validation of a machine learning predictive model to distinguish post-capillary pulmonary hypertension

Conditions: Pulmonary Hypertension; Pulmonary Arterial Hypertension; HFpEF - Heart Failure With Preserved Ejection Fraction

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

Keywords: pulmonary hypertension pulmonary arterial hypertension post-capillary pulmonary hypertension hfpef artificial intelligence diagnostic tool machine learning non-invasive diagnosis