← Eichor
Study identifier: NCT06532994 Synced from ClinicalTrials.gov · July 29, 2026
● Recruiting

Predictive Algorithms for Critical Rehabilitation Outcomes

Condition: Intensive Care · Mechanical Ventilation · Rehabilitation  ·  Sponsor: Wuhan University

PhaseN/A
Planned participants250
Who can joinAll sexes, 18 Years to 90 Years
Healthy volunteersNo

About this study

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.

This description comes directly from the study's public registry record.

Talk to the study team

Qing' Shu, Ph.D  ·  +86 13971081682  ·  shuqingj@whu.edu.cn

Always discuss trial participation with your own doctor first.

Locations (1)

Zhongnan hospital of Wuhan UniversityWuhan, Hubei, ChinaRecruiting

Follow this study

Get one email when the public record changes — results posted, or the study's status changes. Nothing else, ever.

We email about this public record only. Unsubscribe anytime with one click. Never medical advice.

Is this your study? This page was generated automatically from the public registry record. Sponsors can claim it — free — to add branding and verified contact routing. Claim this page →

This page is independently generated by Eichor from the public ClinicalTrials.gov record and re-synced daily. It is not the sponsor's official website unless claimed. Nothing here is medical advice; eligibility is always determined by the study team — talk to your own doctor first.

Source record: clinicaltrials.gov/study/NCT06532994