Supplementary material
Full title: T1-mapping and outcome in nonischemic cardiomyopathy: all-cause mortality
and heart failure
Short title: T1-mapping and outcome in nonischemic cardiomyopathy
First author: Valentina O. Puntmann
Authors:
Valentina O. Puntmann1-4*, MD, PhD; Gerry Carr-White2,3, MBBS, PhD; Andrew
Jabbour5, MBBS, PhD; Chung-Yao Yu5, MBBS; Rolf Gebker6 MD, PhD; Sebastian
Kelle6, MD, PhD; Rocio Hinojar1,2,7, MD, Mres; Adelina Doltra6, MD, PhD; Niharika
Varma2,4, MD; Nicholas Child2,4, MBBS, PhD; Toby Rogers3,4,8, MD; Gonca Suna3,8,
MD; Eduardo Arroyo Ucar2, MD; Ben Goodman2, MSc; Sitara Khan3,8, MD, PhD;
Darius Dabir2,9 , MD; Eva Herrmann10, PhD; Andreas M. Zeiher1, MD, PhD; Eike
Nagel1-4,11, MD, PhD, on behalf of International T1 Multicenter CMR Outcome Study
Affiliations:
1
Department of Cardiology, University Hospital Frankfurt, Frankfurt-am Main, Germany
Guys and St Thomas’ NHS Trust, London, United Kingdom
2
King’s College Hospital NHS Trust, Denmark Hill, London, United Kingdom
3
Department of Cardiac Imaging, King’s College London, United Kingdom
4
St Vincent’s University, Sydney, Australia
5
6
German Heart Institute Berlin, Berlin, Germany
7
Department of Cardiology, University Hospital Ramón y Cajal, Madrid, Spain
Cardiovascular Division, King’s College London, London, UK
8
9
Department of Radiology, University of Bonn, Bonn, Germany
10
Institute of Biostatistics and Mathematical Modelling at Goethe University Frankfurt;
Frankfurt am Main, Germany
11
Institute of Experimental and Translational Cardiac Imaging, DZHK Centre for
Cardiovascular Imaging, Goethe University Frankfurt, Frankfurt am Main, Germany
Methods
Multicentre consortium
A standardized T1-mapping acquisition and postprocessing protocol, developed and validated
at King’ s College London, was distributed to participating centers and comparability as well
as reproducibility were determined and optimized at each location for each field strength.
Sequence specific reference ranges were established in healthy controls. In brief, a
standardized T1-mapping acquisition protocol was adopted by participating centers with
CMR expertize, which actively support their local clinical cardiology departments. The two
UK sites contributed 409 (65%) subjects as the study, followed by Sydney (134, 21%) and
Berlin (94, 15%). Delivery of clinical care was compliant with international guidelines and
recommendations on patient management. The study has been registered with
www.clinicaltrials.gov (NCT02407197).
The primary diagnosis of DCM was made by the primary physicians in charge of clinical
care. Echocardiography was the lead investigation of assessment of cardiac function and
structure. In participating centres, CMR services are integrated into clinical routine and
operate on a range of standard referral reasons, including: confirmation of the clinical
diagnosis of DCM (cardiac volumes and function), assessment of the presence and extent of
LGE, insight into underlying etiology (based non-ischaemic LGE patterns). Relevant clinical
metadata were collected for all patients, as summarized in Table 1. All patients were stable at
the time of inclusion with no change in regular medication in previous 6 weeks. None of the
patients was considered for immediate device treatment (ICD, CRT or LVAD) at the time of
the CMR study. None of the patients previously required an ITU admission or inotropic
support. Competing risk analysis was performed by review of clinical data prior to inclusion
in the study for likelihood of intervening events that could confound the main outcome (an
active malignancy, active chemotherapy, etc). Patients with history of a previous malignancy
were in remission > 24 months and discharged from further follow-up.
We characterize the whole cohort in terms of their mortality risk from heart failure using the
MAGGIC score, which is a generalizable easily used risk score for mortality in patients with
HF, and includes 13 highly significant independent predictors of mortality in the following
order of predictive strength: age, EF, NYHA class, serum creatinine, diabetes, beta-blocker,
systolic BP, body mass index, time since diagnosis, smoking, chronic obstructive pulmonary
disease, gender, and ACE-inhibitor or angiotensin-receptor blockers [19].
Cardiovascular magnetic resonance image acquisition
All subjects underwent a standardized CMR protocol for routine assessment of cardiac
volumes, mass and LGE imaging, at 1.5 or 3-Tesla (T) scanner equipped with advanced
cardiac software, multi-transmit technology (3T only) and a 32-channel receiver coil
(Achieva or higher, Philips Healthcare, Best, The Netherlands).
