Posts Tagged Prognosis

[Abstract + References] Gait rehabilitation after stroke: review of the evidence of predictors, clinical outcomes and timing for interventions


The recovery of walking capacity is one of the main aims in stroke rehabilitation. Being able to predict if and when a patient is going to walk after stroke is of major interest in terms of management of the patients and their family’s expectations and in terms of discharge destination and timing previsions. This article reviews the recent literature regarding the predictive factors for gait recovery and the best recommendations in terms of gait rehabilitation in stroke patients. Trunk control and lower limb motor control (e.g. hip extensor muscle force) seem to be the best predictors of gait recovery as shown by the TWIST algorithm, which is a simple tool that can be applied in clinical practice at 1 week post-stroke. In terms of walking performance, the 6-min walking test is the best predictor of community ambulation. Various techniques are available for gait rehabilitation, including treadmill training with or without body weight support, robotic-assisted therapy, virtual reality, circuit class training and self-rehabilitation programmes. These techniques should be applied at specific timing during post-stroke rehabilitation, according to patient’s functional status.


  1. 1.

    Stevens E, Emmett E, Wang Y, McKevitt C, Wolfe C (2018) The burden of stroke in Europe, report. Division of Health and Social Care Research, King’s College London, London

  2. 2.

    Jorgensen HS, Nakayama H, Raaschou HO, Olsen TS (1995) Recovery of walking function in stroke patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil 76(1):27–32

  3. 3.

    Harvey RL (2015) Predictors of functional outcome following stroke. Phys Med Rehabil Clin N Am 26(4):583–598

  4. 4.

    WHO (2007) International Classification of Functioning, Disability, and Health: Children & Youth Version: ICF-CY. World Health Organization

  5. 5.

    Kinoshita S, Abo M, Okamoto T, Tanaka N (2017) Utility of the revised version of the ability for basic movement scale in predicting ambulation during rehabilitation in poststroke patients. J Stroke Cerebrovasc Dis Off J Natl Stroke Assoc 26(8):1663–1669

  6. 6.

    KNGF (2014) KNGF guidelines: stroke. Royal Dutch Society for Physical Therapy (Koninklijk Nederlands Genootschap voor Fysiotherapie, KNGF)

  7. 7.

    Holsbeeke L, Ketelaar M, Schoemaker MM, Gorter JW (2009) Capacity, capability, and performance: different constructs or three of a kind? Arch Phys Med Rehabil 90(5):849–855

  8. 8.

    Perry J, Garrett M, Gronley JK, Mulroy SJ (1995) Classification of walking handicap in the stroke population. Stroke 26(6):982–989

  9. 9.

    Smith MC, Barber PA, Stinear CM (2017) The TWIST algorithm predicts time to walking independently after stroke. Neurorehabil Neural Repair 31(10–11):955–964

  10. 10.

    Winstein CJ, Stein J, Arena R, Bates B, Cherney LR, Cramer SC et al (2016) Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 47(6):e98–e169

  11. 11.

    Platz T (2019) Evidence-based guidelines and clinical pathways in stroke rehabilitation—an international perspective. Front Neurol 10:200

  12. 12.

    Kollen B, Kwakkel G, Lindeman E (2006) Longitudinal robustness of variables predicting independent gait following severe middle cerebral artery stroke: a prospective cohort study. Clin Rehabil 20(3):262–326

  13. 13.

    Veerbeek JM, Van Wegen EE, Harmeling-Van der Wel BC, Kwakkel G (2011) Is accurate prediction of gait in nonambulatory stroke patients possible within 72 hours poststroke? The EPOS study. Neurorehabil Neural Repair 25(3):268–274

  14. 14.

    Stinear CM, Byblow WD, Ward SH (2014) An update on predicting motor recovery after stroke. Ann Phys Rehabil Med 57(8):489–498

  15. 15.

    Collin C, Wade D (1990) Assessing motor impairment after stroke: a pilot reliability study. J Neurol Neurosurg Psychiatry 53(7):576–579

  16. 16.

    Fulk GD, He Y, Boyne P, Dunning K (2017) Predicting home and community walking activity poststroke. Stroke 48(2):406–411

  17. 17.

    Duncan PW, Sullivan KJ, Behrman AL, Azen SP, Wu SS, Nadeau SE et al (2011) Body-weight-supported treadmill rehabilitation after stroke. N Engl J Med 364(21):2026–2036

  18. 18.

    Kluding PM, Dunning K, O’Dell MW, Wu SS, Ginosian J, Feld J et al (2013) Foot drop stimulation versus ankle foot orthosis after stroke: 30-week outcomes. Stroke 44(6):1660–1669

  19. 19.

    Tudor-Locke C, Bassett DR Jr (2004) How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med (Auckl NZ) 34(1):1–8

  20. 20.

    Friedman PJ (1990) Gait recovery after hemiplegic stroke. Int Disabil Stud 12(3):119–122

  21. 21.

    Bland MD, Sturmoski A, Whitson M, Connor LT, Fucetola R, Huskey T et al (2012) Prediction of discharge walking ability from initial assessment in a stroke inpatient rehabilitation facility population. Arch Phys Med Rehabil 93(8):1441–1447

  22. 22.

    Jones PS, Pomeroy VM, Wang J, Schlaug G, Tulasi Marrapu S, Geva S et al (2016) Does stroke location predict walk speed response to gait rehabilitation? Hum Brain Mapp 37(2):689–703

  23. 23.

    Yelnik AP, Quintaine V, Andriantsifanetra C, Wannepain M, Reiner P, Marnef H et al (2017) AMOBES (Active Mobility Very Early After Stroke): a randomized controlled trial. Stroke 48(2):400–405

  24. 24.

    Bernhardt J, Langhorne P, Lindley RI, Thrift AG, Ellery F, Collier J et al (2015) Efficacy and safety of very early mobilisation within 24 h of stroke onset (AVERT): a randomised controlled trial. Lancet (Lond Engl) 386(9988):46–55

  25. 25.

    Stroke Foundation (2019) Clinical Guidelines for Stroke Management. Melbourne Australia

  26. 26.

    Mehrholz J, Thomas S, Elsner B (2017) Treadmill training and body weight support for walking after stroke. Cochrane Database Syst Rev.

  27. 27.

    Flansbjer UB, Holmback AM, Downham D, Patten C, Lexell J (2005) Reliability of gait performance tests in men and women with hemiparesis after stroke. J Rehabil Med 37(2):75–82

  28. 28.

