ORIGINAL RESEARCH

Diagnosis of pulmonary hypertension from magnetic resonance imaging–based computational models and decision tree analysis Angela Lungu,1,2 Andrew J. Swift,1,2 David Capener,1 David Kiely,2,3 Rod Hose,1,2 Jim M. Wild1,2 1

Cardiovascular Science Department, University of Sheffield, Sheffield, South Yorkshire, United Kingdom; 2Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, South Yorkshire, United Kingdom; 3Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, South Yorkshire, United Kingdom

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. Keywords: wave reflection, Windkessel, machine learning, noninvasive diagnostic. Pulm Circ 2016;6(2):181-190. DOI: 10.1086/686020.

Pulmonary hypertension (PH) is defined at right heart catheterization (RHC) as a mean pulmonary arterial pressure (mPAP) of ≥25 mmHg.1 Although RHC is currently the gold standard for confirming the diagnosis of PH and assessing response to treatment, it is an invasive procedure. In specialist centers, severe complications following RHC are low but not negligible.2 Consequently, there is a growing interest in noninvasive, alternative methods to diagnose PH and assess response to therapy, using nonionizing, image-based metrics. Magnetic resonance imaging (MRI) is considered the gold standard for the assessment of right ventricular (RV) anatomy and function,3 providing high resolution and better accuracy than echocardiography, with a high degree of reproducibility of quantitative metrics of ventricular morphology and function.4 Diagnostic metrics, based on direct quantification of the cardiopulmonary anatomy and blood flow from MRI measurements, have been proposed and show promising results.5-12 However, in addition to structural and morphological modifications of the heart and pulmonary arteries, PH also changes physiological and hemodynamic parameters, including increased pulmonary vascular resistance and decreased total vascular compliance.13-15 Computational models can bring additional insight into the hemodynamic behavior of the pulmonary

vascular system. Windkessel models16 have been implemented by several groups13,17,18 to characterize pulmonary circulation and vessels in terms of resistance and compliance and the changes between healthy and PH subjects. The presence of PH modifies these Windkessel parameters, and quantitative characterization can be provided by measurement of the energy in the reflected waves in the pulmonary artery as a percentage of the total wave energy.19,20 While showing potential for PH assessment, the results reported in these articles were based on invasive catheter measurements. We have recently shown21 that MRI measurements made in the main pulmonary artery (MPA), together with the interpretation provided by 2 such models, can provide a quantitative diagnostic characterization of PH. The first model is a 3-element Windkessel circuit (RcCRd); the capacitor C represents the compliance of the system, and Rc and Rd are proximal and distal resistors. The second is a one-dimensional (1D) model of an elastic tube that is able to separate forward-traveling and reflected waves, measured in the MPA. These models were evaluated in a pilot study of normotensive individuals and patients with PH, diagnosed and stratified according to RHC measurements of mPAP and pulmonary vascular resistance (PVR), and we showed that the zero-dimensional (0D) and 1D

Address correspondence to Dr. Jim Wild, Academic Radiology, Department of Cardiovascular Science, Floor C, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, United Kingdom. E-mail: j.m.wild@sheffield.ac.uk. Submitted November 2, 2015; Accepted February 3, 2016; Electronically published April 22, 2016. © 2016 by the Pulmonary Vascular Research Institute. All rights reserved. 2045-8932/2016/0602-0006. $15.00.

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proposed diagnosis indices can be used as differentiation criteria in PH. Our research hypothesis is that by combining already available and developed noninvasive, MRI-based cardiopulmonary and computational metrics into a machine learning classification algorithm, we can improve the noninvasive diagnostic accuracy of MRI in PH. The aim of this article is to test whether additional sensitivity and specificity can be achieved by combining several MRI-derived metrics of cardiopulmonary function and form into a decisionmaking algorithm for PH diagnosis. To achieve the proposed aim, 3 main objectives have been defined: (1) to test the individual accuracy of several MRI-based PH metrics, (2) to identify the best combination of metrics that will improve the machine learning–based classifier’s diagnostic accuracy, and (3) to evaluate the performance of the best model and discuss the results in the context of the analyzed cohort.

