Validation of a Prognostic Model for Discharge Functional Independence Measure in Stroke Patients Considering the Impact of Aging

Prognostic Prediction for Stroke Patients Using Machine Learning - Prognostic Prediction for Stroke Patients Using Machine Learning

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000056696

Enrollment

300

Registered

2025-01-13

Start date

2022-03-07

Completion date

Unknown

Last updated

2026-09-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ischemic Stroke and Hemorrhagic Stroke

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients diagnosed with ischemic or hemorrhagic stroke who were admitted to a convalescent rehabilitation ward for 30 days or more between January 2018 and December 2020.

Exclusion criteria

Exclusion criteria: Those who experienced recurrence or worsening of their condition during hospitalization Those who suffered a stroke (cerebral infarction or cerebral hemorrhage) while being hospitalized in another hospital Those with a history of neurological diseases other than stroke Those with a history of mental illness or developmental disorders Those who developed epilepsy or hydrocephalus at the time of stroke onset Those who experienced a stroke due to Moyamoya disease Those who required restrictions on rehabilitation in the convalescent rehabilitation ward Those with a history of blindness Those who died during hospitalization Those with missing data

Design outcomes

Primary

Measure	Time frame
Prediction Accuracy of Functional Independence Measure (FIM) at Discharge	—

Countries

Japan

Contacts

Public ContactRyo Matsuda

Hokkaido University of Science Department of Physical Therapy Faculty of Health Sciences

matsuda-r@hus.ac.jp+81-11-688-7197

Outcome results