David Kiely, MD University of Sheffield

1. Imaging assessment in pulmonary
hypertension
David G Kiely
Sheffield Pulmonary Vascular Disease Unit
15th Annual NC Research Triangle Pulmonary Hypertension
Symposium
Thursday 17th November 2023, 1015-11.00am

2. Disclosures
• David has received honoraria for participation in advisory boards, steering
committees, speaker fees and funding to attend educational meetings from
Actelion/Janssen, Altavant, Bayer, Ferrer, Gossamer, GSK, and MSD
• The Sheffield Pulmonary Vascular Disease Unit has received grant funding in the
last 3 years from Actelion/Janssen, Ferrer, GSK, NIHR, BHF, Wellcome Trust and
MRC
• David is the Chair of the UK National Pulmonary Hypertension Audit and a
member of the clinical reference group for NHS England specialist respiratory
medicine

3. Sheffield:

4. Overview
CHD: congenital heart disease; CTD: connective tissue disease; PAH: pulmonary arterial hypertension; PH: pulmonary hypertension.
Pulmonary hypertension overview, classification and imaging
How do we make the most of CT imaging in clinical practice?
Imaging and the near future: AI applications
Why use MRI in PH and can we use it as an end-point in clinical trials?
?

5. PH is heterogeneous
Adapted from Kiely DG et al, BMJ 2013 with the updated classification from the 2022 ESC/ERS Guidelines Humbert M et al 2022
4. PH associated with
pulmonary artery obstructions
4.1 Chronic thrombo-embolic PH (CTEPH)
4.2 Other pulmonary artery obstructions
3. PH in associated with lung disease
and/or hypoxia
3.1 Obstructive lung disease or emphysema
3.2 Restrictive lung disease
3.3 Lung disease with mixed restrictive/obstructive
pattern
3.4 Hypoventilation syndromes
3.5 Hypoxia without lung disease
3.5 Developmental lung disorders
2. PH in association with left heart disease
2.1 PH due to heart failure with preserved LVEF
2.2 PH due to heart failure with reduced LVEF
2.3 Valvular heart disease
2.4 Congenital/acquired cardiovascular conditions leading to
post-capillary PH
5. PH with unclear and/or
multifactorial mechanisms
5.1 Hematological disorders
5.2 Systemic disorders
5.3 Metabolic disorders
5.4 Chronic renal failure
5.5 Pulmonary tumor thrombotic
microangiopathy
5.6 Fibrosing mediastinitis
1. Pulmonary arterial hypertension
1.1 Idiopathic PAH
1.1.1 Non-responders at vasoreactivity
1.2.2 Non-responders at vasoreactivity
1.2 Heritable PAH
1.3 Associated with drugs and toxins
1.4 Associated with:
1.4.1 Connective tissue disease
1.4.2 HIV infection
1.4.3 Portal hypertension
1.4.4 Congenital heart disease
1.4.5 Schistosomiasis
1.5 PAH with features of venous/capillary
(PVOD/PCH) involvement
1.6 Persistent PH of the newborn
Defined treatments Optimal treatment not clear

6. Hurdman J et al, Eur Resp J 2012.
1.0
0.8
0.6
0.4
0.2
0.0
0 1 2 3 4 5 6 7 8 9 10
Years from diagnosis
Cumulative
survival
p < 0.05 PAH versus CTEPH and PH-lung
157
242
598
32
178
1207
132
206
501
26
124
989
97
137
382
15
66
697
55
97
269
10
41
472
33
73
182
8
31
327
18
51
119
6
18
212
Group 2 PH-LHD
Group 4 CTEPH
Group 1 PAH
Group 5 PH-misc
Group 3 PH-lung
Total
PH-LHD
CTEPH
PAH
PH-misc
PH-lung
The importance of clinical classification: impact on outcome

