APPENDIX
Table of contents
Table of contents ......................................................................................................................1
I. Missing data ..........................................................................................................................2
Missing data analysis ............................................................................................................2
Missing data imputation........................................................................................................7
Sensitivity analysis.................................................................................................................8
Survival prediction model ..................................................................................................8
Pulmonary fibrosis prediction model ..............................................................................10
Pulmonary hypertension prediction model......................................................................11
II. Statistical Analysis ............................................................................................................14
Standardised mortality ratio analysis .................................................................................14
Pulmonary complications and survival prediction models building .................................14
III. Threshold analysis...........................................................................................................15
Threshold analysis for survival model................................................................................15
Threshold analysis for pulmonary fibrosis model .............................................................16
Threshold analysis for pulmonary hypertension model ....................................................16
IV. Pulmonary complications and survival prediction models interpretation.................18
Survival model .....................................................................................................................18
Pulmonary fibrosis model ...................................................................................................18
Pulmonary hypertension model ..........................................................................................19
V. Analysis of cumulative incidence and antibody associations of pulmonary arterial
hypertension and clinically significant pulmonary fibrosis-associated pulmonary
hypertension separately.........................................................................................................21
VI. References ........................................................................................................................22
1
I. Missing data
Missing data analysis
The prediction models used 33 predictor and outcome variables, which are listed in Table I.1.
A relatively small proportion of data relating to demographic and general clinical
characteristics of the subjects as well as organ complications and vital status of the patients
were missing. Presence of PF could not be ascertained for 4 (1%) of the patients due to no
information regarding HRCT results, although in 2 of those pulmonary function tests were
available and based on those csPF could be reasonably excluded. In addition ethnicity data
were missing for 15 (3.8%) patients, Raynaud’s phenomenon onset date was not recorded in
25 (6.3%), smoking history in 55 (13.8%) and autoantibody specificities data were missing in
22 (5.5%) of the patients. A significant proportion of the patients had incomplete data
regarding clinical assessments and test results in the first years of their disease. For survival
and csPF prediction models we used data available within the first 3 years from disease onset,
while for the PH prediction model data were used if available within the first 5 years from
disease onset. For that reason the dataset used for the survival/PF prediction model has a
larger proportion of missing data compared to the dataset used for PH prediction. In
particular, mRss was assessed within the first 3 years of disease onset in 61% of the subjects
and within the first 5 years in 76% of them. PFT results within 3 years of disease onset were
available in 65% of the patients and within 5 years in 81% of them. Similar patterns were
observed in blood test results and assessments of Raynaud’s phenomenon severity, presence
of digital ulcers or gangrene, oesophageal involvement and tendon friction rubs (Table I.1).
2
Table I.1. Missing data in the variables used for the prediction models
Variables
Ethnicity
Male gender
Age at scleroderma onset
Diffuse subset
Raynaud's duration at onset of scleroderma
Smoking history
Polymyositis/Dermatomyositis overlap
Rheumatoid arthritis overlap
Sjogren’s syndrome overlap
Systemic lupus erythematosus overlap
Coding
Other/Asian/Black/Caucasian
Y/N
Years
Y/N
Months
Demographic and clinical
Non-/Past-/Active smoker
characteristics
Y/N
Y/N
Y/N
Y/N
Other/ACA/ATA/ARA/U3RNP/U1RNP/
Auto-antibodies
PMScl/ThRNP/ANA/ANAneg*
Pulmonary fibrosis