    Perera S, Mody SH, Woodman RC, Studenski SA (2006) Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc 54(5):743–749

  29. 29.

    Eng JJ, Dawson AS, Chu KS (2004) Submaximal exercise in persons with stroke: test–retest reliability and concurrent validity with maximal oxygen consumption. Arch Phys Med Rehabil 85(1):113–118

  30. 30.

    Mehrholz J, Thomas S, Werner C, Kugler J, Pohl M, Elsner B (2017) Electromechanical-assisted training for walking after stroke. Cochrane Database Syst Rev.

  31. 31.

    de Rooij IJ, van de Port IG, Meijer JG (2016) Effect of virtual reality training on balance and gait ability in patients with stroke: systematic review and meta-analysis. Phys Ther 96(12):1905–1918

  32. 32.

    Cohen J (2013) Statistical power analysis for the behavioral sciences. Routledge, London

  33. 33.

    Faraone SV (2008) Interpreting estimates of treatment effects: implications for managed care. P T Peer Rev J Formul Manag 33(12):700–711

  34. 34.

    English C, Hillier SL, Lynch EA (2017) Circuit class therapy for improving mobility after stroke. Cochrane Database Syst Rev.

  35. 35.

    Aaslund MK, Moe-Nilssen R, Gjelsvik BB, Bogen B, Naess H, Hofstad H et al (2017) A longitudinal study investigating how stroke severity, disability, and physical function the first week post-stroke are associated with walking speed six months post-stroke. Physiother Theory Pract 33(12):932–942

  36. 36.

    Cumming TB, Thrift AG, Collier JM, Churilov L, Dewey HM, Donnan GA et al (2011) Very early mobilization after stroke fast-tracks return to walking: further results from the phase II AVERT randomized controlled trial. Stroke 42(1):153–158

  37. 37.

    de Rooij IJM, van de Port IGL, Visser-Meily JMA, Meijer JG (2019) Virtual reality gait training versus non-virtual reality gait training for improving participation in subacute stroke survivors: study protocol of the ViRTAS randomized controlled trial. Trials 20(1):89

  38. 38.

    Cook DJ, Mulrow CD, Haynes RB (1998) Systematic reviews: synthesis of best evidence for clinical decisions. Ann Intern Med 126(5):376–380

  39. 39.

    Rother ET (2007) Systematic literature review × narrative review. Acta Paul Enferm 20:v–vi

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via Gait rehabilitation after stroke: review of the evidence of predictors, clinical outcomes and timing for interventions | SpringerLink


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[Abstract] Implementing biomarkers to predict motor recovery after stroke.



There is growing interest in using biomarkers to predict motor recovery and outcomes after stroke. The PREP2 algorithm combines clinical assessment with biomarkers in an algorithm, to predict upper limb functional outcomes for individual patients. To date, PREP2 is the first algorithm to be tested in clinical practice, and other biomarker-based algorithms are likely to follow.


This review considers how algorithms to predict motor recovery and outcomes after stroke might be implemented in clinical practice.


There are two tasks: first the prediction information needs to be obtained, and then it needs to be used. The barriers and facilitators of implementation are likely to differ for these tasks. We identify specific elements of the Consolidated Framework for Implementation Research that are relevant to each of these two tasks, using the PREP2 algorithm as an example. These include the characteristics of the predictors and algorithm, the clinical setting and its staff, and the healthcare environment.


Active, theoretically underpinned implementation strategies are needed to ensure that biomarkers are successfully used in clinical practice for predicting motor outcomes after stroke, and should be considered in parallel with biomarker development.

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[ARTICLE] Methods for an Investigation of Neurophysiological and Kinematic Predictors of Response to Upper Extremity Repetitive Task Practice in Chronic Stroke – Full Text PDF



To demonstrate the feasibility of algorithmic prediction utilizing a model of baseline arm movement, genetic factors, demographic characteristics, and multi-modal assessment of the structure and function of motor pathways. To identify prognostic factors and the biological substrate for reductions in arm impairment in response to repetitive task practice.


This prospective single-group interventional study seeks to predict response to a repetitive task practice program using an intent-to-treat paradigm. Response is measured as a change of ≥5 points on the Upper Extremity Fugl-Meyer from baseline to final evaluation (at the end of training).


General community


Anticipated enrollment of 96 community-dwelling adults with chronic stroke (onset ≥6 months) and moderate to severe residual hemiparesis of the upper limb as defined by a score of 10-45 points on the Upper Extremity Fugl-Meyer.


The intervention is a form of repetitive task practice using a combination of robot-assisted therapy coupled with functional arm use in real-world tasks administered over 12 weeks.

Main outcome measures

Upper extremity Fugl-Meyer Assessment (primary outcome), Wolf Motor Function Test, Action Research Arm Test, Stroke Impact Scale, questionnaires on pain and expectancy, magnetic resonance imaging, transcranial magnetic stimulation, arm kinematics, accelerometry, and a saliva sample for genetic testing.


Methods for this trial are outlined and an illustration of inter-individual variability is provided by example of two participants who present similarly at baseline but achieve markedly different outcomes.


This article presents the design, methodology, and rationale of an ongoing study to develop a predictive model of response to a standardized therapy for stroke survivors with chronic hemiparesis. Applying concepts from precision medicine to neurorehabilitation is practicable and needed to establish realistic rehabilitation goals and to effectively allocate resources.

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via Methods for an Investigation of Neurophysiological and Kinematic Predictors of Response to Upper Extremity Repetitive Task Practice in Chronic Stroke – ScienceDirect

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[REVIEW ARTICLE] Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy – Full Text

Blood biomarkers have been explored for their potential to provide objective measures in the assessment of traumatic brain injury (TBI). However, it is not clear which biomarkers are best for diagnosis and prognosis in different severities of TBI. Here, we compare existing studies on the discriminative abilities of serum biomarkers for four commonly studied clinical situations: detecting concussion, predicting intracranial damage after mild TBI (mTBI), predicting delayed recovery after mTBI, and predicting adverse outcome after severe TBI (sTBI). We conducted a literature search of publications on biomarkers in TBI published up until July 2018. Operating characteristics were pooled for each biomarker for comparison. For detecting concussion, 4 biomarker panels and creatine kinase B type had excellent discriminative ability. For detecting intracranial injury and the need for a head CT scan after mTBI, 2 biomarker panels, and hyperphosphorylated tau had excellent operating characteristics. For predicting delayed recovery after mTBI, top candidates included calpain-derived αII-spectrin N-terminal fragment, tau A, neurofilament light, and ghrelin. For predicting adverse outcome following sTBI, no biomarker had excellent performance, but several had good performance, including markers of coagulation and inflammation, structural proteins in the brain, and proteins involved in homeostasis. The highest-performing biomarkers in each of these categories may provide insight into the pathophysiologies underlying mild and severe TBI. With further study, these biomarkers have the potential to be used alongside clinical and radiological data to improve TBI diagnostics, prognostics, and evidence-based medical management.


