{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T02:17:56Z","timestamp":1793067476284,"version":"4.5.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T00:00:00Z","timestamp":1724112000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Neurorehabil Neural Repair"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early prediction of poststroke motor recovery is challenging in clinical settings. The Prediction recovery potential (PREP2) algorithm is the most accurate approach for prediction of Upper Limb function available to date but lacks external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>(i) To externally validate the PREP2 algorithm in a prospective cohort, (ii) to study the characteristics of patients misclassified by the algorithm, and (iii) to compare the performance according to the presence of cognitive syndromes (aphasia, neglect, cognitive disorders).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We enrolled 143 patients with stroke and upper extremity weakness persistent at Day 3. Evaluation to predict the recovery status according to the PREP2 algorithm included age, SAFE and NIHSS scores at Day 3 and transcranial magnetic stimulation to determine the presence of the motor-evoked potential before day seven. Actual recovery (excellent, good, limited, or poor) was defined based on the Action Research Arm test score at 3\u2009months. Accuracy was computed by comparing the predictions of the PREP2 and the actual category of the patient. Additionally, to investigate misclassifications and the impact of cognitive syndromes, we recorded SAFE and NIHSS scores at Day 7, the Montreal Cognitive Assessment (MoCA) score, the presence of aphasia and neglect and Magnetic Resonance Imaging was used to evaluate the corticospinal tract lesion load.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The PREP2 algorithm showed a very good predictive value with 78% accuracy [95% CI: 71.2%-86.1%], especially for the extreme categories of recovery (EXCELLENT 87.5% [95% CI: 78.9%-96.2%] and POOR 94.9% [95% CI: 87.9%-100%]), and only 46.5% [95% CI: 19.05%-73.25%] for the GOOD category and even worse than chance for the LIMITED category 0%. Pessimistic predictions (false-negative cases) had a drastic improvement in the SAFE score acutely compared to that of well-predicted patients with unfavorable recovery (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u2009001). The predictive value of PREP2 decreased significantly when patients had cognitive disorders (MoCA score &lt;24) versus not (69.4% [95% CI: 52.8%-86.1%] vs 93.1% [95% CI: 83.9%-100%],\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009=\u2009.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study provides an external validation of the PREP2 algorithm in a prospective population and underlines the importance of taking into account cognitive syndromes in motor recovery prediction.<\/jats:p>\n                  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