7. 1.0
0.8
0.6
0.4
0.2
0.0
0 1 2 3 4 5 6 7 8 9 10
Years from diagnosis
Cumulative
survival
p < 0.05 PAH versus CTEPH and PH-lung
157
242
598
32
178
1207
132
206
501
26
124
989
97
137
382
15
66
697
55
97
269
10
41
472
33
73
182
8
31
327
18
51
119
6
18
212
Group 2 PH-LHD
Group 4 CTEPH
Group 1 PAH
Group 5 PH-misc
Group 3 PH-lung
Total
PH-LHD
CTEPH
PAH
PH-misc
PH-lung
Cumulative
survival
0 1 2 3 4 5 6 7 8 9 10
Years from diagnosis
P < 0.05 PH-COPD versus other groups
1.0
0.8
0.6
0.4
0.2
0.0
25
101
32
158
23
72
17
112
20
33
7
60
14
20
2
36
10
16
2
28
7
9
0
16
PH-sleep/
alveolar hypoventilation
PH-COPD
PH-ILD
Total
Cumulative
survival
0 1 2 3 4 5 6 7 8 9 10
Years from diagnosis
p < 0.005 IPAH versus other groups
1.0
0.8
0.6
0.4
0.2
0.0
108
175
156
439
98
143
123
364
85
102
83
270
67
67
51
185
46
44
31
121
32
30
14
76
PAH-Eisenmenger’s
IPAH
PAH-SSc
Total
PAH-Eisenmenger’s
IPAH
PAH-SSc
PH-LHD-systolic
PH-LHD-diastolic
PH-LHD-valvular
PH-sleep/
alveolar hypoventilation
PH-COPD
PH-ILD
CTEPH operated
Surgically inaccessible CTEPH
Surgically accessible
CTEPH not operated
0 1 2 3 4 5 6 7 8 9 10
Years from diagnosis
p = 0.001 PH-LHD-diastolic versus -valvular
17
98
42
157
14
84
34
132
10
65
22
97
5
37
13
55
4
21
8
33
2
12
4
18
PH-LHD-systolic
PH-LHD-diastolic
PH-LHD-valvular
Total
0 1 2 3 4 5 6 7 8 9 10
Years from diagnosis
p < 0.05 CTEPH operated versus other groups
108
52
58
218
101
45
51
197
75
27
34
136
55
19
23
97
42
17
14
73
32
10
9
51
CTEPH operated
CTEPH not operated
CTEPH surgically inaccessible
Total
The importance of clinical classification: impact on outcome
Hurdman J et al, Eur Resp J 2012.

8. “IPAH” is heterogeneous: The importance of phenotyping
Data from COMPERA and ASPIRE
Hoeper M et al, Lancet Resp Med 2022
Classical IPAH
Absence of cardiac
comorbidities
DLCO ≥ 45%
100
COMPERA
0
10
20
30
40
50
Classical IPAH (n = 128)
IPAH with lung phenotype (n = 268)
Group 3 PH (n = 910)
Proportion
of
patients
(%)
100
ASPIRE
0
10
20
30
40
50
Classical IPAH (n = 185)
IPAH with lung phenotype (n = 139)
Group 3 PH (n = 375
Proportion
of
patients
(%)
18–29 30–39 40–49 50–59 60–69 70–79 80–89 90–99
Age (years)
100
75
50
25
0
Survival
(%)
Numbers at risk
(number censored)
Classical IPAH
0 1 2 3 4 5
128 (0) 108 (14) 93 (27) 73 (44) 63 (53) 48 (65)
100
75
50
25
0
Survival
(%)
Numbers at risk
(number censored)
Classical IPAH
0 1 2 3 4 5
185 (0) 167 (15) 141 (34) 123 (48) 103 (64) 85 (73)
Time (years)
Classical IPAH
Time (years)
Classical IPAH

9. “IPAH” is heterogeneous: The importance of phenotyping
Data from COMPERA and ASPIRE
IPAH with a lung
phenotype
Smoking history
DLCO < 45%
Classical IPAH
Absence of cardiac
comorbidities
DLCO ≥ 45%
100
COMPERA
0
10
20
30
40
50
Classical IPAH (n = 128)
IPAH with lung phenotype (n = 268)
Group 3 PH (n = 910)
Proportion
of
patients
(%)
100
ASPIRE
0
10
20
30
40
50
Classical IPAH (n = 185)
IPAH with lung phenotype (n = 139)
Group 3 PH (n = 375
Proportion
of
patients
(%)
18–29 30–39 40–49 50–59 60–69 70–79 80–89 90–99
Age (years)
100
75
50
25
0
Survival
(%)
Classical IPAH
IPAH with lung phenotype
Numbers at risk
(number censored)
Classical IPAH
IPAH with lung phenotype
0 1 2 3 4 5
128 (0) 108 (14) 93 (27) 73 (44) 63 (53) 48 (65)
268 (0) 211 (29) 132 (59) 77 (84) 48 (100) 25 (114)
100
75
50
25
0
Survival
(%)
Numbers at risk
(number censored)
Classical IPAH
IPAH with lung phenotype
0 1 2 3 4 5
185 (0) 167 (15) 141 (34) 123 (48) 103 (64) 85 (73)
139 (0) 100 (11) 59 (22) 29 (35) 15 (40) 12 (41)
Time (years)
Classical IPAH
IPAH with lung phenotype
Time (years)
Hoeper M et al, Lancet Resp Med 2022