Traumatic brain injury (TBI) is a common cause of disability and mortality in the US (1) and worldwide (2). Pathological responses to TBI in the CNS include structural and metabolic changes, as well as excitotoxicity, neuroinflammation, and cell death (34). Fluid biomarkers that may track these injury and inflammatory processes have been explored for their potential to provide objective measures in TBI assessment. However, at present there are limited clinical guidelines available regarding the use of biomarkers in both the diagnosis of TBI and outcome prediction following TBI. To inform future guideline formulation, it is critical to distinguish between different clinical situations for biomarker use in TBI, such as detection of concussion, prediction of positive and negative head computed tomography (CT) findings, and prediction of outcome for different TBI severities. This allows for comparisons to determine which biomarkers may be used most appropriately to characterize different aspects of TBI.

The identification of TBI severity has become a contentious issue. Currently, inclusion in TBI clinical trials is primarily based on the Glasgow Coma Scale (GCS), which stratifies patients into categories of mild, moderate, and severe TBI. The GCS assesses consciousness and provides prognostic information, but it does not inform the underlying pathologies that may be targeted for therapy (56). Furthermore, brain damage and persistent neurological symptoms can occur across the spectrum of TBI severity, limiting the use of GCS-determined injury severity to inform clinical management. Biomarkers in TBI have the potential to provide objective and quantitative information regarding the pathophysiologic mechanisms underlying observed neurological deficits. Such information may be more appropriate for guiding management than initial assessments of severity alone. Since the existing literature primarily focuses on applications of biomarkers in either suspected concussion, mild TBI (mTBI), or severe TBI (sTBI), we will discuss biomarker usage in these contexts.

Concussion is a clinical syndrome involving alteration in mental function induced by head rotational acceleration. This may be due to direct impact or unrestrained rapid head movements, such as in automotive crashes. Although there are over 30 official definitions of concussion, none include the underlying pathology. Missing from the literature have been objective measures to not only identify the underlying pathology associated with the given clinical symptoms, but also to indicate prognosis in long-term survival. Indeed, current practices in forming an opinion of concussion involve symptom reports, neurocognitive testing, and balance testing, all of which have elements of subjectivity and questionable reliability (7). While such information generally reflects functional status, it does not identify any underlying processes that may have prognostic or therapeutic consequences. Furthermore, because patients with concussion typically present with negative head CT findings, there is a potential role for blood-based biomarkers to provide objective information regarding the presence of concussion, based on an underlying pathology. This information could inform management decisions regarding resumption of activities for both athletes and non-athletes alike.

Blood-based biomarkers have utility far beyond a simple detection of concussion by elucidating specific aspects of the injury that could drive individual patient management. For example, biomarkers may aid in determining whether a mTBI patient presenting to the emergency department requires a CT scan to identify intracranial pathology. The clinical outcome for a missed epidural hematoma in which the patient is either discharged or admitted for routine observation is catastrophic; 25% are left severely impaired or dead (8). The Canadian CT Head Rule (9) and related clinical decision instruments achieve high sensitivities in predicting the need for CT scans in mild TBI cases. However, they do this at specificities of only 30–50% (10). Adding a blood biomarker to clinical evaluation may be useful to improve specificity without sacrificing sensitivity, as recently suggested (11). In addition, given concern about radiation exposure from head CT scans in concussion cases, particularly in pediatric populations, identification of patients who would be best assessed with neuroimaging is crucial. Thus, the use of both sensitive and specific biomarkers may serve as cost-effective tools to aid in acute assessment, especially in the absence of risk factors for intracranial injury (12). S-100B, an astroglial protein, has been the most extensively studied biomarker for TBI thus far and has been incorporated into some clinical guidelines for CT scans (1314). However, S-100B is not CNS-specific (1516) and has shown inconsistent predictive capacity in the outcome of mild TBI (1718). Given that several other promising biomarkers have also been investigated in this context, it is important to evaluate and compare the discriminative abilities of S-100B with other candidate blood-based biomarkers for future use.

Blood biomarkers also have the potential to help predict unfavorable outcomes across the spectrum of TBI severity. Outcome predication is difficult; in mTBI, existing prognostic models performed poorly in an external validation study (19). Identifying biomarkers that best predict delayed recovery or persistent neurological symptoms following mTBI would help with the direction of resources toward patients who may benefit most from additional rehabilitation or prolonged observation. In sTBI, poorer outcome has often been associated with a low GCS score (20). However, factors such as intoxication or endotracheal intubation may make it difficult to assess GCS reliably in the acute setting (2122). The addition of laboratory parameters to head CT and admission characteristics have improved prognostic models (23). Thus, prognostic biomarkers in sTBI could help determine whether patients are likely to benefit from intensive treatment. Several candidate biomarkers that correlate with various pathologies of mild and severe TBI have been studied (24), but their relative prognostic abilities remain unclear.

Existing reviews on biomarkers in TBI have provided valuable insight into the pathologic correlates of biomarkers, as well as how biomarkers may be used for diagnosis and prognosis (2531). However, there has been no previous quantitative comparison of the literature regarding biomarkers’ discriminative abilities in specific clinical situations. Here, we compare existing studies on the discriminative abilities of serum biomarkers for four commonly studied clinical situations: detecting concussion, predicting intracranial damage after mTBI, predicting delayed recovery after mTBI, and predicting adverse outcome after sTBI.[…]


Continue —-> Frontiers | Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy | Neurology

Figure 2. Anatomical locations of potential TBI biomarkers. The biomarkers included in this schematic all rated as “good” (AUC=0.800.89) or better for any of the four clinical situations studied (detecting concussion, predicting intracranial damage after concussion, predicting delayed recovery after concussion, and predicting adverse outcome after severe TBI). Biomarkers with a pooled AUC <0.8 are not shown. 1Also found in adipose tissue; 2synthesized in cells of stomach and pancreas; may regulate HPA axis; 3found mostly in pons; 4also found extracellularly; 5lectin pathway of the complement system; 6also found in endothelial cells. BBB, blood brain barrier. ECM, Extracellular matrix. Image licensed under Creative Commons Attribution-ShareAlike 4.0 International license. See Supplementary Material for image credits and licensing.

