JNNP: A computerized predictive model for the restoration of upper limb function in stroke patients

Predicting post-stroke exercise recovery is important for setting viable treatment goals, choosing effective treatments, and managing long-term.
addition, predictive results can help clinical trials design to select the patients who are most beneficial to treatment.
a large number of predictive models for the recovery of upper limb movement after stroke have been developed.
models are mostly based on clinical markers such as controlled shoulder outreach and finger stretching.
other models include neurophysiological markers, such as the integrity of cortical spinal pathps.
factors assessed 48 hours after stroke are used to predict recovery six months after stroke.
, the accuracy of the prediction depends on the ability to assess the patient at a predetermined point in time after stroke.
In the current study, it was used to assess the recovery of upper limb capacity in the first six months after a stroke, which is measured by the ARAT score to complete lateral tasks such as picking up a cup, moving objects, and manipulating small objects.
, this dynamic model was studied to evaluate patients' time-dependent clinical improvements.
After comparing several model alternatives, the accuracy of the prediction was cross-verified and an online patient-specific model was developed, where the prediction results could be used for stroke units and the entire care session to predict the recovery of upper limb capacity.
combined data from the first ischemic precipitated stroke patient upper limb function recovery in four post-stroke early-stage prospective cohort studies, all data sets included repeated measurements of ART scores after stroke.
4D-EEG patients were recruited within 3 weeks of stroke.
4D-EEG study was measured in the first week after stroke and after 5, 12 and 26 weeks.
In the explicit study, the patients were measured 1, 2, 3, 5, 12 and 26 weeks after stroke, and in the EPOS study, the patients were measured on the first 3, 5 and 9 days, and after 6 months.
all studies included adult patients with single-sided or paraplegic symptoms within 72 hours of the onset of stroke, no history of disability, no serious impairments in communication, memory or understanding, and no obstruction of normal measurement.
the 14 upper limb capacity measured by ARAT as the cause variable of the model.
ARAT has 19 tasks, divided into four sub-areas: grip, grip, pinch and rough movement.
ARAT scores range from 0 to a maximum of 57 points, ARAT has good tilter performance.
neurological defects were assessed using the National Institutes of Health Stroke Scale (NIHSS; range 0-42).
all patients into at least three repeated measurements and these patients into cross-validation to provide an estimate of the accuracy of the predictions.
select the optimal model from the description model by comparing the predictive accuracy of different models.
To enable clinicians and researchers to use predictive models for individual stroke patients, an online visualization system has been developed to predict in real time the recovery of upper limb development and the prediction intervals of 68% and 95%, indicating the uncertainty of the prediction.
data set included 450 first-time ischemic stroke patients, 52 per cent of whom were men, with an average age of 65 years at baseline examination and an average follow-up day of 166 days.
patients were evaluated continuously, with a median of 6 times (25% of the scale: 4 times; 75% of the scale: 8 times).
the average of early ARATs after stroke was 14 (SD 19), the distribution of ARAT in patients with an early ARAT score of 0 or 1 after a large number of strokes was biased, and the ARAT scores were unevenly distributed throughout the score range (Q1-0, median 1, Q3 was 29, and maximum score was 56).
cross-verify shoulder outreach and finger stretching (SAFE) models to predict the time-dependent accuracy of 6-month ARAT scores.
Accuracy is defined as the absolute difference between the ARAT score for the 6 months after a cross-validated stroke and the ARAT score measured simultaneously, and the accuracy shown as a function of continuous measurements (IQR:Q1=25% and Q3=75%) is shown as a function for predicting the number of consecutive measurements, the last of which is usually performed six months after a stroke, one three months after a stroke, and the other between 2 and 6 weeks after a stroke.
, baseline ARAT scores were lower, medium, and higher in patient subgroups of prediction errors.
patients with a higher initial ARAT score had relatively small prediction errors at each point in time for 6 months, while patients with a lower initial ARAT score had a larger margin of error at baseline, but significantly less after stroke.
, the model can predict the recovery of upper limb capacity in patients within 6 months of stroke.
the model has the flexibility to use all available continuous evaluation data to create predictions, independently or connected to an electronic health record system at any expected moment after a stroke.
Selles R, Andrinopoulou E, Nijland R, et al Computerised patient-specific prediction of the recovery profile of the upper limb capacity within stroke services: the next step Journal of Neurology, Neurosurgery and Psimology Published Online First: 21 January 2021. doi: 10.1136/jnnp-2020-324637MedSci Original Source: MedSci Original Copyright Notice: All notes on this website "Source: Met Medical" or "Source: MedSci Original" text, images and audio and video materials, Copyright is owned by Metz Medicine and may not be reproduced by any media, website or individual without authorization, with the following "Source: Metz Medicine" stated at the time of reprint.
all reprinted articles on this website are for the purpose of transmitting more information and clearly indicate the source and author, and media or individuals who do not wish to be reproduced may contact us and we will delete them immediately.
at the same time reproduced content does not represent the position of this site.
leave a message here