10. “IPAH” is heterogeneous: The importance of phenotyping
Data from COMPERA and ASPIRE
IPAH with a lung
phenotype
Smoking history
DLCO < 45%
PH in association
with lung disease
Classical IPAH
Absence of cardiac
comorbidities
DLCO ≥ 45%
100
COMPERA
0
10
20
30
40
50
Classical IPAH (n = 128)
IPAH with lung phenotype (n = 268)
Group 3 PH (n = 910)
Proportion
of
patients
(%)
100
ASPIRE
0
10
20
30
40
50
Classical IPAH (n = 185)
IPAH with lung phenotype (n = 139)
Group 3 PH (n = 375)
Proportion
of
patients
(%)
18–29 30–39 40–49 50–59 60–69 70–79 80–89 90–99
Age (years)
100
75
50
25
0
Survival
(%)
Classical IPAH
IPAH with lung phenotype
Group 3 PH
Numbers at risk
(number censored)
Classical IPAH
IPAH with lung phenotype
Group 3 PH
0 1 2 3 4 5
128 (0) 108 (14) 93 (27) 73 (44) 63 (53) 48 (65)
268 (0) 211 (29) 132 (59) 77 (84) 48 (100) 25 (114)
910 (0) 602 (119) 407 (175) 260 (218) 168 (252) 119 (267)
100
75
50
25
0
Survival
(%)
Numbers at risk
(number censored)
Classical IPAH
IPAH with lung phenotype
Group 3 PH
0 1 2 3 4 5
185 (0) 167 (15) 141 (34) 123 (48) 103 (64) 85 (73)
139 (0) 100 (11) 59 (22) 29 (35) 15 (40) 12 (41)
375 (0) 220 (22) 133 (32) 96 (42) 63 (50) 42 (58)
Time (years)
Classical IPAH
IPAH with lung phenotype
Group 3 PH
Time (years)
Hoeper M et al, Lancet Resp Med 2022

11. How can imaging aid diagnosis and assessment of PH?
Chest radiograph is usually abnormal in patients with PH and can be
used to assist differential diagnosis1
Ventilation/perfusion lung scan is recommended to exclude CTEPH in
patients with unexplained PH1
Echocardiography is recommended as a first-line, non-invasive
diagnostic test in patients with suspected PH1
CTPA +/- perfusion (DECT/LSIM) can provide key information on
vascular, cardiac, parenchymal and mediastinal abnormalities1,2
Cardiac magnetic resonance imaging (CMRI) can accurately assess
RV size, morphology and function1,2
1. Galiè N, et al. Eur Heart J 2016; 2. Kiely DG, et al. Pulm Circ 2019

12. Kiely DG, et al. Pulm Circ 2019

13. PVRI diagnostic algorithm: importance of imaging
Kiely DG, et al. Pulm Circ 2019.

14. Overview
CHD: congenital heart disease; CTD: connective tissue disease; PAH: pulmonary arterial hypertension; PH: pulmonary hypertension.
Pulmonary hypertension classification and imaging
How do we make the most of CT imaging in clinical practice?
Imaging and the near future: AI applications
Why use MRI in PH and can we use it as an end-point in clinical trials?
?

15. Van Wolferen SA et al. Eur Heart Journal 2007 May;28(10):1250-7, Van de Veerdonk MC et al. JACC 2011 ; 58: 2511-2519, Saba T et al. Eur Resp J
2001Jul;18(1):247, Swift AJ et al. Investigative Radiology 2012 Oct;47(10):571-7, Gan CT et al. Chest 2007 Dec;132(6):1906-12, McCann GP et
al. Circulation 2005 Oct 18;112(16):e268., Blyth KG et al. Eur Heart J 2005 Oct;26(19):1993-9, Sanz J et al. American Journal of Cardiology 2007
Aug 15;100(4):731-5, Swift et al. JACC Cardiovasc imaging 2015, Swift et al. Am J Crit Care Med 2017 Jul 15;196(2):228-239, Marshall H et al. Am
J Respir Crit Care Med 2014 Sep 1;190(5):e18-9, Swift AJ et al. J Cardiovasc Magn Reson 2012 Jun 21;14:40
MRI imaging provides a comprehensive cardiopulmonary
assessment

