Predictive Algorithms for Critical Rehabilitation Outcomes

An increasing amount of evidence from evidence-based medicine indicates that early rehabilitation intervention for patients receiving mechanical ventilation is safe and feasible, and can promote functional recovery and reduce hospital stay. However, the conscious state, respiratory function, and daily living activities of these patients after being discharged from the ICU vary greatly, and some patients do not show obvious benefits. How to identify which patients may have benefit from early rehabilitation is a key issue that needs to be addressed in critical care rehabilitation. This study aims to investigate the clinical data related to the disease of the ICU survivors who received mechanical ventilation as the research object, by collecting their clinical data when receiving early rehabilitation intervention, and constructing a clinical prediction model for the efficacy of early rehabilitation intervention in the ICU through the selection of optimal regression equation or machine learning algorithm. The application of this model can effectively determine whether ICU inpatients need early rehabilitation intervention, thereby reducing complication rates and improving their quality of life.

NCT Number	NCT06532994
Lead Sponsor	Wuhan University Collaborators: General Hospital of the Yangtze River Shipping/Wuhan Brain Hospital, Wuhan Wuchang Hospital
Conditions	Intensive Care, Mechanical Ventilation, Rehabilitation, Algorithms
Enrollment	250 participants
Start Date	2024-08-01
Primary Completion	2026-10-30 (estimated)
Study Completion	2026-12-30 (estimated)
Updated on ClinicalTrials.gov	2026-04-21