Stroke, Upper Limb Motor Deficit
Conditions
Keywords
stroke, motor, prediction
Brief summary
The prediction of motor recovery in the acute phase of stroke is crucial for several clinical reasons: (i) informing the patient and his relatives, (ii) helping to identify the patient's endorsement (return home or rehabilitation) as well as the adaptation of the rehabilitation program to what can be expected from it. To date, an algorithm (decision tree) proposed by C. Stinear's team named PREP2 is the best predictive tool with 75% of patients well classified at 3 months. It predicts the functional recovery of the upper limb after stroke 3 months before the episode by categorizing recovery as excellent, good, limited as well as minor (poor). With two data (SAFE score, age) or three (SAFE score, Motor evoked potential, NIHSS), the prediction is effective three times out of 4. In the study the team is proposing PREP-UCV, it would like to validate this algorithm as it is with patients in the active file who are victims of stroke. The expected accuracy is 75% or more. As a secondary objective, the team would like to confirm that it find the same algorithm starting from the initial data from PREP 2 (side of the stroke, type of stroke (ischemic and / or hemorrhagic), involvement of the corticospinal tract on MRI, sex at birth ) as well as two other factors which are also very important: cognitive status (dysexecutive / aphasia / neglect), as well as the neutrophils on lymphocytes ratio.
Detailed description
Retrospective cohort of stroke patients with a upper limb deficit.Clinical scores such as SAFE score, NIHSS; demographic data such as age and electrophysiological data (such as the absence/presence of Motor evoked potential) will determine the predictive functional outcome of the upper limb deficit according to the PREP2 algorithm. The accuracy of this prediction will be verified according to the actual state of the patient at 3-6 months. Second, another algorithm will be built taking in account cognitive deficits and biological data to determine if the accuracy is higher. All data will be acquired during the clinical routine work-up.
Interventions
OTHERClinical scores such as SAFE score, NIHSS; demographic data such as age and electrophysiological data (such as the absence/presence of Motor evoked potential)
Clinical scores such as SAFE score, NIHSS; demographic data such as age and electrophysiological data (such as the absence/presence of Motor evoked potential) will determine the predictive functional outcome of the upper limb deficit according to the PREP2 algorithm.
Sponsors
Assistance Publique - Hôpitaux de Paris
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* age ≥ 18 y-o, * admitted in the Pitié-Salpêtrière stroke unit, * stroke with a upper limb motor deficit, * agree to participate;
Exclusion criteria
* contra-indication to MRI or TMS, * patients under legal guardianship , * patients without healthcare insurance
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of classification with the PREP2 decision tree | 6 months | Proportion of patients well classified in their group of recovery |
Countries
France
Contacts
Primary ContactCharlotte ROSSO, MD
Outcome results
None listed