Understanding How Structure of Acute Stroke Rehabilitation Delivery Relates to Mobility Outcomes
Grace Bellinger, postdoctoral fellow in physical medicine and rehabilitation at Johns Hopkins
Grace Bellinger, a postdoctoral fellow in physical medicine and rehabilitation at Johns Hopkins, was awarded a two-year postdoctoral fellowship from the American Heart Association. The project aims to understand how the structure of physical therapy delivery relates to mobility outcomes during the first three months after stroke by using electronic medical record data for almost 4,000 patients admitted to Johns Hopkins for acute stroke. Structure of physical therapy delivery will be described by the number of sessions, cumulative minutes of physical therapy, average session length, time from admission to first session and frequency of sessions. With change in Activity Measure for Post-Acute Care (AM-PAC) mobility during the acute care stay, clinical characteristics will explain most of the variance in discharge destination. For longer-term outcomes, it is hypothesized that frequency of sessions and average session length will be most strongly associated with AM-PAC mobility improvement from admission to 90 days. This work will use big data to improve understanding of how the structure of physical therapy delivery may affect mobility outcomes in a large, diverse population of persons with stroke. The results will advance the long-term goal of promoting more efficient health care systems that optimize recovery equitably for everyone with stroke.
Prognostic Indices for Hospitalized Older Adults with and Without Alzheimer’s Disease and Related Dementias
Erik Hoyer, vice chair for quality, safety and satisfaction for the Department of Physical Medicine and Rehabilitation at Johns Hopkins, is the site principal investigator overseeing a multisite study, funded by the National Institutes of Health, that aims to develop electronic health record (EHR)-based prognostic indices for hospitalized older adults. These indices will predict six-month and two-year mortality rates to aid in hospice and palliative care referrals and medication deprescribing for patients with and without Alzheimer’s disease and related dementias (ADRD). The project, ending in September 2029, will create models for both EHR integration and ePrognosis, a web-based compendium This work addresses the gap in current prognostic tools for hospitalized older adults, particularly those with ADRD, by incorporating factors such as social determinants of health and functional status data.
Erik Hoyer, M.D., Vice Chair for Quality, Safety and Satisfaction for the Department of Physical Medicine and Rehabilitation
Re-inventing Fall Risk Assessment Tools and Clinical Decision Making Through Data
Erik Hoyer is also the principal investigator of a project to develop a modern, data-driven system to assess fall risks among older hospitalized patients. The project, enabled by a grant from the Doctors Company Foundation, focuses on identifying novel fall risk factors and dynamic clinical influences on fall risk, such as mobility changes. The goal is to create an automated fall risk assessment tool integrated with electronic medical records to enhance clinical decision-making and reduce hospital falls. The project will end in late fall 2025.
Fall risk and mobility goal alerts are used across Johns Hopkins Medicine.
Effect of Physical Therapy on Acute Hospital Inpatients’ Discharge Disposition and Care Cost
Johns Hopkins physiatrist Erik Hoyer is a co-investigator for research aimed at determining the impact of physical therapy on discharge disposition and care costs for acute hospital inpatients. From January 2023 to July 2024, researchers are investigating how therapy treatment timing and frequency influence functional change and discharge outcomes. Using target trial emulation, the study seeks to provide insights into the optimal allocation of physical therapy resources in hospitals, informing value-based care decisions and enhancing patient care efficiency. This project is funded by the Magistro Family Foundation Research Grant.
A patient at The Johns Hopkins Hospital moves to his highest level of mobility following the JH-AMP program
Kevin McLaughlin and Richard Skolasky Jr. recently received funding from the National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases, as part of the Helping to End Addiction Long-term (HEAL) Initiative, to conduct a study examining the effectiveness of telerehabilitation for patients in rural Maryland who have chronic low back pain. The Advancing Rural Back Pain Outcomes Through Rehabilitation Telehealth (ARBOR-Telehealth) study will be conducted in partnership with TidalHealth, a member of the Johns Hopkins Clinical Research Network that serves patients on Maryland’s Eastern Shore. During a four-year clinical trial, the study team will randomize approximately 400 patients to receive telerehabilitation with a physical therapist or an educational website providing best-practice advice.
The researchers will compare the effectiveness of these two approaches based on patient-reported disability and opioid use. If the study shows that telerehabilitation is clinically effective, they will also examine implementation of this intervention at TidalHealth in order to inform telerehabilitation implementation in other rural health care systems. The study team, currently in the planning phase of its funding mechanism (UG3), anticipates transitioning to the clinical trial phase of the work in September of this year.
Propensity of Development of Severe Disease and Post-Acute SARS-CoV-2 in Patients with Connective Tissue Disease
Connective tissue diseases (CTDs) include a broad grouping of heritable disorders that can impact collagen function, as well as autoimmune conditions. In a study, Daniel Sova at Johns Hopkins focuses on collagen disorders, such as Ehlers-Danlos syndrome (EDS; types 3 and 5 collagen), Stickler syndrome (STL; types 2, 9 and 11 collagen) and Osteogenic imperfecta (OI; type 1 collagen). Approximately 0.3% of people have a CTD, but this figure is estimated to be low due to lack of diagnostic criteria and awareness outside of specialized clinics. The conditions involve multiple body systems, and common clinical features include joint hypermobility, vascular abnormalities, risk for injuries/fracture, and risk for chronic pain and fatigue. This study, funded by the Ehlers-Danlos Syndrome Research Foundation, investigates the propensity of development of severe disease and post-acute SARS-CoV-2 in the connective tissue disease population.
Daniel Sova, M.D.
Improving Quantitative Assessments of Upper Extremity Motor Function After Stroke Through Video-Based Technology
Stroke often impairs many aspects of movement, from fine motor control of the fingers to complex whole-body activities such as walking. A major hurdle in understanding and treating motor impairments after stroke has been the inability to collect objective, quantitative movement data in the home or clinic. Clinicians and researchers rely on subjective clinical scales, expensive technology with limited accessibility (e.g., motion capture systems) and other technologies with limited capability (e.g., wearables, instrumented gait mats) for motor assessment. These approaches impose significant limitations on the frequency and granularity of the data that can be collected, precluding a comprehensive understanding of patient-specific motor deficits and deeper insights into timelines of post-stroke recovery during rehabilitation.
Recent developments in artificial intelligence and machine learning (i.e., the advent of “pose estimation” technologies) have made it possible to track human movement with minimal financial cost, time investment and technological requirements. Pose estimation algorithms can rapidly track movement of key features of the body (e.g., shoulders, elbows, fingers) from simple digital videos that are easily recorded using a smartphone or tablet. These approaches have the potential to greatly expand the ability to track and measure motor impairments after stroke in any setting (including the home or clinic) with very little costs of time, money or effort.
The goal of this proposal, led by Ryan Roemmich at Johns Hopkins, is to enable a novel approach for quantitative, video-based movement analysis of the upper extremity after stroke. A state of the art, open-source pose estimation algorithm will be used to perform video-based assessments of movement post-stroke. Two significant advances are anticipated: 1) development and validation of new video-based assessments of upper extremity and fine motor control in people post-stroke against both ground-truth measurements and clinical standards, and 2) new, freely available workflows that make it possible to implement this approach using only simple videos recorded using common household devices. The ultimate goal of this proposal is to make it faster and easier to obtain accurate, quantitative assessments of upper extremity motor function after stroke.
Ryan Roemmich, Ph.D.