Ignite Creation Date:
2024-05-06 @ 8:18 PM
Last Modification Date:
2024-10-26 @ 3:25 PM
Study NCT ID:
NCT06333002
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-03-27
First Post:
2024-03-19
Brief Title:
Machine Learning Model to Predict Outcome and Duration of Mechanical Ventilation in Acute Hypoxemic Respiratory Failure
Sponsor:
Dr Negrin University Hospital
Organization:
Dr Negrin University Hospital
Study Overview
Official Title:
Developing an Optimal Machine Learning Model to Predict ICU Outcome and Duration of Mechanical Ventilation in Patients With Acute Hypoxemic Respiratory Failure
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MEMORIAL
Brief Summary:
Acute hypoxemic respiratory failure AHRF is the most common cause of admission in the intensive care units UCIs worldwide We will assess the value of machine learning ML techniques for early prediction of ICU death and prolonged duration 7 days of mechanical ventilation MV in 1241 patients enrolled in the PANDORA Prevalence AND Outcome of acute Respiratory fAilure Study in Spain The study was registered with ClinicalTrialsgov NCT03145974 Our aim is to evaluate the minimum number of variables models using logistic regression and four supervised ML algorithms Random Forest Extreme Gradient Boosting Support Vector Machine and Multilayer Perceptron
Detailed Description:
Acute hypoxemic respiratory failure AHRF is the most common cause of admission in the intensive care units UCIs worldwide We will assess the value of machine learning ML techniques for early prediction of ICU death and prolonged duration 7 days of mechanical ventilation MV in AHRF patients on MV Few studies have investigated the prediction of mortality and duration of MV in patients with AHRF
For model development the investigators will extract data for the first 3 days after diagnosis of AHRF from patients included in the de-identified database of the PANDORA cohort We had a database with 2000000 anonymized and dissociated demographics and clinical data from 1241 patients with AHRF enrolled in our PANDORA cohort Prevalence AND Outcome of acute Respiratory fAilure from 22 Spanish hospitals and coordinated by the principal investigator JV The investigators will follow the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis TRIPOD guidelines for model prediction We will screen collected variables employing a genetic algorithm variable selection method to achieve parsimony We evaluated the minimum number of variables models using logistic regression and 4 supervised ML algorithms Random Forest Extreme Gradient Boosting Support Vector Machine and Multilayer Perceptron We will use a 5-fold cross-validation in the dataset of 1000 patients selected randomly in training data 80 and testing data 20 For external validation we will use the remaining 241 patients
For model development the investigators will extract data for the first 3 days after diagnosis of AHRF from patients included in the de-identified database of the PANDORA cohort We had a database with 2000000 anonymized and dissociated demographics and clinical data from 1241 patients with AHRF enrolled in our PANDORA cohort Prevalence AND Outcome of acute Respiratory fAilure from 22 Spanish hospitals and coordinated by the principal investigator JV The investigators will follow the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis TRIPOD guidelines for model prediction We will screen collected variables employing a genetic algorithm variable selection method to achieve parsimony We evaluated the minimum number of variables models using logistic regression and 4 supervised ML algorithms Random Forest Extreme Gradient Boosting Support Vector Machine and Multilayer Perceptron We will use a 5-fold cross-validation in the dataset of 1000 patients selected randomly in training data 80 and testing data 20 For external validation we will use the remaining 241 patients
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None