16. Can MRI diagnose PH? Diagnosis of PH with cardiac MRI:
derivation and validation of regression models (n=603)
1. Johns S, et al. Radiology 2019; 2. Whitfield A, et al. Radiology 2020;Image adapted from Johns S, et al. Radiology 2019;
Precapillary PH status (arbitrary units) >12
−27.7
+ 5.75loge (interventricular septal angle [degree of arc])
+ 1.899loge (right ventricular mass/left ventricular mass)
+ 0.004 (diastolic pulmonary artery area [mm2])
Independent predictors of PH:
-VMI (RV mass/LV mass)
-Deviated septum (interventricular septal angle)
-Diastolic PA size

17. Can MRI identify patients with left heart disease?:
Estimate of PAWP by MRI (n=835)
Independent predictors of PAWP: High left atrial volume and LV mass
Garg P et al, European Heart Journal 2022

18. Can we predict clinically important events using cardiac MRI
in PAH?: results of a meta-analysis (n=1938)
Mortality and clinical worsening
Alabed S et al, J Am Coll Cardiol Img 2020

19. Can we predict clinically important events using cardiac MRI
in PAH?: results of a meta-analysis (n=1938)
Mortality and clinical worsening
Alabed S et al, J Am Coll Cardiol Img 2020

20. Can we predict clinically important events using cardiac MRI
in PAH populations?: results of a meta-analysis (n=1938)
Mortality and clinical worsening
Alabed S et al, J Am Coll Cardiol Img 2020

21. In the clinic : the importance of adjusting for demographics
and integrating the results with clinical data in PAH (n=576)
21
1. Swift AJ, et al. Am J Respir Crit Care Med 2017.
0 20 40 60 80 100
0
20
40
60
80
100
100% - Specificity%
Sensitivity%
IPAH validation cohort at 3 years
MRI model, AUC = 0.820
RVESVI %pred AUC = 0.724
MRI and clinical data model, AUC = 0.872
0 20 40 60 80 100
0
20
40
60
80
100
100% - Specificity%
Sensitivity%
IPAH validation cohort at 3 years
MRI model, AUC = 0.820
RVESVI %pred AUC = 0.724
RVESVI, AUC = 0.605
MRI and clinical data model, AUC = 0.872
ROC Analysis IPAH patients:
Mortality predictors1
Correcting MRI parameters for
known differences in age and sex
increases prognostic value1
Combining MRI with clinical data
(age, sex, WHO FC and PH
diagnosis) improves prognostic
predictions1

22. Can MRI aid risk stratification of patients with PAH and
improve the risk assessment (n=438)
Results from the ASPIRE Registry
Lewis RA, et al. Am J Respir Crit Care Med 2020.

23. When used in conjunction with current approaches to risk
stratification cardiac MRI reclassified 47% of patients, 36%
into a lower and 11% into a higher risk group
MRI thresholds can aid risk stratification in patients with
PAH (n = 438)
Results from the ASPIRE Registry
< 227 ≥ 227
RVESVI % pred
n = 139
(63%)
n = 80
(37%)
0
5
10
15
20
%
Mortality
(1
year)
RVESVI % pred
Lewis RA, et al. Am J Respir Crit Care Med 2020.

24. 2022 ESC/ERS Guidelines comprehensive multiparameter
risk assessment includes cardiac MRI thresholds
Humbert M et al, Eur Resp J 2022,

25. The challenge of demonstrating efficacy in PAH: why we need
a validated surrogate end-point / clinical outcome measure
• What is a surrogate end-
point?
– “a substitute for a direct
measure of how the patient
feels, functions or
survives”
• Surrogate end-points should:
– Reflect disease severity
– Predict clinically relevant
outcomes
– Non-invasive
– Repeatable
– Sensitive to treatment effect
– Have a known minimally
important difference
6th World Symposium, Sitbon O et al, Eur Resp J 2018

