title: Diagnosis of pulmonary hypertension from magnetic resonance imaging-based computational models and decision tree analysis.
abstract: Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH.
authors.affiliation: Cardiovascular Science Department, University of Sheffield, Sheffield, South Yorkshire, United Kingdom; Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, United Kingdom.
authors.forename: Angela
authors.initials: A
authors.lastname: Lungu
authors.affiliation: Cardiovascular Science Department, University of Sheffield, Sheffield, South Yorkshire, United Kingdom; Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, United Kingdom.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Swift
authors.affiliation: Cardiovascular Science Department, University of Sheffield, Sheffield, South Yorkshire, United Kingdom.
authors.forename: David
authors.initials: D
authors.lastname: Capener
authors.affiliation: Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, United Kingdom; Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, South Yorkshire, United Kingdom.
authors.forename: David
authors.initials: D
authors.lastname: Kiely
authors.affiliation: Cardiovascular Science Department, University of Sheffield, Sheffield, South Yorkshire, United Kingdom; Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, United Kingdom.
authors.forename: Rod
authors.initials: R
authors.lastname: Hose
authors.affiliation: Cardiovascular Science Department, University of Sheffield, Sheffield, South Yorkshire, United Kingdom; Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, United Kingdom.
authors.forename: Jim M
authors.initials: JM
authors.lastname: Wild
keywords: Windkessel; machine learning; noninvasive diagnostic; wave reflection