M ET H O D S

features and a local noninvasive assessment that usually included echocardiography. The exclusion criteria were MRI incompatibility, claustrophobia, and pregnancy. The 72-patient cohort was divided into 2 groups—no PH and PH—on the basis of a threshold of 25 mmHg, measured at RHC.22 PH was diagnosed in 15 patients, with 11 patients having an mPAP between 22 and 24 mmHg, referred to in the literature23 as borderline PH. Fifty-seven patients received the PH diagnostic. For patients in whom PH was diagnosed, the distribution within the PH subgroups was as follows: group 1, pulmonary arterial hypertension (PAH; n = 21), with 13 idiopathic PAH, 4 PAH in association with connective tissue diseases, 2 PAH in association with congenital heart disease, 1 PAH related to drug use, and 1 portopulmonary hypertension; group 2, PH owing to left heart disease (n = 11); group 3, PH associated with respiratory disease (n = 8); group 4, chronic thromboembolic PH (n = 16); and group 5, PH unclear/multifactorial (sarcoidosis; n = 1). Ethics approval was obtained (North Sheffield Ethics Committee) for analysis of routinely performed investigations, and written consent was not required.

Patients Seventy-two consecutive patients undergoing investigation for suspected PH who underwent RHC and MRI examination within 48 hours were identified from the Sheffield Pulmonary Vascular Disease Unit, a National PH referral center. The patients were referred from other centers to the Sheffield Unit on the basis of clinical

RHC RHC was undertaken using a balloon-tipped 7.5-Fr thermodilution catheter (Becton-Dickinson). PH was defined as having an mPAP of ≥25 mmHg at rest. Cardiac output was measured using the thermodilution technique. PVR was calculated as follows: PVR

Figure 1. Descriptive diagram of the noninvasive pulmonary workflow. From synchronized flow (phase contrast) and anatomy (balanced steady state free precession) images of the main pulmonary artery (MPA) over the cardiac cycle, the area A(t) and flow Q(t) were derived. A zero-dimensional model (Windkessel) and a one-dimensional model were solved, using the latter as input, and computational metrics were computed: Rc: characteristic resistance; Rd: distal resistance; C: total pulmonary compliance; Wb/Wtot: contribution of the backward reflected wave to the total pressure wave. The anatomy metrics were calculated from main pulmonary artery and cardiac images alone: RAC: relative area change; RVEDVI: right ventricle end-diastolic volume index; RVEF: right ventricle ejection fraction; VMI: ventricular mass index; RVMI: right ventricle mass index; syst angle: systolic angle. A random forest classification algorithm was applied, including all the metrics as well as their in-turn addition, and a no–pulmonary hypertension (PH)/PH diagnosis was attributed to each subject.

Pulmonary Circulation

(Wood units) = (mPAP − pulmonary capillary wedge pressure)/ cardiac output.

MRI acquisition All the patients underwent MRI in the supine position on a 1.5tesla whole-body scanner (GE HDx, GE Healthcare, Milwaukee) using an 8-channel cardiac coil. Two-dimensional (2D) phase contrast images of the MPA were acquired to quantify the flow Q(t) through the artery. Although the magnitude of images of the phase contrast sequence can be used to segment the MPA area, the main drawback of this approach is that extraction of accurate area during the diastolic period—when there is low flow in the pulmonary artery and therefore low signal—is difficult because the vessel con-

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tour is not well defined. A balanced steady state free precession (bSSFP) cine anatomical sequence with better vessel/blood delineation was used to quantify the area A(t) during the entire cardiac cycle. The 2 sequences measuring blood flow and distension were spatially and temporally synchronized, using the same imaging dimensions with retrospective cardiac gating, which generated the same number of cardiac images per heartbeat. The images were acquired during breath hold, with a slice perpendicular to the pulmonary trunk at approximately 2 cm from the pulmonary valve. Four-chamber and short-axis cine cardiac images were also acquired, using a retrospective cardiac gated multislice bSSFP sequence. The imaging parameters for all the sequences are detailed in the appendix, available online.

Table 1. Patients demographics, right heart catheterization (RHC) data, and mathematical model–image-derived parameters No PH Median Demographics Patients, no. Male/female sex, no. Age, years RHC data mPAP, mmHg PVR, Wood units mRAP, mmHg CO, L/min 1D model–derived parameter Wb/Wtot 0D model–derived parameters Rd, mmHg s/mL Rc, mmHg s/mL C, mL/mmHg PA imaging–derived parameters RAC, % CMR RVEDVI, mL/m2 RVEF, % VMI, ratio RVMI, g/m2 Systolic septal angle, degrees

Mean ± SD

15 7/8 59

56 ± 16

22 2 5 7

21 ± 3 1.9 ± 0.7 4.9 ± 2.4 6.5 ± 1.4

PH IQR

29 2.75 1.28 4 2.42

Median

P

Mean ± SD

IQR

57 25/32 67

64 ± 16

23

45 6 10 5

44.7 ± 14.3 6.9 ± 4.5 11.3 ± 3.1 5.2 ± 1.5

16.5 5.39 7.5 1.9