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[ARTICLE] Course of Social Participation in the First 2 Years After Stroke and Its Associations With Demographic and Stroke-Related Factors – Full text

Background. Many persons with stroke experience physical, cognitive, and emotional problems that contribute to restrictions in social participation. There is, however, a lack of knowledge on the long-term course of participation over time post-stroke.

Objective. To describe the time course of participation up to 2 years post-stroke and to identify which demographic and stroke-related factors are associated with this time course.

Methods. This was a multicenter, prospective cohort study following 390 persons with stroke from hospital admission up to 2 years (at 2, 6, 12, and 24 months). Multilevel modeling with linear and quadratic time effects was used to examine the course of the frequency of vocational and social/leisure activities, experienced restrictions, and satisfaction with participation.

Results. The frequency of vocational activities increased up to 1 year post-stroke and leveled off thereafter. Older and lower-educated persons showed less favorable courses of participation than younger and higher-educated persons, respectively. The frequency of social/leisure activities decreased post-stroke. Participation restrictions declined up to 1 year post-stroke and leveled off thereafter. Persons dependent in activities of daily living (ADL) kept experiencing more restrictions throughout time than independent persons. Satisfaction with participation increased slightly over time.

Conclusions. Changes in participation occurred mostly in the first year post-stroke. Particularly older and lower-educated persons, and those dependent in ADL showed less favorable courses of participation up to 2 years post-stroke. Clinicians can apply these findings in identifying persons most at risk of long-term unfavorable participation outcome and, thus, target rehabilitation programs accordingly.

Stroke can lead to long-lasting physical problems such as mobility limitations,1cognitive problems such as attention or memory deficits,2 and emotional problems such as anxiety,3,4 depressive symptoms,35 and fatigue.4,6 The population of persons surviving a stroke7,8 increases, consistent with major improvements in acute stroke care (eg, stroke units, thrombolysis, and thrombectomy9,10), but this also means that more people have to deal with the long-lasting consequences of stroke.11,12 These consequences contribute to the deterioration of social participation post-stroke.1317 Importantly, persons with stroke view social participation (participation hereafter) as a central aspect of their recovery.18,19

Participation can be defined as involvement in a life situation such as paid work, family, or community life,17 which consists of actual performed activities,20 such as the frequency of observable actions and behaviors,2123 and the subjective experience of persons,20 such as experienced restrictions and satisfaction.2123

In previous studies, it was observed that the frequency of activities decreases in persons with stroke, relative to their premorbid levels.16,2428 This particularly applies to vocational activities (work, unpaid work, and household activities), but social activities decrease after stroke, too.28 Four months after discharge from outpatient rehabilitation, 50% of persons with stroke still experienced participation problems.29Social activity levels have been reported to be lower in persons with stroke at 1 year post-stroke than in healthy controls,30 a level that remained stable up to 3 years.31Past studies showed that only 39% of persons with stroke were satisfied with their lives as a whole after 1 year,16 which might be even lower up to 3 years post-stroke,32 especially in socially inactive persons.33

Although studies have shed some light on the course of participation over time post-stroke, it is difficult to get a good understanding of how levels of participation develop and change over time. This is a result of the use of cross-sectional designs,16,24,26,27,33 longitudinal designs limited to either only the first 6 months13,25,28,29 or only the long-term levels of participation after stroke,31,32,34studies only incorporating 2 time points,35 and many different participation measures, some measuring the frequency of activities and others the subjective experience of participation.36

Research into factors associated with participation post-stroke could lead to identifying possible risk factors of an unfavorable outcome. Earlier studies showed that demographic factors such as older age at stroke onset,14,37 lower levels of education,29,38 and female sex37 were related to a less favorable outcome in terms of participation, along with stroke-related factors such as dependence in activities of daily living (ADL),39,40 more severe stroke,37 and lower levels of cognitive functioning.26,29 However, these factors are yet to be examined in relation to the course of participation over time and as such to be identified as possible risk factors.

To get a more detailed and comprehensive understanding of participation over time, it is necessary to include repeated measurements of objective (ie, frequency of activities) as well as subjective (ie, experienced restrictions and satisfaction) aspects of participation. Furthermore, it is important to identify persons in the early stage after stroke, who are at risk of an unfavorable outcome in the long term. At this point in time, potential risk factors can be easily determined through available information, including demographics and stroke-related information, and rehabilitation care can be provided. Consequently, we studied participation over a 2-year follow-up in a clinical cohort of persons with stroke in order to answer the following research questions: how does participation develop over the first 2 years after stroke in terms of frequency, restrictions, and satisfaction? Moreover, which demographic and stroke-related factors are associated with this time course?[…]


Continue —> Course of Social Participation in the First 2 Years After Stroke and Its Associations With Demographic and Stroke-Related Factors – Daan P. J. Verberne, Marcel W. M. Post, Sebastian Köhler, Leeanne M. Carey, Johanna M. A. Visser-Meily, Caroline M. van Heugten, 2018

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[Abstract] Predicting Long-Term Global Outcome after Traumatic Brain Injury: Development of a Practical Prognostic Tool Using the Traumatic Brain Injury Model Systems National Database

For patients surviving serious traumatic brain injury (TBI), families and other stakeholders often desire information on long-term functional prognosis, but accurate and easy-to-use clinical tools are lacking. We aimed to build utilitarian decision trees from commonly collected clinical variables to predict Glasgow Outcome Scale (GOS) functional levels at 1, 2, and 5 years after moderate-to-severe closed TBI. Flexible classification tree statistical modeling was used on prospectively collected data from the TBI-Model Systems (TBIMS) inception cohort study. Enrollments occurred at 17 designated, or previously designated, TBIMS inpatient rehabilitation facilities. Analysis included all participants with nonpenetrating TBI injured between January 1997 and January 2017. Sample sizes were 10,125 (year-1), 8,821 (year-2), and 6,165 (year-5) after cross-sectional exclusions (death, vegetative state, insufficient post-injury time, and unavailable outcome). In our final models, post-traumatic amnesia (PTA) duration consistently dominated branching hierarchy and was the lone injury characteristic significantly contributing to GOS predictability. Lower-order variables that added predictability were age, pre-morbid education, productivity, and occupational category. Generally, patient outcomes improved with shorter PTA, younger age, greater pre-morbid productivity, and higher pre-morbid vocational or educational achievement. Across all prognostic groups, the best and worst good recovery rates were 65.7% and 10.9%, respectively, and the best and worst severe disability rates were 3.9% and 64.1%. Predictability in test data sets ranged from C-statistic of 0.691 (year-1; confidence interval [CI], 0.675, 0.711) to 0.731 (year-2; CI, 0.724, 0.738). In conclusion, we developed a clinically useful tool to provide prognostic information on long-term functional outcomes for adult survivors of moderate and severe closed TBI. Predictive accuracy for GOS level was demonstrated in an independent test sample. Length of PTA, a clinical marker of injury severity, was by far the most critical outcome determinant.


