Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06627907
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-10-03
Brief Title:
Hemodynamic Response to the End-expiratory Occlusion Test to Titrate Fluid Challenge in Operating Room
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Hemodynamic Response to the End-expiratory Occlusion Test to Titrate Fluid Challenge in Operating Room
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-10
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:
FC_EEOT
Brief Summary:
Personalzing intraoperative anesthetic fluid management may help in preventing fluid accumulation and related complications
Fluids are gine as boluses in operating room the so-called FC The response to the FC is due to several physiological conditions related to the preload dependency ie the intrinsic ability of the heart of increasing the stroke volume - SV - in response to fluid administration
The minimal volume required to appropriately challenge the cardiovascular system is 4 mlkg of fluid but higher volumes up to 6 mlkg may be needed
Predicting the response to FC administration may be possible by applying a physiological test called functional hemodynamic test such as the end-expiratory occlusion test consisting in interrupping the mechanical ventilation and hence promoting venous return and consequente SV changes The percentage of SV increase associated to EEOT may predict fluid responsiveness to the FC patients responders will increase SV to a bigger extent as compared to non-responders
Fluids are gine as boluses in operating room the so-called FC The response to the FC is due to several physiological conditions related to the preload dependency ie the intrinsic ability of the heart of increasing the stroke volume - SV - in response to fluid administration
The minimal volume required to appropriately challenge the cardiovascular system is 4 mlkg of fluid but higher volumes up to 6 mlkg may be needed
Predicting the response to FC administration may be possible by applying a physiological test called functional hemodynamic test such as the end-expiratory occlusion test consisting in interrupping the mechanical ventilation and hence promoting venous return and consequente SV changes The percentage of SV increase associated to EEOT may predict fluid responsiveness to the FC patients responders will increase SV to a bigger extent as compared to non-responders
Detailed Description:
A two-step statistical approach will be used to define the best model to predict the fluid responsiveness
1 A univariable logistic regression model to test the association of the considered hemodynamic variables provided by the hemodynamic monitoring with the primary outcome fluid responsiveness at 10th minute Then a multivariable analysis incorporating the variable in univariable analysis with a p 02 after testing the colinearity and interactions Significance threshold for multivariable analysis will be set to 005
2 A Hosmer and Lemeshow test was calculated to evaluate goodness of fit for the logistic regression model the Informative criterion metrics such as Akaike Infromation Criterion AIC and the receiver operating characteristic ROC curve standard error SE analysis evaluated the performance of predictive items for FC response ieY dependent variable SVI increasedby 10 10 minutes Y10 after FC infusion The absence of a significant increase in the likelihood value afteromission of each of the remaining variables was checked
To define the best model to predict the amount of fluid in responder group a machine-learning approach will be considered where Y dependent variable total amount of crystalloids to obtained SVI 10 after FC infusion X matrix of parameters The final model decision will be made among following commonly used regression algorithms linear reagression Lasso Regression or Ridge Regression The model performance assessment will be made using metrics like Mean Squared Error MSE or R-squared K-fold cross-validation technique will be applied to get a more robust estimate of the models performance
The hemodynamic values of responders and non-responders at each step of the protocol are analyzed with a one -way analysis of variance for repeated measurements ANOVA and Geisser -Greenhouse G-G correction as ajustement for lack of sphericity if needed Post-hoc pairwise multiple comparisons analysis are performed using Tukeys test to control familywise error
To understand whether hemodynamic changes after EEOT could help in the prediction of minimal dose of FC the study will enroll 2-year evaluable patients and the final numer will be foreseen in about 300 with aroud 500 fluid challenge
The sample size will allow us to perform three step
4 an initial step of 50 patients to understand which variables of hemodynamic changes after EEOT will affect the minimal dose of FC In this initial part a variable will be considered interesting if the relative p value will be under 01 5 A second step including 200 patients data to create the model considering also variables interactions 6 A finel step of 50 patients to validate the model
1 A univariable logistic regression model to test the association of the considered hemodynamic variables provided by the hemodynamic monitoring with the primary outcome fluid responsiveness at 10th minute Then a multivariable analysis incorporating the variable in univariable analysis with a p 02 after testing the colinearity and interactions Significance threshold for multivariable analysis will be set to 005
2 A Hosmer and Lemeshow test was calculated to evaluate goodness of fit for the logistic regression model the Informative criterion metrics such as Akaike Infromation Criterion AIC and the receiver operating characteristic ROC curve standard error SE analysis evaluated the performance of predictive items for FC response ieY dependent variable SVI increasedby 10 10 minutes Y10 after FC infusion The absence of a significant increase in the likelihood value afteromission of each of the remaining variables was checked
To define the best model to predict the amount of fluid in responder group a machine-learning approach will be considered where Y dependent variable total amount of crystalloids to obtained SVI 10 after FC infusion X matrix of parameters The final model decision will be made among following commonly used regression algorithms linear reagression Lasso Regression or Ridge Regression The model performance assessment will be made using metrics like Mean Squared Error MSE or R-squared K-fold cross-validation technique will be applied to get a more robust estimate of the models performance
The hemodynamic values of responders and non-responders at each step of the protocol are analyzed with a one -way analysis of variance for repeated measurements ANOVA and Geisser -Greenhouse G-G correction as ajustement for lack of sphericity if needed Post-hoc pairwise multiple comparisons analysis are performed using Tukeys test to control familywise error
To understand whether hemodynamic changes after EEOT could help in the prediction of minimal dose of FC the study will enroll 2-year evaluable patients and the final numer will be foreseen in about 300 with aroud 500 fluid challenge
The sample size will allow us to perform three step
4 an initial step of 50 patients to understand which variables of hemodynamic changes after EEOT will affect the minimal dose of FC In this initial part a variable will be considered interesting if the relative p value will be under 01 5 A second step including 200 patients data to create the model considering also variables interactions 6 A finel step of 50 patients to validate the model
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