PREP2: A biomarker-based algorithm for predicting upper limb function after stroke

Cathy M Stinear, Winston D Byblow, Suzanne J Ackerley, Marie-Claire Smith, Victor M Borges, P Alan Barber, Cathy M Stinear, Winston D Byblow, Suzanne J Ackerley, Marie-Claire Smith, Victor M Borges, P Alan Barber

Abstract

Objective: Recovery of motor function is important for regaining independence after stroke, but difficult to predict for individual patients. Our aim was to develop an efficient, accurate, and accessible algorithm for use in clinical settings. Clinical, neurophysiological, and neuroimaging biomarkers of corticospinal integrity obtained within days of stroke were combined to predict likely upper limb motor outcomes 3 months after stroke.

Methods: Data from 207 patients recruited within 3 days of stroke [103 females (50%), median age 72 (range 18-98) years] were included in a Classification and Regression Tree analysis to predict upper limb function 3 months poststroke.

Results: The analysis produced an algorithm that sequentially combined a measure of upper limb impairment; age; the presence or absence of upper limb motor evoked potentials elicited with transcranial magnetic stimulation; and stroke lesion load obtained from MRI or stroke severity assessed with the NIHSS score. The algorithm makes correct predictions for 75% of patients. A key biomarker obtained with transcranial magnetic stimulation is required for one third of patients. This biomarker combined with NIHSS score can be used in place of more costly magnetic resonance imaging, with no loss of prediction accuracy.

Interpretation: The new algorithm is more accurate, efficient, and accessible than its predecessors, which may support its use in clinical practice. While further work is needed to potentially incorporate sensory and cognitive factors, the algorithm can be used within days of stroke to provide accurate predictions of upper limb functional outcomes at 3 months after stroke. www.presto.auckland.ac.nz.

Figures

Figure 1
Figure 1
CART analysis for patients with a SAFE score ≥ 5 within 72 h poststroke. All of these patients are MEP+.
Figure 2
Figure 2
CART analyses of patients with a SAFE score < 5 at 72 h poststroke. (A) Both TMS and MRI biomarkers available. The analysis selects sensorimotor tract (SMT) lesion load to differentiate between MEP− patients who will have a Limited versus Poor upper limb outcome. (B) TMS but no MRI biomarkers available. The analysis selects NIHSS score to differentiate between MEP− patients who will have a Limited versus Poor upper limb outcome.
Figure 3
Figure 3
The PREP2 algorithm predicts upper limb functional outcome at 3 months poststroke. The four possible upper limb outcomes are color‐coded. The colored dots depict the proportion of patients expected to achieve each color‐coded outcome, depending on their pathway through the algorithm, based on the results of the CART analysis. Patients who achieve a SAFE score of five or more within 72 h of stroke symptom onset, and are less than 80 years old, are most likely to have an Excellent upper limb outcome. Patients who achieve a SAFE score of five or more within 72 h of stroke symptom onset and are 80 years old or more, are most likely to have an Excellent upper limb outcome provided their SAFE score is at least 8; otherwise they are likely to have a Good upper limb outcome. Patients whose SAFE score is less than 5 at 72 h after stroke symptom onset need TMS to determine MEP status in the paretic upper limb, a key biomarker of corticospinal tract integrity. If a MEP can be elicited (MEP+) approximately 5 days poststroke then the patient is likely to have at least a Good upper limb outcome. If a MEP cannot be elicited, the NIHSS score obtained 3 days poststroke can be used to predict either a Limited outcome if the score is less than 7, or a Poor outcome if the score is 7 or more.