via Predicting Long-Term Global Outcome after Traumatic Brain Injury: Development of a Practical Prognostic Tool Using the Traumatic Brain Injury Model Systems National Database | Journal of Neurotrauma

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[ARTICLE] Moderate Traumatic Brain Injury: Clinical Characteristics and a Prognostic Model of 12 Month Outcome – Full Text



Patients with moderate traumatic brain injury (TBI) are often studied together with patients with severe TBI, even though expected outcome is better. Therefore, we aimed to describe patient characteristics and 12-month outcome, and to develop a prognostic model based on admission data, specifically for patients with moderate TBI.


Patients with Glasgow Coma Scale score of 9-13 and age ≥16 years were prospectively enrolled in two level I trauma centers in Europe. Glasgow Outcome Scale Extended (GOSE) score was assessed at 12 months. A prognostic model predicting moderate disability or worse (GOSE score ≤6) as opposed to a good recovery, was fitted by penalized regression. Model performance was evaluated by area under the curve (AUC) of the receiver operating characteristics curves.


Of the 395 enrolled patients 81% had intracranial lesions on head CT and 71% were admitted to an intensive care unit. At 12 months, 44% were moderately disabled or worse (GOSE score ≤6), while 8% were severely disabled and 6% died (GOSE score ≤4). Higher age, lower GCS score, no day-of-injury alcohol intoxication, presence of a subdural hematoma, occurrence of hypoxia and/or hypotension and pre-injury disability were significant predictors of GOSE score ≤6 (AUC = 0.80).


Patients with moderate TBI exhibit characteristics of significant brain injury. Although few patients died or experienced severe disability, 44% did not experience good recovery, indicating that follow-up is needed. The model is a first step in development of prognostic models for moderate TBI that are valid across centers.


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via Moderate Traumatic Brain Injury: Clinical Characteristics and a Prognostic Model of 12 Month Outcome – ScienceDirect

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[BLOG  POST] An Integrative Brain Injury Treatment Approach! 

Integrative Brain Injury Treatment plan helps The moment you hear the doctor say you have a brain injury your life stops, no different than if you were told that you have Cancer. Your first reaction is numbness, and then fear sets in. I know personally, for I was diagnosed with both!

Prognosis after Concussion (mild traumatic brain injury)

The first question that you ask is, “Am I going to get better?’ And most PCPs, neurologists and rehabilitation clinicians will say, “Wait and see.” The usual prescription for a concussion is to go home and rest, limit your activity, especially TV, electronics and sports. Nothing is said about changing your diet, or treatments such as water therapy which can actually help you heal your brain. Since this advice is typically not given, most people who sustain a Concussion, a mild traumatic brain injury, are sent home. Without proper treatment, these symptoms not only don’t go away, they worsen, leading to Post-Concussion Syndrome (PCS).

In my previous blog, I mentioned both a patient who fell off a horse and her symptoms were dismissed, and the Customer Service Representative, who has had symptoms for 8 years, without any help or relief. Because concussions are generally misdiagnosed, undiagnosed, and misunderstood, there are unfortunately many more people out there with experiences like theirs who are living with PCS.

Help and Hope! – There is a Way!

Long before my brain injury on March 5, 1990, I opened the first integrative health team in New England in 1979. The practice was called Lafayette Counseling, Inc. We had three locations and over 256 patients at the time of my accident. Our integrative team was unique because it included conventional, complementary and alternative approaches to treatment for trauma, educational, health issues and sports. We treated many issues were from chronic pain, chronic illness, including Cancer, Irritable Bowel Syndrome (IBS), learning disabilities, ADD, ADHD, incest, rape, abuse, and Post Traumatic Stress Disorder (PTSD).  Also, we did hypnosis for Pain Control during Childbirth, which I’m published in the field, and for work in Cancer, along with methods for Peak Performance Training.

The team worked seamlessly together, promoting the welfare of each person who came to the practice the team. We consulted each other and conducted team meetings for every one of the 256 patients we served. We had a psychiatrist, neuropsychologist, psychologist, neurologist, gastroenterologist, endocrinologist, physical therapist, speech and language pathologist, polarity therapist, massage therapist, acupuncturist and homeopathic practitioner as well as psychiatric nurses and social workers. It was during this time I developed Dr. Diane®’s 5 Prong Approach to treatment. It was a thriving practice… until my accident occurred.

After My Brain Injury

During the four years following my accident, I was not offered any services or treatments to help me progress in my rehabilitation and to help me regain my life. In 1991, I had to close my practice since I could no longer manage it. In 1994, I was told by one of the neurologists that I needed to see a psychiatrist to help me deal with the fact I was permanently brain damaged and that I would never walk or talk properly every again. I remember initially wishing I had died in the accident. I even contemplated suicide.  I was no longer the wife or mother I wanted to be.  I had 3 young children at home, and I could not function. It was a dark and lonely place. Then after a period of grieving, I decided, “Doc, you are going to heal yourself”. It was then I realized the amazing professionals that once made up the integrative team at my practice. I contacted the various team members and engaged them in my own rehabilitation, starting with polarity and acupuncture. Prior to my brain injury I had gone on a 6-month elimination diet to deal with food allergies. From this information, I realized after my brain injury that if I ate certain foods that my symptoms would get worse. I realized that changing my diet was improving my symptoms. Yet, no doctor could have given me that advice. I followed those same guidelines from the earlier elimination diet.  I started with one food and noted, along with the family members, who were living with all my symptoms, if that food made my symptoms better or worse. After 6 months, it was extremely clear that certain foods only made my symptoms worse, while others truly did help.  With this knowledge, I developed my brain food diet, which to this day has helped every single patient.

During this period, I was introduced to Dr. Igor Burdenko, Ph.D., the founder and chairman of the Burdenko Water and Sports Therapy Institute. Dr. Burdenko developed The Burdenko Method, a practical application of water and land exercises based on holistic approach to rehabilitation, conditioning, and training. The Burdenko Method changed my life, as did being on my brain health diet and being introduced to neurofeedback.