No/Mild/Clinically significant
Pulmonary hypertension
Y/N
Cardiac scleroderma
Y/N
Scleroderma renal crisis
Y/N
Organ complications
Death
Y/N
and
Time to clinically significant pulmonary fibrosis Months
vital status
Time to pulmonary hypertension
Months
Time to cardiac scleroderma
Months
Time to scleroderma renal crisis
Months
Time to death
Months
Modified Rodnan skin score
0 ÷ 51
Forced vital capacity (FVC)
% predicted
Carbon monoxide diffusion capacity (DLCO)
% predicted
Corrected diffusion capacity (KCO)
% predicted
Haemoglobin
g/dL
Erythrocyte sedimentation rate (ESR)
mm/1sth
First available assessments
Serum creatinine
μmol/L
Proteinuria
Y/N
Raynaud's grade 2-3
Y/N
Digital ulcers and/or gangrene
Y/N
Friction rubs
Y/N
Oesophageal involvement
Y/N
*Anti-centromere antibody; Anti-topoisomerase I antibody; Anti-RNA polymerase antibody; Anti-U3RNP antibody; Anti-U1RNP
Non-identified anti-nuclear antibody; Anti-nuclear antibody negative;
3
Missing data in the
survival/PF model, n (%)
15
(3.8)
0
(0.0)
0
(0.0)
0
(0.0)
25
(6.3)
55
(13.8)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
Missing data in the
PH model, n (%)
15
(3.8)
0
(0.0)
0
(0.0)
0
(0.0)
25
(6.3)
55
(13.8)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
22
22
(5.5)
(5.5)
4
(1.0)
4
(1.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
2
(0.5)
2
(0.5)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
0
(0.0)
156
(39.2)
96
(24.1)
139
(34.9)
74
(18.6)
139
(34.9)
76
(19.1)
267
(67.1)
179
(45.0)
150
(37.7)
76
(19.1)
156
(39.2)
84
(21.1)
145
(36.4)
77
(19.3)
135
(33.9)
83
(20.9)
153
(38.4)
93
(23.4)
152
(38.2)
92
(23.1)
136
(34.2)
84
(21.1)
182
(45.7)
149
(37.4)
antibody; Anti-PmScl antibody; Anti-ThRNP antibody;
Overall, for models using data available within the first 3 years of disease, only 88 subjects
(22%) had complete data for all variables, while for the model using data available within 5
years of disease onset, 136 (34%) of the subjects had complete data. As a result, it was judged
that analysing only cases with complete data would lose a substantial amount of available
information, and therefore reduce precision. In addition, complete case analysis gives valid
unbiased results only when data are missing completely at random (1,2). On the other hand,
multiple random imputation gives unbiased results with improved precision while coping with
data missing at random (the probability that an observation is missing is not related to the
missing value itself, but can be related to other variables in the analysis) (2,3). Neither approach
would produce valid results if missing data are not random.
For that reason we performed analysis of our data to assess if the assumption that data are
missing at random or completely at random is justified. Comparison between complete and
incomplete cases in terms of different variables showed some differences. In both datasets (3
year dataset and 5 year dataset) FVC and DLCO were significantly higher in the completed cases
compared to those that had at least one missing variable (difference of 6% and 8% for FVC and
8% and 9% for DLCO in the two datasets respectively, p<0.05). More importantly, completed
cases were more often of the diffuse SSc subset and ARA positive, while having lower
proportion of overlap syndromes, U1RNP positivity and lower cumulative incidence of PH and
PF. A very strong relationship was found between missing values and disease duration at the
time of patient referral. For the 3 and 5 year datasets at the time patients were first seen in our
centre, complete cases had mean disease duration of 8 and 10 months respectively compared to
32 and 35 months in the incomplete cases.
4
We also looked for any patterns in the missing data that may suggest that values were not
missing at random. In particular, as lower values of PFT results are generally associated with
pulmonary complications in SSc, we compared frequency of lung complications in patients with
and without missing PFTs. Similarly, higher serum creatinine levels and presence of proteinuria
are associated with renal involvement; therefore we compared frequency of SRC in patients with
and without missing creatinine and proteinuria data. In both 3 year and 5 year datasets we found
significantly higher frequency of PH among patients with missing PFT data. On the other hand,
there was no difference in frequency of csPF among those patients. In addition, we observed
significantly longer disease duration at first visit among patients with missing PFT values. PH
patients on average had longer disease duration at first visit (42 months) compared to non-PH
patients (24 months, p<0.001), which can explain why among patients with missing baseline
PFT data PH frequency was higher. For markers of renal function we found no difference in
SRC frequency between patients with missing and non-missing creatinine and proteinuria
information in the 3 year dataset. In the 5 year dataset there was a significantly lower proportion
of SRC cases in the group with missing proteinuria data (1.2% v 7.6%, p=0.038), but no
difference in SRC frequency in the missing and non-missing creatinine data patients.
