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120 active trials for Stroke Acute

RGS@Home: Personalized 24/7 Home Care Post-stroke

Stroke represents one of the main causes of adult disability and will be one of the main contributors to the burden of disease in 2030. However, the investigator's healthcare systems do not have enough resources to cover the current demand let alone its future increase. There is a need to deploy new approaches that advance the current rehabilitation methods and enhance their efficiency. One of the latest approaches used for the rehabilitation of a wide range of deficits of the nervous system is based on virtual reality (VR) applications, which combine training scenarios with dedicated interface devices. Market drivers exist for new ICT based treatment solutions. IBEC/ Eodyne Systems has developed and commercialised the Rehabilitation Gaming System (RGS), a science-based ICT solution for neurorehabilitation combining brain theory, AI, cloud computing and virtual reality and targeting motor and cognitive recovery after stroke. RGS provides a continuum of evaluations and therapeutic solutions that accompany the patient from the clinic to the therapy centre. RGS has been clinically validated showing its superiority over other products while reducing cost also through its use of standard off-the-shelf hardware and a Software as a Service model (SaaS). Commercial evaluations have shown that RGS acts as a workforce multiplier while delivering a high quality of care at clinical centres (RGS@Clinic). However, in order to achieve significant benefits in the patients' QoL, it is essential that RGS becomes an at home solution providing 24/7 monitoring and care. For this reason, this project aims at investigating the RGS acceptability and adoption model. The findings derived from this study will contribute to establish a novel and superior neurorehabilitation paradigm that can accelerate the recovery of hemiparetic stroke patients. Besides the clinical impact, such achievement could have relevant socioeconomic impact.

Start: November 2020
MRI Repository for Acute Stroke Clinical and Research Applications

Stroke is one of the leading causes of death and long-term disability. A major unresolved problem in MRI-based stroke assessment is to relate image features to brain function in a way that can properly guide stratification for treatment and rehabilitation. This requires extracting meaningful and reproducible models of brain function from stroke images, a daunting task severely hindered by the great variability of lesion frequency and pattern. Large datasets are imperative to uncover possible lesion-function relationships. In this project the investigators will create a large database of acute strokes MRIs. The investigators will retrospectively archive an estimated 3,000 MRIs of patients with acute stroke, acquired at the Johns Hopkins Hospital, 2009-2019. This dataset will include 1.5 and 3 Tesla scans, diverse protocols and sequences (e.g., diffusion and perfusion weighted images (DWI/b0, PWI), T1, T2, FLAIR, susceptibility weighted images), with typical clinical low voxel resolution (4-7 mm3). Lesions will be initially delineated on DWI/b0, the most informative MRI sequence for acute stroke. After anonymization and defacing, two trained evaluators will perform the manual lesion segmentation. Two expert neuroradiologists will create consensual structured radiological reports with information about stroke type and location according to different criteria (e.g., 34 brain structures and 11 vascular territories). The investigators will also archive structured information from discharge (demographics, laboratory, and neurological evaluation of patients, including NIH stroke scale and modified Rankin scale, mRS), as well as the 90-days follow-up mRS.

Start: August 2021
Establishment and Validation of a Predictive Model for Hemorrhage

Background: Patients with acute ischemic stroke (AIS) are at risk of hemorrhagic transformation (HT) after intravenous thrombolysis. Although there is a risk assessment model for hemorrhagic transformation after thrombolysis, there is no evidence of clinical application in the population of Guangdong Province. . Purpose: To verify the clinical application effect of the existing risk assessment model for hemorrhage transformation after thrombolysis in the local population; to improve the existing prediction model and verify the predictive value of HT after intravenous thrombolysis. Methods: (1) Continuously collect AIS patients who received intravenous thrombolysis in our hospital from January 2014 to December 2020 to verify the clinical application effects of three existing models (HAT, SIT-sICH, THRIVE) on bleeding transformation. Collect baseline and bleeding transformation information within 7 days after thrombolysis, and use ROC curve, calibration curve, sensitivity and specificity to evaluate the prediction effect. A logistic regression model was used to construct an improved HT prediction model based on the AIC principle; (2) Continuous collection of AIS patients who received intravenous thrombolysis in two local hospitals from January 2021 to December 2022 for internal and external verification. Expected results: (1) Evaluate the clinical application value of the existing prediction model in local AIS patients with intravenous thrombolysis; (2) Develop a modified risk assessment model suitable for hemorrhage transformation after intravenous thrombolysis in AIS patients in Guangdong area, and evaluate the risk early Provide guarantee for clinical diagnosis and treatment.

Start: March 2021