26. RESPIRE: Repeatability and sensitivity to change of
MRI in PAH (n=40)
Swift AJ et al. Thorax 2021.
6MWT
NT-proBNP
MRI
6MWT
NT-proBNP
MRI
6MWT
NT-proBNP
MRI
Visit 1 Visit 3
Visit 2
Tests
1
Tests
2
Tests
3
Assess
change
between
visits 1 & 2
Visits 2 and 3 performed within 24 hours
Assess test-test repeatability between
visits 2 and 3
~ 6 months
between
visits 1 & 2
Interstudy ICC
0.2
Cohen’s
d
0.4
0.6
0.8
1.0
0.0
0.00 0.25 0.50 0.75 1.00

27. RESPIRE: Repeatability and sensitivity to change of
MRI in PAH (n=40)
Swift AJ et al. Thorax 2021.
6MWT
NT-proBNP
MRI
6MWT
NT-proBNP
MRI
6MWT
NT-proBNP
MRI
Visit 1 Visit 3
Visit 2
Tests
1
Tests
2
Tests
3
Assess
change
between
visits 1 & 2
Visits 2 and 3 performed within 24 hours
Assess test-test repeatability between
visits 2 and 3
~ 6 months
between
visits 1 & 2
However, no comparison between MRI and right heart catheter measurements
Interstudy ICC
cardiac MRI metrics
pulmonary flow
DCE imaging
septal angles
walk test
Log10 NT-proBNP
RVSV
RVEF
6MWT
Log10 NT-proBNP
0.2
Cohen’s
d
0.4
0.6
0.8
1.0
0.0
0.00 0.25 0.50 0.75 1.00
PAFWHM
Septal angle
diastole

28. REPAIR: Evaluating the effect of macitentan on RV structure
and function in patients with PAH using cMRI
Vonk Noordegraaf A, et al. JACC Imaging 2022.

29. REPAIR Study: Cohen's d comparison of MRI variables with
accepted endpoints for PAH studies
Kiely DG, et al. Comparison of standardized treatment effect sizes for invasive and non-invasive endpoints in pulmonary arterial hypertension: insights
from the REPAIR study. Presented at ACC in 2021, Manuscript under revision
Treatment effect size for MRI metrics similar to PVR and greater than 6MWT distance

30. Establishing minimally important differences for cardiac
MRI end-points in pulmonary arterial hypertension (n=254)
Alabed S et al, Eur Resp J 2023

31. Using imaging and remote monitoring devices to address clinical
equipoise: a novel trial design in patients with PAH (PHOENIX)
PHOENIX Frankie Varian and Alex Rothman (Chief Investigator, UK multi-centre study)

32. Integrating imaging with remote monitoring in clinical practice:
FIT-PH study
Middleton J et al, under review

33. Overview
CHD: congenital heart disease; CTD: connective tissue disease; PAH: pulmonary arterial hypertension; PH: pulmonary hypertension.
Pulmonary hypertension classification and imaging
How do we make the most of CT imaging in clinical practice?
Imaging and the near future: AI applications
Why use MRI in PH and can we use it as an end-point in clinical trials?
?

34. CT imaging in PH: Systematic approach
Moore NR et al. Clin Radiol 1988; Devaraj et al. Radiology 2008; Condliffe R et al. Rheumatology 2011; Remy-Jardin M et al, Eur
Resp J 2021; Lewis RA et al, Respirology 2020; Castener E et al, Radiographics 2009; Swift AJ et al. Eur Radiology 2021, Rajaram
S et al. Thorax 2015;, Currie BJ et al, Int J Cardiol 2018; 260: 172-177, Mosaic perfusion Sherrick AD et al, Am J Roentgenol
1997, Montani D et al, Eur Respir J 2009.
Vessels Lungs
Cardiac chambers Mediastinum / other

35. Can CTPA diagnose PH? Diagnosis of PH with CTPA: derivation
and validation of regression models (n=491)
Swift AJ et al, Eur Radiogy 2020
PA ≥30mm + RVOTH ≥6 mm and
RV:LV ratio ≥1 highly predictive
of PH1

36. Left atrial area=16.9cm2 Left atrial area=43.75cm2
PAH
mPAP 52 mmHg, PAWP 13 mmHg
PH-LHD
mPAP 52 mmHG, PAWP 22 mmHg
Full cohort (Validation) Threshold (cm2) Sensitivity Specificity
PAWP>15 mmHg 26.8 60% (43-74%) 89% (84-94%)
PAWP>18 mmHg 30.0 53% (35-71%) 94% (89-97%)
Currie
B et al International Journal of Cardiology 2018
Can CTPA identify patients with left heart disease?:
Estimate of PAWP by MRI