Although patients with milder skin and organ disease are generally seen less frequently than
those with significant clinical problems, in this analysis we used only the first available
measurements and assessments, rather than repeated ones and those were used, if taken within
comparatively wide time windows (first 3 or 5 years of disease). As a result, frequency of
follow-up, which is strongly dependent on disease severity, is unlikely to have substantially
affected the patterns of missing data.
5
The majority of patients seen in our centre are not local to the hospital and often, as part of
shared care, they undergo disease monitoring, including regular blood tests and organ disease
screening in their local hospitals. Relevant results are generally forwarded to us and since they
are a necessary part of initial patient assessment, locally performed tests are often sent to us as
part of patient referral, therefore shared care is not necessarily associated with larger proportion
of missing data.
It is often difficult to ascertain the mechanisms leading to missing information and sometimes
those can be different for the different variables and even for different values. For example blood
test results done in a local hospital are less likely to be forwarded to a specialist centre,
especially if the patient has mild disease, while lung function results are routinely sent to us,
regardless of disease severity. On the other hand, missing lung function results, particularly
DLCO measurements, are often result of poor patient technique at the time of testing and are
generally due to SSc mouth involvement, although in some cases measurements could be too low
to detect, if lung involvement is very advanced.
Consistently in both datasets we observed significantly lower frequency of diffuse cutaneous
subset and longer disease duration at first assessment among patients with missing values for
mRss, Hb, ESR, serum creatinine and proteinuria which may reflect that patients with more
severe skin disease get diagnosed and referred to a specialist centre earlier and therefore have
less missing data relating to their early disease.
For that reason, our data were assumed to be missing at random (related to referral pattern and
disease subset) and disease duration at first assessment was included in the imputation model,
although not in the survival and pulmonary complication prediction analysis.
6
Missing data imputation
We performed multiple random imputation of missing variables using SPSS. All variables were
included in the imputation model and those having no missing data were used as predictors only.
Constraints based on the range of the available values were used for all missing continuous
variables. In addition, those with positively skewed distribution (ESR, Cr, mRss and time
between Raynaud’s and SSc onset) were log-transformed, while negatively skewed ones (Hb)
were square-root transformed in order to achieve near normal distribution prior to the imputation
procedure and the imputed values were subsequently exponentiated for the Cox regression
analysis. Variables with the least proportion of missing values were imputed first and those with
the largest were imputed last. Linear regression was used for imputation of continuous and
logistic regression for imputation of categorical variables. Imputation method used was fully
conditional specification and 25 imputed datasets were created. The prediction model results
presented are pooled from the analysis of the 25 imputed datasets.
Comparisons between observed and imputed values demonstrated significantly greater frequency
of proteinuria and friction rubs and lower levels of mRss in the imputed group for both 3 year
and 5 year datasets. In addition, the 5 year dataset had lower imputed DLCO levels (mean DLCO
of 54%) compared to the observed DLCO values (mean DLCO of 66%, p=0.006). When
comparing observed and imputed mRss and DLCO levels for lcSSc and dcSSc subset separately,
the only difference we found was for DLCO levels in lcSSc patients (mean observed 67%, mean
imputed 54%, p=0.003).
7
Sensitivity analysis
Sensitivity analysis was performed to assess the degree to which missing data imputation has
affected the findings of the analysis. Missing values were substituted with their minimum and
maximum imputed values and prediction models were derived in both datasets.
Survival prediction model
When missing values were substituted with their imputed minimum, the derived survival model
was very similar to the one based on the multiply imputed dataset. The only additional variable
in the model was presence of proteinuria, which together with serum creatinine levels is a marker
of renal involvement (Table I.2).
