Ignite Creation Date:
2024-10-26 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06532994
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-07-15
Brief Title:
Predictive Algorithms for Critical Rehabilitation Outcomes
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Development and Validation of a Prediction Algorithms to Estimate the Clinical Effect of Early Rehabilitation on ICU Survivors Received Mechanical Ventilation in the ICU
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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
Detailed Description:
An increasing amount of evidence from evidence-based medicine suggests that early rehabilitation intervention including early active and passive exercises position management pulmonary rehabilitation etc for mechanical ventilation patients is safe and feasible and can promote certain degree of functional recovery and reduce the length of stay in the intensive care unit ICU However the differences in consciousness state muscle strength respiratory function and activity of daily living ADL among patients who are discharged from the ICU after condition stabilization are very large even some patients did not obtain obvious benefits Therefore how to identify which patients may have better benefit from early rehabilitation intervention is a key issue that needs to be focused on in ICU
This study used Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis TRIPOD as the guideline Survivors undergoing mechanical ventilation in the ICU were recruited as the participants whether patients gained progress in ADL function at different time points after receiving early rehabilitation intervention in the ICU was used as the outcome which is a time-to-event indicator Demographic data clinical diagnostic data and disease intervention data of the subjects were collected as alternative predictors Variable transformation and variable screening were used to find predictors that could predict the outcome The process of constructing clinical predictive models is completed by fitting models through regression equations and machine learning algorithms internal validation external validation and clinical value assessment The model with the best prediction efficiency is selected based on the differentiation and calibration of different models after validation This model will be presented with a nomogram or a web app The application of this clinical predictive model will identify whether and when this patient can received better recovery on ADL after receiving early rehabilitation intervention so as to further optimize the timing of early intervention in rehabilitation and improve his survival quality
This study used Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis TRIPOD as the guideline Survivors undergoing mechanical ventilation in the ICU were recruited as the participants whether patients gained progress in ADL function at different time points after receiving early rehabilitation intervention in the ICU was used as the outcome which is a time-to-event indicator Demographic data clinical diagnostic data and disease intervention data of the subjects were collected as alternative predictors Variable transformation and variable screening were used to find predictors that could predict the outcome The process of constructing clinical predictive models is completed by fitting models through regression equations and machine learning algorithms internal validation external validation and clinical value assessment The model with the best prediction efficiency is selected based on the differentiation and calibration of different models after validation This model will be presented with a nomogram or a web app The application of this clinical predictive model will identify whether and when this patient can received better recovery on ADL after receiving early rehabilitation intervention so as to further optimize the timing of early intervention in rehabilitation and improve his survival quality
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None