All cine CMR were performed using a balanced steady-state free precession sequence in
combination with parallel imaging (SENSitivity Encoding, factor 2) and retrospective gating
during a gentle expiratory breath- hold (TE/TR/flip-angle: 1.7ms/3.4ms/60°, spatial resolution
1.8x1.8x8 mm).
Late gadolinium enhancement was performed using gapless whole heart coverage of short
axis (SAX) slices ~15 minutes after administration of 0.1 or 0.2 mmol/kg body weight
gadobutrol (Gadovist®, Bayer, Leverkusen, Germany), as appropriate for the individual
eGFR) [1], using a mid-diastolic inversion prepared 2-dimensional gradient echo sequence
(TE/TR/flip-angle 2.0 msec/3.4 msec/25°, acquired voxel size 1.4x1.4x8mm) with an
individually adapted prepulse delay to achieve optimally nulled myocardium.
Balanced steady state free precession single breath-hold modified Look-Locker Imaging
(MOLLI, (3(3)3(3)5)) was used for T1 mapping and performed in a single midventricular
short axis slice at mid-diastole, prior to contrast administration and prior to LGE imaging,
respectively (TE/TR/flip-angle: 1.64msec/3.3msec/50°, acquired voxel size 1.8x1.8x8 mm,
phase encoding steps n=166, 11 images corresponding to different inversion times (3+3+5
MOLLI scheme), adiabatic prepulse to achieve complete inversion). Full sequence exam card
is included at the end of this document (Table 1S).
Image analysis
Assessment of cardiac volumes and LV mass was performed following recommendations for
standardized postprocessing using commercial software (CVI42®, Circle, Calgary, Canada).
Endocardial LV borders were manually traced at end-diastole and end-systole. The papillary
muscles were included as part of the LV cavity volume. LV end-diastolic (EDV) and endsystolic (ESV) volumes were determined using rule of discs. Ejection fraction (EF) was
computed as EDV-ESV/EDV. All volumetric indices were normalized to body surface area
(BSA).
T1 measurements were performed using OsiriX® (Pixmeo, Switzerland)-based plug-in in a
midventricular short axis (SAX) slice conservatively within the septal myocardium (septal) as
well as in the whole SAX myocardium, as previously described and validated. Following
offline image co-registration and motion correction, T1 values were determined by fitting a 2parameter exponential model to the measured data applying Look-Locker, noise and heart
rate correction. Care was taken to avoid contamination with signal from the blood pool. Areas
of LGE were excluded from the T1 region of interest.
Statistical analysis
Missing data for hematocrit was solved using multiple imputations based on regression
method (SPSS V.22). Using this programme we first analyzed the existing data for the
patterns and determined that the hematocrit values were missing in a random fashion. We
used a set number of 5 imputations and chose the automatic method. We reviewed the
iterations for variance. Means and variances of the pooled imputed hematocrit data were
similar to the original (same for ECV original vs. the total using also the imputed). We also
have tested the comparative strengths of predictions based on original and pooled data, which
remained similar.
A uni- and multi-variable Cox proportional hazards model was used for analyzing predictive
associations of variables with the outcome. Univariable Cox regression models were fitted
for each continuous predictor to test the assumption of linearity with the outcome. For T1
mapping measurements, this was performed per field-strength, as well for the whole cohort.
Kaplan-Meier curves are used for visualizing the cumulative event free survival. A log-rank
test was performed to compare event free survival curves in subjects for dichotomized
variables.
Collinearity in multivariable analysis was avoided by exclusion of variables, which suffered
with interdependency between these same variables in its calculations (such as native T1 and
ECV, or NYHA and MAGGIC score), as well as by grouping the role of different biomarkers
in prediction of outcome as per their physiological meaning (i.e. tissue characterization of
diffuse disease (by T1 mapping indices) vs. tissue characterization of regional disease (by
LGE), vs. biomarkers of cardiac structure (EDV) vs. biomarkers of cardiac function (by EF).
We used ‘the strongest of the group’ approach: we weighted the individual biomarker by
individual Chi2/Wald values obtained in univariable analyses to select the strongest
biomarker of the group (for example: tissue characterization of diffuse disease – T1 mapping
indices-> native T1). Where there were more than 1 marker available, we only conducted
analysis by the strongest predictor of the group, which allowed controlling for the
interdependency of measures and collinearity effects on their individual prediction).
