Using machine learning to improve diagnosis of 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

This study is testing a new computer model that uses simple health tests to see if it can help doctors diagnose post-capillary pulmonary hypertension more accurately in patients who might have it.

Quick facts

• Study type: Observational
• Enrollment: 100 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: KU Leuven (Academic / other)
• Locations: 4 sites (Genk, Limburg and 3 other locations)
• Trial ID: NCT06405126 on ClinicalTrials.gov

What this trial studies

This observational study aims to validate a machine learning predictive model designed to enhance the diagnostic accuracy for post-capillary pulmonary hypertension. The model utilizes 20 non-invasive parameters derived from various clinical assessments, including laboratory results, ECG, echocardiography, and spirometry, to estimate the likelihood of group 2 pulmonary hypertension. Patients with an intermediate or high suspicion of pulmonary hypertension who require a diagnostic right heart catheterization will be included in the study. The model's effectiveness will be evaluated against standard diagnostic procedures.

Who should consider this trial

Good fit: Ideal candidates are adults aged 18 and older with an intermediate to high probability of pulmonary hypertension based on echocardiography.

Not a fit: Patients with significant pulmonary comorbidities or those with risk factors for group 3, 4, or 5 pulmonary hypertension may not benefit from this study.

Why it matters

Potential benefit: If successful, this study could lead to more accurate and timely diagnoses of post-capillary pulmonary hypertension, improving patient management and outcomes.

How similar studies have performed: Other studies utilizing machine learning for diagnostic purposes in pulmonary hypertension have shown promise, indicating potential for success in this approach.

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

Where this trial is running

Genk, Limburg and 3 other locations

• Ziekenhuis Oost-Limburg — Genk, Limburg, Belgium (Recruiting)
• Jessa Hospital — Hasselt, Limburg, Belgium (Recruiting)
• UZ Leuven — Leuven, Vlaams Brabant, Belgium (Recruiting)
• AZ Groeninge — Kortrijk, West-Vlaanderen, Belgium (Recruiting)

Study contacts

• Study coordinator: Laura Hardy, MD

Conditions Pulmonary Hypertension; Pulmonary Arterial Hypertension; HFpEF - Heart Failure With Preserved Ejection Fraction; pulmonary hypertension; pulmonary arterial hypertension; post-capillary pulmonary hypertension; HFpEF; artificial intelligence