37. How do CTPA and CMR perform in the diagnosis of PH and
the identification of patients with elevated PAWP?
Area under
curve (AUC)
P-value
CT metrics for mPAP ≥25mmHg
PA size 0.84 <0.001
RVOTH 0.80 <0.001
Septal angle 0.85 <0.001
MRI metrics for mPAP ≥25mmHg
Pulmonary artery area 0.83 <0.001
VMI 0.85 <0.001
Septal angle 0.85 <0.001
Diagnosis of PH1
Area under
curve (AUC)
P-value
CT metrics for PAWP>18mmHg
LA area 0.873 <0.001
LA area BSA indexed 0.855 <0.001
LA Anterior-Posterior diameter 0.842 <0.001
MRI metrics for PAWP>18mmHg
LA 2ch area 0.868 <0.001
LA 4ch area 0.857 <0.001
LA volume 0.878 <0.001
LA Volume BSA Indexed 0.865 <0.001
Identification of elevated PAWP2
1. Swift AJ et al, Eur Radiology 2020, 2. Johns CS et al, Radiology 2018

38. Overview
CHD: congenital heart disease; CTD: connective tissue disease; PAH: pulmonary arterial hypertension; PH: pulmonary hypertension.
Pulmonary hypertension classification and imaging
How do we make the most of CT imaging in clinical practice?
Imaging and the near future: AI applications
Why use MRI in PH and can we use it as an end-point in clinical trials?
?

39. Artificial intelligence: bad cop or good cop?
…. probably better than the alternative

40. AI and MRI: Using ASPIRE data to improve and automate
biventricular segmentation (n=5051 MRI in 3782 patients)
Alabed S et al, Radiology 2022.
Frequent
failures
Sporadic
failures
Satisfactory
Stronger correlations with haemodynamics
and higher repeatability on scan-rescan
testing than manual contours

41. Using feature extraction on MRI to diagnose PH and
predict outcome
Physiological feature extraction
Supervised by binary outcome
1. Swift AJ et al. Eur Heart J Cardiovasc Img 2020; 2. Alabed S et al, Eur Heart J Digit Health 2022
Diagnosis1 (treatment naïve) Mortality2 (1-yr newly diagnosed PAH)
When used in combination with REVEAL 2.0, CMR metrics and MPCA AUC for1 year mortality 0.83

42. Sharkey M et al Front Cardiovasc Med 2022.
AI and CTPA: Using ASPIRE data to automate vessel and
biventricular segmentation to aid diagnosis prediction
(validation cohort n=500)

43. Sharkey M et al Front Cardiovasc Med 2022.
AI and CTPA: Using ASPIRE data to automate vessel and
biventricular segmentation to aid mortality prediction
(validation cohort n=500)

44. Dwivedi K et al, ATS 2013 Manuscript Under review.
AI and CTPA: Using ASPIRE data to automate lung
parenchymal assessment to aid phenotyping
Total, n=521, test cohort, n=246

45. Dwivedi K et al, ATS 2013 Manuscript Under review.
AI and CTPA: Using ASPIRE data to automate lung
parenchymal assessment to aid mortality prediction
AI is sensitive to minor lung changes and
when used in with radiological reporting, it
provides additional predictive value.

46. Quantitative CT evaluation of small pulmonary vessels has
functional and prognostic significance in PH (n=1823)
Shahin Y et al, Radiology 2022, Alkhanfar D et al, ERJ Open Res 2022
.

47. On going work: How can we incorporate AI into our clinical work
streams to aid decision making: a new member of the MDT ?
Urgent review
Routine review
Discharge
Request further
information
Triage of new patient referrals
On going work Swift A, Sharkey M, Kiely DG Carusi A et al, Nature Machine Intelligence 2023

48. The ASPIRE Registry: Assessing the Spectrum of
Pulmonary hypertension Identified at a REferral centre
Opportunities for collaboration
*patients may opt out by contacting the study team or via the NHS national data opt out. Informed
consent is not required in the UK for use of retrospectively collected de-identified data for research.
Ethically approved research database of
consecutive patients1 assessed at the Sheffield
Pulmonary Vascular Disease Unit since 2001
(population 15-20 million)
Systematic evaluation in accordance to
annually audited national standards
Patients treatment-naïve at enrolment with
multiple visits
Data can be linked to Hospital Episode
Statistics and office of national statistics
allowing accurate pre-diagnosis HCRU and
survival data
Data not prospectively entered so can be
challenges with data extraction
Limited funding

