Ignite Creation Date:
2024-05-11 @ 8:30 AM
Last Modification Date:
2024-10-26 @ 3:29 PM
Study NCT ID:
NCT06405126
Status:
RECRUITING
Last Update Posted:
2024-05-08
First Post:
2024-04-16
Brief Title:
Validation of a Machine Learning Predictive Model to Distinguish Post-capillary Pulmonary Hypertension
Sponsor:
KU Leuven
Organization:
KU Leuven
Study Overview
Official Title:
Optimizing the Pulmonary Hypertension Diagnostic Network in Belgium Validation of a Machine Learning Predictive Model to Distinguish Post-capillary Pulmonary Hypertension
Status:
RECRUITING
Status Verified Date:
2024-05
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:
Optiek 2-0
Brief Summary:
In this study the diagnostic accuracy of a diagnostic tool for the diagnosis of post-capillary pulmonary hypertension will be investigated
The diagnostic tool was designed based on artificial intelligence using machine learning on a database of 344 patients with group 1 or group 2 pulmonary hypertension The tool uses 20 non-invasive parameters which are derived from laboratory results ECG echocardiography and spirometry Based on these parameters the predictive tool estimates the probability of group 2 pulmonary hypertension
During this clinical study patients with an intermediate or high suspicion of pulmonary hypertension with an indication for a diagnostic right heart catheterization will be included Patients with risk factors for group 3 4 or 5 pulmonary hypertension will be excluded
The necessary parameters to run the predictive model will be extracted from the patients medical file Patients will undergo a standard of care right heart catheterization gold standard Afterwards the results of the predictive model will be compared to those of the right heart catheterization to allow the assessment of the sensitivity specificity positive and negative predictive value of the predictive tool
The diagnostic tool was designed based on artificial intelligence using machine learning on a database of 344 patients with group 1 or group 2 pulmonary hypertension The tool uses 20 non-invasive parameters which are derived from laboratory results ECG echocardiography and spirometry Based on these parameters the predictive tool estimates the probability of group 2 pulmonary hypertension
During this clinical study patients with an intermediate or high suspicion of pulmonary hypertension with an indication for a diagnostic right heart catheterization will be included Patients with risk factors for group 3 4 or 5 pulmonary hypertension will be excluded
The necessary parameters to run the predictive model will be extracted from the patients medical file Patients will undergo a standard of care right heart catheterization gold standard Afterwards the results of the predictive model will be compared to those of the right heart catheterization to allow the assessment of the sensitivity specificity positive and negative predictive value of the predictive tool
Detailed Description:
None
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
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| CIV-23-11-044814 | OTHER | Eudamed | None |