Posthoc sample size analyses are performed as comparisons of rates for all cause mortality,
using Post-hoc Power Calculator (http://clincalc.com/Stats/Power.aspx). For this we used
dichotomized data for native T1 (normal/abnormal), as well as native T1 in lower-middle vs
upper tertile, based on the present cohort of 637 subjects.
Results
Native T1 normal/abnormal
Native T1 (tertiles)
All cause mortality
Univariable analysis
LR Chi2 (p-value)
Wald
Adj HR (95%CI)
Sig. (p-value)
Multivariable analysis
1.5 T
Model 1
<0.001
23.5(<0.001)
Native T1 (septal)(per 10msec change)
27.1
1.1 (1.11-1.27)
<0.001
Native T1 (septal)(per 10msec change)
14.2
1.1 (1.109-1.22)
<0.001
ECV (%)
8.9
1.06(1.01-1.16)
0.002
15.8
1.1 (1.08-1.21)
<0.001
18.6.
1.1(1.04-1.21)
<0.001
9.0
1.06(1.01-1.16)
0.001
Model 2
3.0 T (n=280)
Model 1
Native T1 (septal)(per 10msec change)
ECV (per % change)
28.1(<0.001)
17.4 (<0.001)
23.2 (<0.01)
Discussion
The performance of ECV may have been reduced by the following 2 reasons:
1.) Hematocrit was unavailable in 15.9% of patients. In 45% of patients hematocrit was
sampled on the same day. The most recent SCMR recommendations on ECV
determination recommend hematocrit contemporaneous sampling at the time of CMR
study. It must be noted, that a) this statement is not a guideline, b) this
recommendation has been made on consensus of a scientific premise rather than on
evidence of feasibility. Most importantly, the viability of this recommendation has
not been tested in clinical practice. There is evidence for considerable variability of
hematocrit measurement, due to within-subject biological variation (3%) and
analytical variation (3%), which may explain a relative change of approximately 12%
between two successive hematocrit values, measured with a time interval between 1
day and 1-2 months, in a normal healthy adult [2]. Whereas evidence in the presence
of disease and medications is missing, there is evidence that methods used of one
hospital vary from that of another [3,4]. Therefore, the point of care blood testing at
the scanner appears to offer no alternative to the central laboratory testing (as used in
this study) due to inherent high variability of hematocrit measurement. We conducted
a prospective study in a clinical setting involving active clinical CMR imaging
departments with high patient throughput. Although these were departments with
dedicated CMR scanners, we found implementation of contemporaneous blood
sampling/hematocrit measurement at the scanner impossible due to constraints on
time and personnel without available specific research personal, resulting in higher
costs of the CMR procedure.
2.) The results of T1 mapping indices are T1 mapping sequence dependent. For example,
a very T1 accurate sequence will provide more information in the postcontrast values
while missing important information in the native measurement, and conversely. As
such, ECV by a T1 accurate method and ECV by the type of MOLLI, as used in the
present study, may differ significantly in terms of information and may not be
comparable. In the young field of T1 mapping research, details of all these aspects
are relevant, and should not be limited to the research in phantoms, but provided by
experience in a clinical setting, because many of these effects cannot be sufficiently
understood by in vitro measurements. Investigators should be encouraged to report
the data in full, as well as to include the details of sequences to clarify the different
results, as a part of comprehensive clinical sequence characterization.
Loss to follow-up is a major limitation of prognostic models. We observed 8% loss-to follow
up, which is more than the observed deaths. This is partly a reflection of an increasingly
mobile human population particularly affected by inclusion of London-sites. However, these
subjects were on average similar to the overall cohort in terms of heart risk score (MAGGIC)
and T1 mapping indices. Applying the similar rate of events as observed in the overall cohort
we believe that we would have missed at the most 1.6 cardiac mortality event – which would
not substantially change the results based on the overall cohort.
LGE reflects extracellular accumulation of gadolinium contrast agent and it is visualized
when the disease is sufficiently regionalized, allowing the contrast between one type of tissue
to another. LGE is a marker of extracellular regional myocardial disease. In a defined clinical
setting it corresponds to replacement fibrosis. In another setting it may also represent
extracellular edema and other histological entities, with or without various amounts of
cellular disruption. T1 mapping (outside LGE) is a quantifiable reflection of myocardial
composition, including areas, which LGE cannot see based on its regionally defined scale.
We strived to exclude LGE from T1 mapping ROIs by cross-referencing the images,
especially in cases of midwall striae. Because midwall stria tend to be present in basal
segments, they are usually out of the mid-SAX imaging plane and rarely problematic. Thus,
the number of patients where the LGE poses an issue for the septal ROI is relatively small.