I learned about neurofeedback through a presentation given at a brain injury support group by Janet Bloom, who trained with Dr. Margaret Ayers. I had been trained in hypnosis and biofeedback, yet I had never heard of neurofeedback.  I am so grateful I attended that brain injury support group and discovered neurofeedback.  These three methods were the vital forces integral to regaining my life.

With all the information I acquired from these methods and with the help of my previous integrative team, I set out to write a book to help other like myself to regain their life again.  I co-authored, Coping with Mild Head Injury that was later changed to Coping with Mild Traumatic Brain Injury. The entire focus of this book was and is about the integrative approach that helped me rebuild my life. The book contains Conventional, Complementary and Alternative approaches to taking back your life after brain injury.  The book was released in 1997, the same year I was able to resume my practice, as a solo practitioner. I still was not ready cognitively to resume all of the responsibilities of having an integrative team of experts working with me.

From 1997 until 2007, I worked alone, yet was gradually meeting and working different practitioners developing a new integrative team with the current brain health experts.

Major Difference with Integrative Brain Injury Team

Just as the previous integrative team prior to my brain injury, this new team of brain health experts works seamlessly together, in true integrative fashion. One of my biggest complaints own with medical team treating my brain injury was they never spoke to one another. I can’t tell you how many times I was informing a specific doctor on what the other doctor had said or was doing.  They did NOT communicate with one another.  There was NO joint documentation of my files.  And often there was disagreement of methods of medication or procedures.

Having had this awful experience, I vowed that this would never happen to any patient we consult with or treat in our practice.  The Dr. Diane Brain Health team each has their own private practice, and are throughout the US.  Also, I’m extremely fortunate that since 1997, I met and personally know a network of national and international practitioners that we work with.  Hence, where ever you are located in the world, we can either refer you to a specialist in your area or we can provide remote, virtual, now called (tele-health) services.


In 2011, Penguin Publishing asked me to write another book. I agreed if the book’s focus was on the integrative treatment approach that was working so well in my practice. They agreed. Thus, Barbara Albers Hill and I set out to write a book with the main focus of an integrative approach of treatment for brain injury. The book came out in 2013.

Susan Connors, the president and CEO of the Brain Injury Association wrote the following review which appears on the back cover of the book:  “Coping with Concussion and Mild Traumatic Brain Injury” is a long-awaited prescription for the millions who experience a so-called mild TBI and for their families and care providers.  Incorporating detailed information, practical suggestions and personal insight, Dr. Stoler, has compiled a must-have encyclopedia for managing life after a Concussion.”


My 5 Prong Approach evolved as part of my own journey in regaining my life after my brain injury, and with working with the brain injury patients and consult clients upon my return to active practice. What I realized is that even though there are common symptoms related to injury to the brain, each person is unique. Because of this, each treatment program for similar symptoms has to be different in order to achieve an excellent outcome. I believe the key to healing is to view and treat each person from the five distinct views that make up our approach: physical, psychological, emotional, spiritual and energy while looking for the core issue. Often these areas are connected and each needs to be addressed to ultimately reach your goal, Each integrative team member brings their own specialty and together we develop customized treatment programs based on the individual’s unique needs and goals and symptoms, using a wide-range of traditional, alternative, and complementary methods.


  • Neurofeedback, Biofeedback, and QEEG
  • Nutrition Education and Nutrition Response Testing
  • Physical Therapy/CranioSacral Therapy
  • Water Therapy/Burdenko Method
  • Speech-Language Pathology
  • Cognitive Remediation Therapy
  • Energy Psychology
  • Energy Healing
  • Reiki
  • Acupuncture
  • Other Energy Healing Treatments- Tom Tam, Evan Pantazi
  • Hypnosis and Relaxation Techniques
  • Massage and Muscular Therapy
  • Psychiatry and Psychopharmacology
  • Psychotherapy
  • Cognitive Behavioral Therapy
  • Aromatherapy
  • Bach Flower Essence
  • BAUD
  • Brainspotting
  • Chiropractic
  • Homeopathy
  • iListen Therapy™
  • Interactive Metronome
  • Irlen Method
  • Light Therapy + Photonic Modulation
  • The Tomatis Method®
  • Care Management


In the following weeks, my blogs will be focused on introducing you to the various individual team members, their background, beliefs and philosophy about specific treatment and the importance of being a part of an integrative team.  Here is a brief introduction of the team members. For more detailed information about each member and the services they offer, please click on the links.

Dr. Diane Roberts Stoler, Ed.D.

Dr. Diane® is a Neuropsychologist, Board Certified Health Psychologist,  and Board Certified Sports Psychologist with a focus on brain fitness and brain rehabilitation.  She has worked with amateur, professional and Olympic athletes to help them achieve Peak Performance and be in “The Zone”.

Amy Karas MS, CCC-SLP Speech-Language Pathologist

Certified Speech-Language Pathologist with over 19 years’ experience working with acquired brain injuries, learning disabilities and other social and communication disorders. Amy’s approach emphasizes understanding what someone needs to improve quality of life, task efficiency and effectiveness and maximizing independence.

Clara Diebold, Energy Healing Practitioner, Reiki Master

Clara practices several forms of energy healing, including Reiki, HBLU, and techniques for emotional processing. She is a Reiki Master trained in the Usui Shiki Ryoho tradition by John and Lourdes Gray.

Paul Soper, M.M., RCTC

Specializing in Biofeedback, Brain Training, Listening Training, and Neurofeedback, Paul earned his certification in the first authorized Tomatis training in the US at Spectrum Center in Bethesda, MD, and was trained in neurofeedback and neuroscience at ESII and BrainMaster Institute.

Martha Lindsay, MS, CNE, certified in Nutrition Response Testing℠, GAPS certified practitioner

Offering a muscle testing technique is used to choose the most appropriate specific nutrition products for each person. The specific nutritional program thus chosen enhances that individual’s immune system function which then helps the brain to function more efficiently.

Joan Flynn, Craniosacral, Physical Therapist

Joan is certified in CranioSacral Therapy from the Upledger Institute. She has an intuitive and insightful approach to her work.  She treats chronic pain, stroke, alignment disorders, and most orthopedic problems.

Wendy Keiver-Hewett, NCTMB, LMT, Massage Therapist, Muscle-Release Therapist

Wendy is a Nationally Licensed Massage Therapist and Muscle Release Therapist.  Muscle release technique can break down scar tissue, lengthen a muscle, restore muscle memory and relieve pain.