49. Part of UK Network of Pulmonary Hypertension Referral centres
(67 million)
UK National PH Audit 13th annual report April 21 to March 22

50. The ASPIRE Registry: data and analytical approaches
Diagnostic and
assessment
tools
Advanced
computing
>13,000 >16,000 CT
>7,000 cMRI
Patients

51. HES = Hospital Episode Statistics, SPHInX = Sheffield Pulmonary Hypertension Index, NHS = National Health Services, STH = Sheffield Teaching Hospitals,
HSCIC = Health & Social Care Information Centre, SPVDU = Sheffield Pulmonary Vascular Disease Unit ;iPAH = idiopathic Pulmonary Arterial Hypertension
Using the ASPIRE to share de-identified data with research
partners to build the SPHInX dataset
Data accessed
via UoS DMZ
1
2
3
4 5
5
6
6
Research informatics
platform
(incl. PLD)
Analysis platform
(pseudonymised
only)
Refreshed extract of HES data was linked to diagnostic data to iPAH cohort – Sheffield Pulmonary
Hypertension Index (SPHInX) dataset
6
Research Partner
GSK
store clinical data on a server
shares NHS
numbers
returns the requested
de-identified HES
records
Study ID linked HES
data handled in
compliance
with GDPR
Analysis for the
agreed research
questions
IQVIA shares aggregated analysis of
diagnostic
pathways with GSK, STH and UoS
IQVIA
Sheffield Teaching
Hospitals NHS
The
University of
Sheffield
HSCIC now NHS Digital
Health and Social Care Information
Centre
Sheffield pulmonary vascular disease
unit
Bergemann R et. al. Pulmonary Circulation 2018; 8(4): 1-9

52. The value of collaboration: using MRI imaging database to aid
refinement of risk approaches and aid understanding of
molecular signaling
Ray Benza1: Adding MRI variables
to REVEAL 2.0 improves AUC from
0.78 to 0.83
Using advanced statistical models MRI
metrics improve performance of
current PAH risk models
Multicentre2: Transcriptional profiling identifies
subgroups of adaptive remodeling in pulmonary
hypertension
1. Correa-Jaque P et al, AHA Nov 2023 (abstract); 2. Khassafi F et al, Nature Cardiovascular Research 2023

53. In PH accurately classifying patients is important in defining disease trajectory and
likely response to treatment
MRI allows for a comprehensive cardiopulmonary assessment and is a potential
end-point for clinical trials
Exciting opportunities exist for the application of imaging to improve outcomes for
patients with PH; success will be depending on international collaboration
Take home messages
CT imaging is widely available aids disease classification and is underutilised in
patients with suspected and confirmed PH

54. Acknowledgements
• Clinical Team
• Sheffield Pulmonary Vascular Disease Unit
– David Kiely
– Charlie Elliot
– Robin Condliffe
– Thanos Charalaompopoulos
– Abdul Hameed
– Roger Thompson
– Alex Rothman
– Judith Hurdman
– Robert Lewis
– Charlotte Durrington
– Jen Middleton
– Hamza Zafar
– Frankie Varian
– Iain Armstrong
– John Harrington
– Neil Hamilton
– CNS Nurse specialist team
• Collaborators
– Jens Vogel-Claussen
– Rob Van Der Geest
– Allan Lawrie / Dennis Wang
– COHORT / COMPERA / PHORA / PVRI Go Deep
– Industry collaborations Ferrer, GSK, IQVIA, Janssen
• University of
Sheffield
• ASPIRE
– Lisa Watson and ASPIRE Data Management Committee
• Academic Dept Radiology
– Jim Wild
– Andrew Swift
– Samer Alabed
– Krit Diwedi
– Michael Sharkey
– Dheya Alkhanfar
– Adrian Goh
– Yousef Shahin
– Dave Capener
• NHS Radiology
– Catherine Hill
– Steve Thomas
– Smitha Rajaram
– Chris Johns
• Infection, Immunity and Cardiovascular Disease
– Alex Rothman and Roger Thompson and Research Group
– Haiping Lu
• Clinical Research Facility
– David Foote and Ali Lye
– Cath Billings and team
– Clinical Trials assistants

55. Sheffield in the 21st century