The effect of non-ischaemic LGE is likely minimal, however this issue requires a systematic
study, also across a range of LGE sequences [5].
References:
1. Reiter T, Ritter O, Prince MR, Nordbeck P, Wanner C, Nagel E, Bauer WR.
Minimizing risk of nephrogenic systemic fibrosis in cardiovascular magnetic
resonance. J Cardiovasc Magn Reson. 2012;14:31.
2. Thirup P. Haematocrit: within-subject and seasonal variation. Sports
Med. 2003;33(3):231-43.
3. Lacher DA, Barletta J, Hughes JP. Biological variation of hematology tests based on
the 1999-2002 National Health and Nutrition Examination Survey. Natl Health Stat
Report. 2012;(54):1-10.
4. Banfi G1, Lombardi G, Colombini A, Lippi G. A world apart: Inaccuracies of
laboratory methodologies in antidoping testing. Clin Chim Acta. 2010;411(1516):1003-8.
5. Puntmann VO, Voigt T, Chen Z, et al. Native T1 mapping in differentiation of
normal myocardium from diffuse disease in hypertrophic and dilated
cardiomyopathy. JACC Cardiovasc Imaging. 2013;6(4):475-84
Table 1S. Details of T1 mapping sequence parameters based on the exam card.
3(3)3(3)5MOLLI
Nucleus =
H1;
Coil selection 1 =
HRTorsoCardiacP;
element selection =
Coil selection 2 =
element selection =
All (16);
HRTorsoCardiacA;
All (16);
Dual coil =
yes;
CLEAR =
yes;
Recon voxel size (mm) =
0.99;
Reconstruction matrix =
400;
SENSE =
yes;
P reduction (RL) =
2;
P os factor =
1;
Stacks =
1;
slices =
1;
fold-over direction =
AP
fat shift direction =
F;
Contrast enhancement =
B-FFE offset frequency =
balanced;
0;
Acquisition mode =
cartesian;
Fast Imaging mode =
TFE;
shot mode =
TFE startup echoes =
(number) =
single-shot;
user defined;
10;
B-TFE startup mode =
lin. sweep up;
shot interval =
shortest;
profile order =
linear;
Echoes =
1;
TE =
shortest;
Flip angle (deg) =
50;
TR =
shortest;
Halfscan =
yes;
factor=
0.60;
Water-fat shift =
minimum;
RF Shims =
adaptive;
Shim =
volume;
ShimAlign =
no;
Fat suppression =
no;
Water suppression =
no;
TFE prepulse =
invert;
delay =
(ms) =
user defined;
300;
T1 mapping =
MOLLI 3-3-5;
Cardiac synchronization =
trigger;
device =
Cardiac frequency =
ECG;
50;
R-R window (%) =
10, 20;
Number of heart phases =
single phase;
trigger delay =
longest;
Respiratory compensation =
breath hold;
Reference tissue =
Cardiac muscle;
Total scan duration =
00:18.0;
Rel. signal level (%) =
100;
Act. TR/TE (ms) =
3.3 / 1.64;
Scan time / BH =
00:18.0;
ACQ matrix M x P =
180 x 216;
ACQ voxel MPS (mm) =
1.80 / 1.80 / 8.00;
REC voxel MPS (mm) =
0.98 / 0.97 / 8.00;
Scan percentage (%) =
100;
TFE factor =
67;
TFE dur. shot / acq (ms) =
259.0 / 219.7;
TFE shot interval (beats) =
1;
Min. TI delay =
92.5238037;
Entered heartrate =
50;
Trigger delay max. / act. (ms) =
1080.0 / 908.6;
Max. heart phases =
3;
Act. WFS (pix) / BW (Hz) =
0.393 / 1104.7;
Min. WFS (pix) / Max. BW (Hz) =
0.391 / 1111.1;
SAR / local torso =
< 59 %;
Whole body / level =
< 0.9 W/kg / normal;
B1 rms =
1.60 uT / 47 %;
PNS / level =
47 % / normal;
Table 2S. Patients’ baseline characteristics. A- demographic data, B-Cardiovascular magnetic resonance findings. Data is presented as median
(interquartile range, IQR), comparisons *p-value of <0.05, **p<0.01. Comparison between groups was made for HF endpoint (a composite of HF death or
unplanned HF hospitalization). Significance relates to the comparisons between patients that survived and died (p<0.05 is considered significant). BMI –
body mass index, NYHA – New York Heart Association, GFR – glomerular filtration rate, SCD – sudden cardiac death; RAS- Renin-angiotensin-aldosterone
system; MAGGIC integer risk score of survival in heart failure, as previously described [20]. LV – left ventricular, EDV – end-diastolic volume, ESV – endsystolic volume, EF-ejection fraction, RV-right ventricular, LGE- late gadolinium enhancement, SAX – short axis, ECV – extracellular volume fraction, T –
Tesla. Results for patients with available haematocrit results only.
