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RecruitingNCT06405126

Validation of a Machine Learning Predictive Model to Distinguish Post-capillary Pulmonary Hypertension

Optimizing the Pulmonary Hypertension Diagnostic Network in Belgium: Validation of a Machine Learning Predictive Model to Distinguish Post-capillary Pulmonary Hypertension

Status
Recruiting
Phase
Study type
Observational
Enrollment
100 (estimated)
Sponsor
KU Leuven · Academic / Other
Sex
All
Age
18 Years
Healthy volunteers
Not accepted

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.

Conditions

Interventions

TypeNameDescription
DIAGNOSTIC_TESTOptiek predictive modelPatient data will be extracted to run the predictive model, which will estimate the probability of group 2 pulmonary hypertension. However the results of the model will have no diagnostic or therapeutic implications in this phase of the investigation.

Timeline

Start date
2024-02-21
Primary completion
2025-08-01
Completion
2026-02-01
First posted
2024-05-08
Last updated
2024-05-08

Locations

4 sites across 1 country: Belgium

Source: ClinicalTrials.gov record NCT06405126. Inclusion in this directory is not an endorsement.