Table I.2. Survival prediction model derived in the dataset with missing
values substituted with their imputed minimum values
β
p-value
HR
95.0% CI for HR
DcSSc
0.425
0.023
1.530 1.059
2.210
Age at onset
0.049 <0.001
1.050 1.035
1.065
DLCO
-0.017 <0.001
0.984 0.976
0.991
Hb
-0.179
0.001
0.836 0.752
0.931
Cr
0.003
0.001
1.003 1.001
1.004
Proteinuria
1.431
0.001
4.182 1.811
9.656
PH3y
1.372
0.001
3.943 1.761
8.828
Cardiac SSc3y
1.843 <0.001
6.318 2.746 14.538
Derivation of the survival model in a dataset where missing values were substituted with their
imputed maximum demonstrated that subset, age, presence of PH and cardiac SSc are
significantly associated with survival. Although lung function results did not remain in the final
model, presence of csPF and ATA positivity, which are associated with lower FVC and DLCO,
did. In this model, smoking history was also a significant predictor of survival. Serum creatinine
levels were no longer in the model, but were substituted by history of SRC (Table I.3).
8
Table I.3. Survival prediction model derived in the dataset with missing values
substituted with their imputed maximum values
β
p-value
HR
95.0% CI for HR
DcSSc
0.379
0.035
1.460
1.028
2.075
Age at onset
0.051 <0.001
1.052
1.036
1.068
ATA
0.634
0.001
1.886
1.306
2.723
PF3y
0.527
0.009
1.694
1.141
2.515
Smoking history - no
reference category
Smoking history - ex
0.286
0.182
1.330
0.875
2.024
Smoking history - current
0.774 <0.001
2.168
1.462
3.216
RC3y
1.078 <0.001
2.939
1.650
5.236
PH3y
1.398
0.001
4.047
1.795
9.124
Cardiac SSc3y
1.404
0.004
4.072
1.554 10.670
When the model derived from the imputed dataset was tested in the datasets where missing
values were substituted by their imputed minimums and maximums, the model performed
comparatively well with only serum creatinine levels not showing significant association with
the outcome in the dataset using imputed maximums of the missing variables (Table I.4).
Dataset with imputed
maximum values
Dataset with imputed
minimum values
Table I.4. Performance of the survival prediction model derived from the
imputed dataset in the datasets with using imputed minimums and
maximums of missing values
β
p-value
HR
95.0% CI for HR
DcSSc
0.461
0.014
1.585
1.099
2.287
Age at onset
0.049 <0.001
1.050
1.035
1.065
DLCO
-0.017 <0.001
0.984
0.976
0.991
Hb
-0.177
0.001
0.838
0.754
0.932
Cr
0.003
0.001
1.003
1.001
1.004
PH3y
1.395
0.001
4.034
1.810
8.991
Cardiac SSc3y
1.799 <0.001
6.045
2.631 13.889
DcSSc
0.572
0.002
1.771
1.235
2.540
Age at onset
0.051 <0.001
1.052
1.037
1.068
DLCO
-0.015 <0.001
0.985
0.978
0.993
Hb
-0.180
0.001
0.835
0.750
0.931
Cr
0.0001
0.803
1.000
0.999
1.001
PH3y
1.423
0.001
4.151
1.799
9.577
Cardiac SSc3y
1.857 <0.001
6.407
2.736 15.004
9
Pulmonary fibrosis prediction model
The PF prediction models derived in the datasets with missing values substituted with their
imputed minimums and maximums were similar to the model derived in the multiply imputed
dataset. In particular, ACA remained significant negative predictor, while ATA was a strong
positive predictor of csPF. Reduction in DLCO also significantly increased the risk of csPF in
both models, although FVC remained a significant predictor only in the model derived in the
dataset with missing values substituted with their imputed maximums. Age at onset was also
present only in this model, while diffuse subset was associated with signifivant increase in the
hazard for csPF only in the model derived from the dataset where missing values were
substituted with their imputed minimums (Tables I.5 and I.6).
