonkwang University School of Medicine, Iksan, South Korea. (G.-J.O., Y.-H.L.)
| | - Won Hyuk Chang
- Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (W.H.C., Y.-H.K.)
| | - Soo Mi Choi
- Division of Chronic Disease Prevention, Korea Disease Control and Prevention Agency, Cheongju, South Korea (S.M.C., S.K.L.)
| | - Seon Kui Lee
- Division of Chronic Disease Prevention, Korea Disease Control and Prevention Agency, Cheongju, South Korea (S.M.C., S.K.L.)
| | - Jongmin Lee
- Department of Rehabilitation Medicine, Konkuk University Medical Center and Konkuk University School of Medicine, Seoul, South Korea (H.H.L., J.L.)
| | - Yun-Hee Kim
- Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (W.H.C., Y.-H.K.).,Department of Health Science and Technology, Department of Medical Device Management and Research, Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea (Y.-H.K.)
| |
Collapse
|
9
|
Bonkhoff AK, Hope T, Bzdok D, Guggisberg AG, Hawe RL, Dukelow SP, Chollet F, Lin DJ, Grefkes C, Bowman H. Recovery after stroke: the severely impaired are a distinct group. J Neurol Neurosurg Psychiatry 2022; 93:369-378. [PMID: 34937750 DOI: 10.1136/jnnp-2021-327211] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 12/06/2021] [Indexed: 01/13/2023]
Abstract
INTRODUCTION Stroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently. METHODS We designed a Bayesian hierarchical model to estimate 3-6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5-30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range. RESULTS Recovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3-6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%). CONCLUSIONS Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.
Collapse
Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tom Hope
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada.,Mila - Quebec Artificial Intelligence Institute, Montreal, Québec, Canada.,Canadian Institute for Advanced Research (CIFAR), Montreal, Québec, Canada
| | - Adrian G Guggisberg
- Department of Clinical Neurosciences, Hopitaux Universitaires de Geneve Hopital de Beau-Sejour, Geneva, Switzerland
| | - Rachel L Hawe
- School of Kinesiology, University of Minnesota, Minneapolis, Minnesota, USA.,Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Sean P Dukelow
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - François Chollet
- Neurology, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - David J Lin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christian Grefkes
- Department of Neurology, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
| | - Howard Bowman
- School of Psychology, University of Birmingham, Birmingham, UK.,School of Computing, University of Kent, Canterbury, UK
| |
Collapse
|
10
|
Bonkhoff AK, Grefkes C. Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 2021; 145:457-475. [PMID: 34918041 PMCID: PMC9014757 DOI: 10.1093/brain/awab439] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 11/02/2021] [Accepted: 11/21/2021] [Indexed: 11/16/2022] Open
Abstract
Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.
Collapse
Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Grefkes
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany.,Department of Neurology, University Hospital Cologne.,Medical Faculty, University of Cologne, Germany
| |
Collapse
|