A. Patients characteristics
All patients
No HF endpoint
HF Endpoint
Significance
(n=536)
(n=501)
(n=62)
(P-value)
Age (years)
50(37-76)
49(39-74)
52(42-77)
0.36
Gender (male n,%)
332(62%)
295 (60%)
37 (60%)
0.95
BMI (kg/m2)
27(23-30)
27(23-30)
26(22-29)
0.70
Heart rate (bpm)
69(60-79)
69(60-78)
69(61-74)
0.72
127(113-139)
126(113-138)
131(119-141)
0.82
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)
79(71-85)
77(69-84)
80(70-89)
0.86
Estimated GFR (mls/min/1.73m2)
79(76-81)
79(77-82)
77(64-79)
0.09
Haematocrit (%)
43(39-46)
44(39-45)
42(37-45)
0.33
Hypertension (n,%)
257(48%)
225(45%)
32 (51%)
0.43
Diabetes (n, %)
129(24%)
115(23%)
14 (22%)
0.85
Atrial fibrillation (n,%)
43(8%)
33(7%)
10(16%)
0.009
High cholesterol (n,%)
161(30%)
140(28%)
21(34%)
0.30
Smoking, current or previous (n,%)
150(28%)
127(25%)
23(37%)
0.03
Family history of cardiomyopathy or SCD
48(9%)
52(9%)
4(7%)
0.58
Alcohol excess (n,%)
70(13%)
68 (9%)
2(3%)
0.06
Chronic kidney impairment (n,%)
86(16%)
71(14%)
15(24%)
0.10
II
381(71%)
352(70%)
29(47%)
<0.001
>III
155(29%)
122(24%)
33(53%)
<0.001
13 (10-19)
13(10-18)
15(11-22)
0.005
NYHA functional class (n,%)
MAGGIC score
Medication
RAS inhibitors
311(58%)
282(56%)
29(63%)
0.04
Diuretics
204(38%)
177(35%)
35(57%)
<0.001
Beta-blockers
155(29%)
130(26%)
25(40%)
0.008
Calcium channel blockers
145(27%)
126(25%)
19(30%)
0.37
All patients
No HF endpoint
HF Endpoint
Significance
(n=536)
(n=501)
(n=62)
(P-value)
LVEDV index (mL/m2)
109(89-132)
101(91-125)
114(90-119)
0.04
LVESV index (mL/m2)
48(31-58)
44(30-55)
56(39-70)
0.07
LVEF (%)
47(29-50)
48(43-51)
44(34-48)
0.001
B. CMR quantification of function and
structure
LV mass index (g/m2)
88(62-98)
87(66-93)
94(63-107)
0.29
RVEF (%)
53(31-61)
55(49-61)
44(32-59)
<0.001
145 (27%)
116 (24%)
29 (46%)
<0.001
Midwall stria (n,%)
60(11%)
43(9%)
17(27%)
<0.001
Epicardial (n,%)
21(4%)
17 (4%)
4 (7%)
0.25
Regional fibrosis (patchy), n%)
21 (4%)
18 (4%)
3(4%)
0.71
Diffuse (n,%)
43 (8%)
39 (8%)
4(6%)
0.57
6.2(2.1-9.5)
5.7(1.9-7.2)
8.1(3.2-11.4)
0.002
Native T1 (septal) (msec)
997(958-1056)
991(955-1012)
1052(1002-1081)
<0.001
Native T1 (SAX)(msec)
962(842-1031)
959(898-1001)
978(926-1022)
0.06
Postcontrast T1 (msec)
439(397-483)
441(397-483)
429(401-471)
0.23
26(21-32)
26(22-30)
30(23-31)
<0.001
CMR tissue characterisation
LGE (present, n,%)
LGE type
LGE extent (% of LV volume)
CMR T1 mapping
1.5 T (n=303)
ECV (%)
3.0 T (n=233)
Native T1 (msec)
1113(1064-1157)
1106(1057-1142)
1177(1106-1203)
<0.001
Native T1 (SAX)(msec)
1058(958-1128)
1057(956-1104)
1079(968-1137)
0.04
Postcontrast T1 (msec)
441(401-489)
439(401-489)
429(411-488)
0.36
26(21-32)
26(21-30)
30(26-33)
<0.001
ECV (%)