References

    1. Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol 2009;8:741–754.
    1. Veerbeek JM, Kwakkel G, van Wegen EE, et al. Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke 2011;42:1482–1488.
    1. Coupar F, Pollock A, Rowe P, et al. Predictors of upper limb recovery after stroke: a systematic review and meta‐analysis. Clin Rehabil 2012;26:291–313.
    1. Nijland RH, van Wegen EE, Harmeling‐van der Wel BC, et al. Accuracy of physical therapists’ early predictions of upper‐limb function in hospital stroke units: the EPOS study. Phys Ther 2013;93:460–469.
    1. Stinear CM, Byblow WD, Ackerley SJ, et al. Predicting recovery potential for individual stroke patients increases rehabilitation efficiency. Stroke 2017;48:1011–1019.
    1. Kim B, Winstein C. Can neurological biomarkers of brain impairment be used to predict poststroke motor recovery? A systematic review Neurorehabil Neural Repair 2016;31:3–24.
    1. Burke E, Cramer SC. Biomarkers and predictors of restorative therapy effects after stroke. Curr Neurol Neurosci Rep 2013;13:329.
    1. Byblow WD, Stinear CM, Barber PA, et al. Proportional recovery after stroke depends on corticomotor integrity. Ann Neurol 2015;78:848–859.
    1. Bembenek JP, Kurczych K, Karli Nski M, et al. The prognostic value of motor‐evoked potentials in motor recovery and functional outcome after stroke ‐ a systematic review of the literature. Funct Neurol 2012;27:79–84.
    1. Puig J, Blasco G, Schlaug G, et al. Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke. Neuroradiology 2017;59:343–351.
    1. Feng W, Wang J, Chhatbar PY, et al. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Ann Neurol 2015;78:860–870.
    1. Buch ER, Rizk S, Nicolo P, et al. Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging. Neurology 2016;86:1924–1925.
    1. Puig J, Blasco G, Daunis IEJ, et al. Decreased corticospinal tract fractional anisotropy predicts long‐term motor outcome after stroke. Stroke 2013;44:2016–2018.
    1. Puig J, Pedraza S, Blasco G, et al. Acute damage to the posterior limb of the internal capsule on diffusion tensor tractography as an early imaging predictor of motor outcome after stroke. AJNR Am J Neuroradiol 2011;32:857–863.
    1. Stinear CM, Barber PA, Petoe M, et al. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain 2012;135(Pt 8):2527–2535.
    1. Riley JD, Le V, Der‐Yeghiaian L, et al. Anatomy of stroke injury predicts gains from therapy. Stroke 2011;42:421–426.
    1. Petoe MA, Byblow WD, de Vries EJ, et al. A template‐based procedure for determining white matter integrity in the internal capsule early after stroke. Neuroimage Clin 2014;4:695–700.
    1. Behrens TE, Johansen‐Berg H, Woolrich MW, et al. Non‐invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci 2003;6:750–757.
    1. Burke E, Dodakian L, See J, et al. A multimodal approach to understanding motor impairment and disability after stroke. J Neurol 2014;261:1178–1186.
    1. Nakayama H, Jorgensen HS, Raaschou HO, et al. The influence of age on stroke outcome. The Copenhagen Stroke Study. Stroke 1994;25:808–813.
    1. Chang MC, Do KH, Chun MH. Prediction of lower limb motor outcomes based on transcranial magnetic stimulation findings in patients with an infarct of the anterior cerebral artery. Somatosens Mot Res 2015;32:249–253.
    1. Pizzi A, Carrai R, Falsini C, et al. Prognostic value of motor evoked potentials in motor function recovery of upper limb after stroke. J Rehabil Med 2009;41:654–660.
    1. Nijland RH, van Wegen EE, Harmeling‐van der Wel BC, et al. Presence of finger extension and shoulder abduction within 72 hours after stroke predicts functional recovery: early prediction of functional outcome after stroke: the EPOS cohort study. Stroke 2010;41:745–750.
    1. Persson HC, Alt Murphy M, Danielsson A, et al. A cohort study investigating a simple, early assessment to predict upper extremity function after stroke ‐ a part of the SALGOT study. BMC Neurol 2015;15:92.
    1. Lindenberg R, Renga V, Zhu LL, et al. Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke. Neurology 2010;74:280–287.
    1. Takenobu Y, Hayashi T, Moriwaki H, et al. Motor recovery and microstructural change in rubro‐spinal tract in subcortical stroke. Neuroimage Clin 2014;4:201–208.
    1. Phan TG, van der Voort S, Chen J, et al. Impact of corticofugal fibre involvement in subcortical stroke. BMJ Open 2013;3:e003318.
    1. Archer DB, Vaillancourt DE, Coombes SA. A template and probabilistic atlas of the human sensorimotor tracts using diffusion MRI. Cereb Cortex 2017;. .
    1. Park CH, Kou N, Ward NS. The contribution of lesion location to upper limb deficit after stroke. J Neurol Neurosurg Psychiatry 2016;87:1283–1286.
    1. Koch P, Schulz R, Hummel FC. Structural connectivity analyses in motor recovery research after stroke. Ann Clin Transl Neurol 2016;3:233–244.
    1. Granziera C, Daducci A, Meskaldji DE, et al. A new early and automated MRI‐based predictor of motor improvement after stroke. Neurology 2012;79:39–46.
    1. Grefkes C, Ward NS. Cortical reorganization after stroke: how much and how functional? Neuroscientist 2014;20:56–70.
    1. Stewart JC, Dewanjee P, Tran G, et al. Role of corpus callosum integrity in arm function differs based on motor severity after stroke. Neuroimage Clin 2017;14:641–647.
    1. Meyer S, De Bruyn N, Lafosse C, et al. Somatosensory impairments in the upper limb poststroke: distribution and association with motor function and visuospatial neglect. Neurorehabil Neural Repair 2016;30:731–742.
    1. Meyer S, Karttunen AH, Thijs V, et al. How do somatosensory deficits in the arm and hand relate to upper limb impairment, activity, and participation problems after stroke? A systematic review Phys Ther 2014;94:1220–1231.

Source: PubMed

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