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Memory decline in young stroke survivors during a 9-year follow-up: A cohort study
ConclusionYoung stroke survivors might be at risk of memory decline over the decade following the stroke.
Source: Frontiers in Neurology - November 25, 2022 Category: Neurology Source Type: research

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
ConclusionA fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.
Source: Frontiers in Neurology - August 22, 2022 Category: Neurology Source Type: research

Clustering and prediction of long-term functional recovery patterns in first-time stroke patients
ConclusionsThe longitudinal, multi-dimensional, functional assessment data of first-time stroke patients were successfully clustered, and the prediction models showed relatively good accuracies. Early identification and prediction of long-term functional outcomes will help clinicians develop customized treatment strategies.
Source: Frontiers in Neurology - March 8, 2023 Category: Neurology Source Type: research

Expression of Cytokines and Chemokines as Predictors of Stroke Outcomes in Acute Ischemic Stroke
Conclusions: Machine learning algorithms can be employed to develop prognostic predictive biomarkers for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.
Source: Frontiers in Neurology - January 14, 2020 Category: Neurology Source Type: research

Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms
Conclusions: DAMS and R-DAMS, as prediction-driven decision support tools, were designed to aid clinical decision-making for mild stroke patients in emergency contexts. In addition, even within a narrow range of baseline scores, NIHSS on admission is the strongest feature that contributed to the prediction.
Source: Frontiers in Neurology - December 23, 2021 Category: Neurology Source Type: research

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Source: Frontiers in Neurology - September 27, 2021 Category: Neurology Source Type: research

Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke
ConclusionThis is the first study to address BP management in the acute phase of ischemic stroke using ML techniques. The results indicate that the treatment choice should be adjusted to different clinical and BP parameters, thus, providing a better decision-making approach.
Source: Frontiers in Neurology - February 14, 2022 Category: Neurology Source Type: research

Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol
Discussion: By identifying data-driven prognosis prediction models in stroke rehabilitation, this study might contribute to the development of patient-oriented therapy and to optimize rehabilitation outcomes.Clinical Trial Registration:ClinicalTrials.gov, NCT03968627. https://www.clinicaltrials.gov/ct2/show/NCT03968627?term=Cecchi&cond=Stroke&draw=2&rank=2.
Source: Frontiers in Neurology - April 8, 2021 Category: Neurology Source Type: research

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Source: Frontiers in Neurology - March 31, 2021 Category: Neurology Source Type: research

Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion
Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.
Source: Frontiers in Neurology - December 2, 2021 Category: Neurology Source Type: research

Effects of Repetitive Peripheral Sensory Stimulation in the Subacute and Chronic Phases After Stroke: Study Protocol for a Pilot Randomized Trial
DiscussionThe results of this study are relevant to inform future clinical trials to tailor RPSS to patients more likely to benefit from this intervention.Trial RegistrationNCT03956407.
Source: Frontiers in Neurology - February 16, 2022 Category: Neurology Source Type: research

Bridging the Transient Intraluminal Stroke Preclinical Model to Clinical Practice: From Improved Surgical Procedures to a Workflow of Functional Tests
Acute ischemic stroke (AIS) remains a leading cause of mortality, despite significant advances in therapy (endovascular thrombectomy). Failure in developing novel effective therapies is associated with unsuccessful translation from preclinical studies to clinical practice, associated to inconsistent and highly variable infarct areas and lack of relevant post-stroke functional evaluation in preclinical research. To outreach these limitations, we optimized the intraluminal transient middle cerebral occlusion, a widely used mouse stroke model, in two key parameters, selection of appropriate occlusion filaments and time of occ...
Source: Frontiers in Neurology - March 11, 2022 Category: Neurology Source Type: research

Design and implementation of a Stroke Rehabilitation Registry for the systematic assessment of processes and outcomes and the development of data-driven prediction models: The STRATEGY study protocol
ConclusionsThis study will test the feasibility of a stroke rehabilitation registry in the Italian health context and provide a systematic assessment of processes and outcomes for quality assessment and benchmarking. By the development of data-driven prediction models in stroke rehabilitation, this study will pave the way for the development of decision support tools for patient-oriented therapy planning and rehabilitation outcomes maximization.Clinical tial registrationThe registration on ClinicalTrials.gov is ongoing and under review. The identification number will be provided when the review process will be completed.
Source: Frontiers in Neurology - October 10, 2022 Category: Neurology Source Type: research

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction
Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients.
Source: Frontiers in Neurology - June 18, 2021 Category: Neurology Source Type: research

Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning
Conclusions: Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the RFC model can improve the prediction of 6-month outcome in Chinese AIS patients.
Source: Frontiers in Neurology - November 19, 2020 Category: Neurology Source Type: research

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