Jennifer Stanley, LMT Massage Therapist

With over ten years’ experience, Jennifer specializes in Deep Tissue, Swedish Eflurage and Sports Massage. Using Reflexology and Shiatsu in addition to traditional massage, she intuitively combines these techniques to release muscle tension and promote relaxation and wellness.

Karen Campbell, CMC – Certified Care Manager

Care management can increase the quality of life for the senior or disabled adult, improve the quality of care, and to reduce caregiver stress. Karen specializes in care management working with seniors, adults with disabilities, and the families that love them.

Dr. Igor Burdenko, Ph.D., Sports Therapist, Water Therapist

Dr. Burdenko, founder and chairman of the Burdenko Water and Sports Therapy Institute, is one of the world’s leading authorities on the use of water for rehabilitation, conditioning, and training.

William Mogan, L.Ac. Acupuncturist, Chinese Herbal Medicine

William specializes in TBI, Headaches/Migraines, chronic pain, chronic illness, sleep and insomnia issues. He is nationally board certified by the National Association of Acupuncture and Oriental Medicine (NCCAOM) and licensed in Massachusetts.

David Sollars, MAc., LAc. Acupuncturist, Chinese Herbal Medicine, Homeopathic

David founded a series of Integrative Medical clinics that pioneered the then uncommon practice of a combined conventional and integrative medical staff. Focus areas include: Breaking the Wellness Barrier with solutions for stress, anxiety and depression, Developing Patient Medical Leadership Skills, Healthy Aging at Home, Successful Engagement with Wellness at Work and Ancient Solutions for Modern Problems.

Dr. Paul Schoonman, DC, Chiropractor

Paul obtained his undergraduate training in Biology at the University of Connecticut followed by graduate education at the National College of Chiropractic in Lombard, Illinois. He graduated cum laude, with a doctorate in Chiropractic in 1992. He complements his chiropractic education with an extensive postgraduate program in rehabilitation, which help patients manage some of the most complicated and/or chronic cases of musculo-skeletal pain.

Dr. Jorge Gonzalez, MD Neurologist

Primary area of interest centers on head injuries, migraines, seizures, movement disorders, neuropathies, and the injection of botulinum toxin (botox) in the treatment of migraines, headaches, and similar forms of pain, muscle contractions and movement disorders, as well as Alzheimer’s disease, stroke, and epilepsy. One of the many qualities that set Dr. Gonzales apart from the rest is his acceptance of alternative approaches to migraine treatment and palliation.

Dr. Sharon Barrett, MD, Psychopharmacologist, Psychiatrist

Board Certified adult psychiatrist with over 25 years’ experience treating people with Brain Injury, Fibromyalgia and Chronic Fatigue Syndrome. She is a graduate of Emory University School of Medicine, and completed her residency at the Beth Israel Deaconess in Boston, Massachusetts.

Dr Kathleen O’Neil-Smith, MD Endocrinologist, FAARM

Dr.O’Neil-Smith is a magna cum laude graduate of Boston University Medical School. She completed an internship in pathology at Massachusetts General Hospital followed by an internship and residency in internal medicine at the Brigham and Women’s Hospital in Boston. Dr. O’Neil-Smith has an extensive background in nutrition, applied physiology and sports medicine.

Tom Tam, Acupuncturist

Tom is a licensed Acupuncturist who has also practiced Tai Chi and Chi Gong since 1975, specializing in Acupuncture, Qui Gong. Tom formed his own healing system, and wrote the Tom Tam Healing System (1995). Also, he wrote a Chinese healing book, An Zhen – The Palpation diagnose (2005). This book combined the west and east medical knowledge and formed a new theory for the understanding and healing the difficulty disease.

Evan Pantazi, Oriental Body Work and Nerve Trauma Instructor

Evan has formulated a new and highly advanced method of Kyusho (Vital Point) for use in Health, Martial, Intimacy Enhancement and Law Enforcement. Based on the ancient understanding of acupuncture and pressure point massage methods, but adapted with modern science… the vital point is that all of us can easily rid the body of common ailments.

Integrative Brain Injury Consult

There are many practices that call themselves “integrative”.  However, if you call and ask you will typically find the integrative team keep does not keep the same notes or meet to discuss your individual needs or treatment. This is norm at Dr. Diane Brain Health, not the exception.

Whether you are looking for help restoring your Brain Health and regain your life again……There is a Way! ®


With over 30 years of experience as a Neuropsychologist, Board Certified Health Psychologist, Board Certified Sports Psychologist and brain injury survivor Dr. Diane can help you!


Schedule a personal consult today with Dr. Diane®

please call us at 800-500-9971 or submit a contact form.

Source: Dr. Diane Brain Health | An Integrative Brain Injury Treatment Approach! | Dr. Diane Brain Health

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[ARTICLE] Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? – Full Text

Background. There is growing interest to establish recovery biomarkers, especially neurological biomarkers, in order to develop new therapies and prediction models for the promotion of stroke rehabilitation and recovery. However, there is no consensus among the neurorehabilitation community about which biomarker(s) have the highest predictive value for motor recovery. Objective. To review the evidence and determine which neurological biomarker(s) meet the high evidence quality criteria for use in predicting motor recovery. Methods. We searched databases for prognostic neuroimaging/neurophysiological studies. Methodological quality of each study was assessed using a previously employed comprehensive 15-item rating system. Furthermore, we used the GRADE approach and ranked the overall evidence quality for each category of neurologic biomarker. Results. Seventy-one articles met our inclusion criteria; 5 categories of neurologic biomarkers were identified: diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), conventional structural MRI (sMRI), and a combination of these biomarkers. Most studies were conducted with individuals after ischemic stroke in the acute and/or subacute stage (~70%). Less than one-third of the studies (21/71) were assessed with satisfactory methodological quality (80% or more of total quality score). Conventional structural MRI and the combination biomarker categories ranked “high” in overall evidence quality. Conclusions. There were 3 prevalent methodological limitations: (a) lack of cross-validation, (b) lack of minimal clinically important difference (MCID) for motor outcomes, and (c) small sample size. More high-quality studies are needed to establish which neurological biomarkers are the best predictors of motor recovery after stroke. Finally, the quarter-century old methodological quality tool used here should be updated by inclusion of more contemporary methods and statistical approaches.

