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In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Applied Physiology
In the
School of Biological Sciences
MARY ALICE SALTÃO DA SILVA
Will defend her dissertation
EARLY PREDICTORS OF POST-STROKE MOTOR RECOVERY
NOVEMBER 8, 2021
2:30 PM
Thesis Advisors:
LEWIS WHEATON, Ph.D.
School of Biological Sciences
Georgia Institute of Technology
MICHAEL BORICH, Ph.D.
School of Medicine
Emory University
School of Biological Sciences
Georgia Institute of Technology
Committee Members:
RICHARD NICHOLS, Ph.D.
School of Biological Sciences
Georgia Institute of Technology
SAMIR BELAGAJE, M.D.
School of Medicine
Emory University
CATHY STINEAR, Ph.D.
School of Medicine
University of Auckland
ABSTRACT:
Stroke is a leading cause of long-term disability worldwide. Despite robust spontaneous biological recovery mechanisms and provision of intensive rehabilitation therapies, most stroke survivors experience persistent loss of upper extremity function which is directly related to reduced independence in activities of daily living and diminished quality of life. Identification of clinical, anatomical, or neurophysiologic indices that accurately predict the capacity for recovery post-stroke is crucial to facilitate precision-based medicine approaches for clinical management, including targeted therapeutic interventions. The Predict Recovery Potential (PREP2) prediction tool uses a combination of clinical measurements and neurological biomarkers to predict paretic upper extremity (PUE) motor outcomes but has yet to be externally validated in the US healthcare system. The primary study objectives were to: 1) evaluate external validation feasibility of PREP2 in the US; 2) retrospectively assess current care practices to determine the routinely collected measures that are most predictive of PUE functional outcome post-stroke; 3) evaluate the prognostic merit of biomarkers isolated from clinical neuroimaging.
The studies were conducted via retrospective chart review for two cohorts of stroke patients over fiscal years 2016-2018. In Aim 1, I assessed prospective validation feasibility of the PREP2 prediction tool in acute care settings in the US using a cohort of all stroke admissions to Emory University Hospital and Grady Memorial Hospital. In Aims 2 and 3, I assessed the ability of currently collected clinical measures and neurologic biomarkers isolated from clinical imaging to predict PUE motor outcomes post-stroke using a cohort of patients with similar clinical management across the continuum of stroke rehabilitation and recovery. This cohort of patients remained within the Emory University Hospital system for acute hospitalization, inpatient rehabilitation, and outpatient care, allowing longitudinal assessment to track recovery and to estimate the level of PUE motor function return. Institutional electronic medical record systems were utilized to extract metrics including demographic data, stroke characteristics, longitudinal documentation of post-stroke motor function, and metrics of stroke care management along the post-stroke care continuum. Clinically diagnostic MRI was used to create lesion masks which were spatially normalized and processed to obtain corticospinal tract (CST) lesion overlap in both primary motor (M1) and non-M1 CST projections. Metric associations were investigated with correlation and cluster analyses, Kruskal-Wallis tests, classification and regression tree (CART) analyses.
In Aim 1, we found that current stroke management allows for shoulder abduction finger extension manual muscle tests (SAFE score) to be obtained at therapy evaluations and for the National Institutes of Stroke Scale score to be extracted from the patient chart. On average, patients appropriate for CST integrity assessment remain in the acute care hospital setting at a time when CST function should be evaluated for PREP2 validation. In Aims 2 and 3, estimations of PUE strength extracted from the patient chart (E-SAFE) and clinical MRI-derived CST lesion overlap were associated with PUE functional outcome. Cluster analysis produced three distinct outcome groups and aligned closely to previous outcome categories. Outcome groups significantly differed in E-SAFE scores and lesion overlap on cortical projections within the CST, in particular those emanating from non-M1 cortical areas. Exploratory predictive models using clinical MRI metrics, either alone or in combination with clinical measures, were able to accurately identify recovery outcome category for patients using assessments made during both the acute and early subacute phases of post-stroke recovery.
Results suggest that (1) prospective PREP2 validation studies are feasible in a US healthcare setting, (2) SAFE is an easy-to-acquire, readily implementable screening metric with high clinical utility for patients who undergo AR post-stroke, and (3) clinical MRI-derived biomarkers of both M1 and non-M1 contributions to CST integrity may offer unique insight into PUE motor outcome potential.
