Brain Stroke is a leading cause of acquired, permanent disability worldwide. Although the treatment of acute stroke has been improved considerably, most patients to date are left disabled with a considerable impact on functional independence and quality of life. As the absolute number of stroke survivors is likely to increase due to our ageing societies’ lifestyle changes, new strategies are needed to improve neuro-rehabilitation. The most critical driver of functional recovery post-stroke is a neural reorganisation. For developing novel, neurobiologically informed strategies to promote function healing, an improved understanding of the mechanisms enabling plasticity and recovery is mandatory. 

It is well-known that standard treatments available today are limited in their ability to help stroke patients attain full functional recovery. To address this, research into novel treatment strategies is ongoing. One such area of research is rehabilitative medicine, which aims to restore lost movements through a specific kind of exercise strategy. Vijay Bathina, an Indian Physiotherapist, developed 16 module program called Neuro circuit Program through which therapists can Map -Strategize -Predict Stroke Recovery. 

“Neuro Circuit Program enhances Functional recovery efficiency in stroke survivors through guiding algorithms which aid Therapists like Automation in the rehabilitation process.” 

Stroke recovery Pattern : 

The neurological recovery after stroke displays a nonlinear, logarithmic pattern ( Kwakkel et al., 2006; Langhorne et al., 2011) 

The more significant part of recovery is reported in the first three months following stroke (Wade et al., 1983). However, there is evidence that recovery is not limited to this period; upper extremity recovery has been reported many years after stroke (Carey et al., 1993; Yekutiel and Guttman, 1993). 

Improvement occurs through a complex combination of spontaneous and learning-dependent processes, including restitution, substitution, and compensation (Kwakkel et al., 2004; Langhorne et al., 2011). Until the third month after stroke onset, a variable spontaneous neurological recovery can be considered a confounder of rehabilitation intervention (Kwakkel et al., 2006). In the past, the observation of spontaneous recovery after stroke has misled some clinicians to believe that recovery of upper extremity function is intrinsic and therapists can do that little to influence it (Wade et al., 1983; Heller et al., 1987). 

Progresses in functional outcome appearing after three months seem primarily dependent on learning adaptation strategies (Kwakkel et al., 2004). Evidence suggests that neurological repair through brain reorganization supporting proper recovery or through compensation may also take place in 

the subacute and chronic phase after stroke (Krakauer, 2006). 

Predicting Recovery after Stroke : 

An accurate prognosis of recovery after stroke can help decide the type, duration, and specific rehabilitation goals for individual patients. Many factors impact stroke recovery, including the baseline (pre-stroke) cognitive and functional status of the patient in addition to the non-motor stroke deficits described earlier. 

Neuro Circuit Program works with Algorithms developed to predict motor function recovery after stroke. Predictor variables include age, sex, lesion site, initial motor impairment, motor-evoked potentials, and somatosensory-evoked potentials. Initial measures of upper extremity impairment and function were the most significant predictors of upper extremity recovery. Findings first assessments should be quick and simple, such as bedside tests of motor impairment, with progression to more complex tests. Later tests can include neurophysiological assessments and neuroimages of the motor system integrity. 

I applied a machine learning approach to develop predictive models of clinical outcomes at hospital discharge (using cross-validated Lasso regression). 

Predicting clinical outcomes at admission has been shown to improve therapy efficiency, increase therapists’ confidence and help to prepare for a probable discharge timeline. 

However, the type of rehabilitation program or engagement of the patient could also affect the discharge outcomes. 

In the future, I will work towards objective measures of the rehabilitation program and even measures of patient attitude or engagement during the rehabilitation process to further refine the model predictions and improve generalization to alternative rehabilitation programs. 

Wearable sensors are an emerging technology that can allow precise, fine-scale measurement of biomechanical and physiological markers during rehabilitation. Such technologies may improve the prediction of clinical outcomes by capturing an objective, high-resolution data signatures of post-stroke impairment and informing efficient, patient-specific rehabilitation strategies. 

About Me: Vijay Bathina 

MPT, Fellow in Neurorehabilitation, Certification in Pain & Palliative care, Certification in Manual Therapy, Certification in 

Noninvasive coma stimulation, Certification in COPD rehabilitation

 Current Positions:

Director – Rehabilitation services, Ucchvas Transitional Care Centre, Hyderabad 

Associate Professor – KNR Health University, Telangana. Clinical Mentor/Partner: Startoon labs PVT LTD, Hyderabad Chief of Innovation: Neuro Circuit Labs, India/USA 

Awards :

 ● Sanchethi National Award-Best Therapy Professional 

-2020

 ● BestPaperPresentation-Dubai-2016 

Contact : +91 9848857464 

drvijaybathina@gmail.com

LinkedIn profile : http://linkedin.com/in/vijay-bathina-a2105abbhttps://www.ucchvas.com/caregivers/

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