Clinical trial NCT06405126: Validation of a Machine Learning Predictive Model to Distinguish Post-capillary Pulmonary Hypertension
Official title: Optimizing the Pulmonary Hypertension Diagnostic Network in Belgium: Validation of a Machine Learning Predictive Model to Distinguish Post-capillary Pulmonary Hypertension
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Pulmonary Hypertension
Pulmonary Arterial Hypertension
HFpEF - Heart Failure With Preserved Ejection Fraction
Keywords: pulmonary hypertension
pulmonary arterial hypertension
post-capillary pulmonary hypertension
HFpEF
artificial intelligence
diagnostic tool
machine learning
non-invasive diagnosis
Lead sponsor: KU Leuven
Sponsor class: OTHER
Collaborators: name: Research Foundation Flanders
First posted: 2024-05-08
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with an intermediate to high probability of pulmonary hypertension based on an echocardiography. Patients with a probability of group 3, 4 or 5 pulmonary hypertension will be excluded.

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.


Eligibility criteria: Inclusion Criteria:

1. Voluntary written informed consent of the participant or their legally authorized representative has been obtained prior to any screening procedures
2. Male or female patients of at least 18 years old.
3. Availability of the results of a basic work-up:

   1. Medical history, demographic information and clinical information (including BMI)
   2. Laboratory tests including hemoglobin, hematocrit and uric acid
   3. ECG
   4. Pulmonary function tests
   5. Echocardiography
4. Intermediate to high probability of PH based on echocardiography according to the 2022 ESC/ERS guidelines (see Figure 2 and Table 2). (1)
5. Indication for RHC according to ESC/ERS 2022 guidelines. (1)

Exclusion Criteria:

1. Evidence of significant pulmonary comorbidity based on abnormal pulmonary function tests (FEV1 below 60%) or aberrant lung parenchyma more than mild on radiological imaging.
2. Perfusion defects and ventilation mismatch on a recent V/Q scan.
3. Arterial perfusion defects on a recent thoracic CT angiography.
4. The following comorbidities associated with group 1 PH:

   1. Connective tissue disease
   2. HIV infection
   3. Portal hypertension
   4. Congenital heart disease
5. The following comorbidities associated with group 5 PH:

   1. Hematological disorders such as chronic hemolytic anemia or myeloproliferative disorders.
   2. Systemic and metabolic disorders such as pulmonary Langerhans cell histiocytosis, Gaucher disease, glycogen storage diseases, neurofibromatosis or sarcoidosis.
   3. Chronic renal failure (eGFR below 30 ml/min) with or without hemodialysis
   4. Fibrosing mediastinitis

Primary outcomes:
measure: Diagnostic accuracy of the Optiek model
timeFrame: 18 months
Secondary outcomes:
measure: Number of right heart catheterizations which could have been avoided
timeFrame: 18 months
measure: Right heart catheterization related adverse events
timeFrame: 18 months
measure: Feasibility of the implementation of the Optiek model in clinical practice
timeFrame: 18 months
Other outcomes:
Locations:
city: Genk
country: Belgium
facility: Ziekenhuis Oost-Limburg
state: Limburg
city: Hasselt
country: Belgium
facility: Jessa Hospital
state: Limburg
city: Leuven
country: Belgium
facility: UZ Leuven
state: Vlaams Brabant
city: Kortrijk
country: Belgium
facility: AZ Groeninge
state: West-Vlaanderen
Study officials:
References: