Ignite Creation Date:
2024-10-26 @ 3:43 PM
Last Modification Date:
2024-10-26 @ 3:43 PM
Study NCT ID:
NCT06654388
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-10-09
Brief Title:
To Construct a Prognosis Prediction Model for ECMO Patients Based on Machine Learning Algorithms
Sponsor:
None
Organization:
None
Study Overview
Official Title:
To Construct a Prognosis Prediction Model for ECMO Patients Based on Machine Learning Algorithms
Status:
COMPLETED
Status Verified Date:
2018-01
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:
Extracorporeal membrane oxygenation ECMO is a critical life-support technique for patients with severe medical conditions Various factors affect the mortality rates of patients in intensive care units presenting a significant clinical challenge in accurately predicting outcomes based on a limited set of indicators
Detailed Description:
Extracorporeal Membrane Oxygenation ECMO is used to provide continuous extracorporeal respiratory and circulatory support for patients with severe cardiopulmonary failure It is the most important life support method in critical care medicine and also one of the most complex and expensive treatment methods in intensive care unit ICU It takes a lot of resources to maintain Therefore it is particularly important to strictly grasp the indications of patients and accurately predict the prognosis of patients to assist clinical decision-making
Several previous published studies have used clinical scores to predict the prognosis of ECMO patients but most of them focused on ECMO outcomes in specific patient groups such as adult respiratory distress syndromeARDS respiratory failure lung transplantation cardiogenic shock and so on In addition most of these estimates were calculated using traditional statistical methods and have limited fitting power for data sets with more characteristic variables
Artificial Intelligence AI and Machine Learning ML provide a more advanced alternative to traditional statistical methods and have unparalleled advantages in dealing with data sets with high-dimensional characteristic variables and nonlinear data And it can self-iterate to improve model performance In addition ML which can process information based on causal or statistical data may reveal hidden dependencies between clinical indicators and disease prognosis and support clinical decision making has emerged as the best alternative The primary outcome measures discharged alive from the hospital and died during hospitalization A total of 69 clinical characteristic indicators were identified based on relevant literature and insights from ECMO experts in critical care medicine These indicators included demographic data such as age height weight and the medical history of ECMO patients Additionally infection indicators were assessed within 24 hours prior to the initiation of ECMO support and within 24 hours after its discontinuation Furthermore indicators pertaining to cardiac renal and hepatic function as well as the type of shock distributive shock hypovolemic shock cardiogenic shock obstructive shock were included In addition the average daily liquid volume within three days after the initiation of ECMO support the duration of ECMO support and the ICU length of stay were also considered
Several previous published studies have used clinical scores to predict the prognosis of ECMO patients but most of them focused on ECMO outcomes in specific patient groups such as adult respiratory distress syndromeARDS respiratory failure lung transplantation cardiogenic shock and so on In addition most of these estimates were calculated using traditional statistical methods and have limited fitting power for data sets with more characteristic variables
Artificial Intelligence AI and Machine Learning ML provide a more advanced alternative to traditional statistical methods and have unparalleled advantages in dealing with data sets with high-dimensional characteristic variables and nonlinear data And it can self-iterate to improve model performance In addition ML which can process information based on causal or statistical data may reveal hidden dependencies between clinical indicators and disease prognosis and support clinical decision making has emerged as the best alternative The primary outcome measures discharged alive from the hospital and died during hospitalization A total of 69 clinical characteristic indicators were identified based on relevant literature and insights from ECMO experts in critical care medicine These indicators included demographic data such as age height weight and the medical history of ECMO patients Additionally infection indicators were assessed within 24 hours prior to the initiation of ECMO support and within 24 hours after its discontinuation Furthermore indicators pertaining to cardiac renal and hepatic function as well as the type of shock distributive shock hypovolemic shock cardiogenic shock obstructive shock were included In addition the average daily liquid volume within three days after the initiation of ECMO support the duration of ECMO support and the ICU length of stay were also considered
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