There is growing interest in establishing stroke recovery biomarkers. Researchers define stroke recovery biomarkers as surrogate indicators of disease state that can have predictive value for recovery or treatment response.1 Specifically, previous studies have suggested that better understanding of neurological biomarkers, derived from brain imaging and neurophysiological assessments, is likely to move stroke rehabilitation research forward.1,2

Recovery biomarkers acquired during the acute and subacute phases (acute—within 1 week after onset; subacute—between 1 week and 3 months after onset) may be vital to set attainable neurorehabilitation goals and to choose proper therapeutic approaches based on the recovery capacity. Furthermore, motor recovery prediction using neurological biomarkers in the chronic phase (more than 3 months after onset) can be useful to determine whether an individual will benefit from specific therapeutic interventions applied after the normal period of rehabilitation has ended. Hence, use of recovery biomarkers is likely to improve customization of physical interventions for individual stroke survivors regarding their capacity for recovery, and to facilitate development of new neurorehabilitation approaches.

There have been fundamental changes in recovery biomarkers from simple clinical behavioral biomarkers to brain imaging and neurophysiological biomarkers. In particular, a number of recent studies have shown that neurologic biomarkers (ie, neuroimaging and/or neurophysiological measures of brain) are more predictive of motor recovery than clinical behavioral biomarkers.35

Although there is some evidence that neurological biomarkers are more valuable as predictors of motor recovery than clinical behavioral biomarkers, there are significant gaps between the published evidence and clinical usage. First, there is no consensus on which specific neurological biomarkers would be best for prediction models.4,6,7 Viable neurological biomarker of motor recovery have evolved from lesion size and location, prevalent in the early 1990s8 to more contemporary complex brain network analysis variables.9 Despite this evolution, there is a paucity of high-level evidence for determining the most critical neurological biomarkers of motor recovery. A number of literature reviews and systematic reviews of studies published since the 1990s aimed to identify the most appropriate biomarkers of motor recovery or functional independence.8,1012 Among these reviews, only one by Schiemanck and colleagues8 assessed the evidence quality of neurologic biomarkers, while many focused on clinical measures (ie, clinical motor and/or functional measures).11 Their review was limited to only 13 studies that employed structural magnetic resonance imaging (sMRI) measures of lesion volume as neurologic biomarkers. Besides lesion volume derived from structural MRI, there are other viable neurological biomarkers of brain impairment. Therefore, this systematic review includes a broad set of relevant biomarkers for consideration as critical predictors for inclusion in motor recovery prediction models.

Furthermore, there is some evidence to suggest that multivariate prediction models that use neurological biomarkers in addition to clinical outcome measures are more accurate than those that use clinical outcome measures alone.2,13 However there is still no consensus about whether incorporating behavioral and neurological predictors in a multimodal prediction model is superior (ie, more accurate) to a univariate model that includes either behavioral or neurological predictors alone.

Taken together, this systematic review has 2 aims. The first is to conduct a critical and systematic comparison of selected studies to determine which neurological biomarker(s) is likely to have sufficient high-level evidence in order to render the most accurate prediction of motor recovery after stroke. The second aim is to identify whether adding clinical measures along with neurological biomarkers in the model improves the accuracy of the model compared to the models that use neurological biomarkers alone.

Continue —> Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review – Aug 08, 2016


Figure 1. Evidence search strategy diagram.

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[ARTICLE] Prediction of Walking and Arm Recovery after Stroke: A Critical Review – Full Text HTML


Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery. In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

1. Introduction

It would be useful to be able to predict recovery of walking and arm after stroke. Accurate predictions are needed so that clinicians can provide patients with prognoses, set goals, select therapies and plan discharge [1,2,3,4]. For example, if it was possible to predict with some certainty that a particular patient would be unable to walk independently at six months, the clinicians providing that patient with acute and subacute care might work toward a discharge goal of safe transfers. Therapy might involve carer training and equipment prescription rather than intensive gait training. The ability to make accurate predictions could reduce the length of stay in hospitals and enable efficient utilization of stroke care resources [4,5].
Several systematic reviews have identified strong predictors of walking and arm recovery after stroke [2,3,6]. In one systematic review of prognostic studies on walking, clinical variables such as age, severity of paresis and leg power were found to be strong predictors of walking after stroke (based on five studies, each of between 197 and 804 patients) [2]. In another systematic review of prognostic studies on arm recovery, clinical, neurophysiological and neuroimaging data were found to be strong predictors of arm recovery after stroke (based on 58 studies of 9–1197 patients) [3]. These clinical, neurophysiological and neuroimaging data included measures of upper limb impairment, upper limb function, lower limb impairment, motor and somatosensory evoked potentials, and measures obtained with diffusion tensor imaging [3].
In practice, clinicians base their predictions about clinical outcomes on multiple variables [7,8,9]. If multiple predictors are to be used to make prognoses, there needs to be a proper accounting of the independent (incremental) predictive value of each predictor variable. Therefore the most useful information about prognosis is likely to come from multivariate prediction models [7,8,9].
The research which underpins establishment of clinically useful multivariate prediction models involves several steps. First ‘development studies’ are conducted to build the multivariate prediction models [7]. Subsequently the predictive accuracy of the models is tested on new cohorts [7,10]. These studies are known as ‘validation studies’ [7]. It is recommended that prediction models should not be used in clinical practice until both development and validation studies have been conducted [7,10]. Once development and validation studies have been conducted, impact studies may be conducted, although the reality is that few reports of impact studies are published. Impact studies resemble clinical trials; they test the efficacy of use of prediction models on patient outcomes, clinician behaviour and cost-effectiveness of care [7,11]. Recent narrative reviews have provided updates on the prediction of motor recovery after stroke [5,12] but these reviews have not focused on development and validation studies of models for predicting walking and arm recovery.
This review provides a critical review of prediction models of walking and arm recovery after stroke. Studies were identified using the search strategy and inclusion criteria in the Appendix. The review begins in the second section with the definitions and measurements of walking and arm recovery. The third section provides a detailed description of the recommended process for developing and validating a prediction model because this process provides a benchmark against which prediction modelling studies of walking and arm recovery can be evaluated. The fourth section critically appraises development and validation studies of walking and arm recovery with the aim of identifying multivariate models that could potentially be implemented in clinical practice. Much has been written about the role of neurophysiological and neuroimaging data in predicting arm recovery. The fifth section considers whether neurophysiological and neuroimaging data provide additional predictive value over clinical data alone in predicting arm recovery. We conclude with a summary and recommendations for future prediction modelling studies.

Continue —> Brain Sciences | Free Full-Text | Prediction of Walking and Arm Recovery after Stroke: A Critical Review | HTML

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