Table I.5. Clinically significant pulmonary fibrosis prediction model derived
in the dataset with missing values substituted with their imputed minimum
values
β
p-value
HR
95.0% CI HR
DcSSc
0.538
0.011
1.712
1.129
2.597
DLCO
-0.026 <0.001
0.974
0.965
0.983
ESR
0.023 <0.001
1.024
1.015
1.033
ACA
-1.658 <0.001
0.191
0.090
0.405
ATA*T(years)
0.188 <0.001
1.207
1.106
1.317
Table I.6. Clinically significant pulmonary fibrosis prediction model derived
in the dataset with missing values substituted with their imputed maximum
values
β
p-value
HR
95.0% CI HR
Age at onset
0.017
0.022
1.017
1.002
1.032
FVC
-0.015
0.003
0.985
0.975
0.995
DLCO
-0.012
0.033
0.988
0.977
0.999
ACA
-2.051 <0.001
0.129
0.046
0.356
ATA*T(years)
0.173 <0.001
1.188
1.096
1.288
10
When the model derived in the multiply imputed dataset was tested in the two datasets using the
minimum and maximum imputed values for the missing variables, the model demonstrated a
comparatively good fit with FVC showing no association with csPF in the dataset using imputed
minimums of the missing values and disease subset showing no association with csPF in the
dataset using imputed maximums of the missing values.
Dataset with
imputed maximum
values
Dataset with
imputed minimum
values
Table I.7. Performance of the clinically significant pulmonary fibrosis prediction
model derived from the imputed dataset in the datasets using imputed minimums
and maximums of missing values
β
p-value
HR
95.0% CI HR
DcSSC
0.790 <0.001
2.203
1.463
3.317
Age at onset
0.015
0.050
1.015
1.000
1.030
FVC
0.004
0.451
1.004
0.994
1.014
DLCO
-0.025
0.001
0.975
0.961
0.990
ACA
-1.659 <0.001
0.190
0.089
0.405
ATA*T(years)
0.180 <0.001
1.198
1.099
1.305
DcSSC
0.129
0.518
1.138
0.769
1.682
Age at onset
0.018
0.019
1.018
1.003
1.033
FVC
-0.015
0.005
0.985
0.975
0.996
DLCO
-0.012
0.040
0.988
0.977
0.999
ACA
-1.992 <0.001
0.136
0.048
0.384
ATA*T(years)
0.175 <0.001
1.191
1.098
1.291
Pulmonary hypertension prediction model
Derivation of prediction models for PH in the datasets with missing values substituted with their
imputed minimums and maximums demonstrated almost identical results (Table I.8 and I.9).
Both showed, similar to the model derived from the multiply imputed dataset, that age at onset,
DLCO, ARA, AFA, SRC and its interaction with DLCO were significant predictors of PH
development. On the other hand, unlike the originally developed model, ATA and serum
creatinine levels did not show any significant association with PH, although both models
included presence of proteinuria.
11
Table I.8. Pulmonary hypertension prediction model derived in the
dataset with missing values substituted with their imputed minimum
values
β
p-value
HR
95.0% CI for HR
Age at onset
0.035
0.001
1.035
1.014
1.057
DLCO
<0.001
-0.046
0.955
0.939
0.971
Proteinuria
<0.001
2.069
7.914
3.093 20.248
Raynaud’s severity
0.992
0.015
2.698
1.217
5.982
ARA
1.405
0.003
4.076
1.628 10.204
AFA
1.208
0.004
3.347
1.485
7.545
RC5y
-4.212
0.011
0.015
0.001
0.377
DLCO*SRC5y
0.072
0.006
1.075
1.021
1.132
Table I.9. Pulmonary hypertension prediction model derived in the
dataset with missing values substituted with their imputed maximum
values
β
p-value
HR
95.0% CI for HR
Age at onset
0.032
0.003
1.033
1.011
1.055
DLCO
<0.001
-0.031
0.970
0.959
0.980
Proteinuria
<0.001
1.560
4.758
2.720
8.323
ARA
1.056
0.023
2.876
1.159
7.137
AFA
1.348
0.001
3.848
1.697
8.727
RC5y
-2.717
0.087
0.066
0.003
1.483
DLCO*SRC5y
0.044
0.034
1.045
1.003
1.087
The original PH prediction model, derived in the multiply imputed dataset, demonstrated a
relatively good fit in the datasets using the imputed minimum and maximum values in the
missing variables (Table I.10). ATA did not show a significant association with PH development
in either test dataset, although there was a trend towards significance in the dataset using the
imputed maximum of the missing values. ARA showed significant association with PH only in
the dataset using imputed minimums of the missing values and while history of SRC in the first 5
years of disease was significant in both datasets, its interaction with DLCO was significant only
in the dataset using imputed maximums of the missing values.
12
Dataset with imputed
maximum values
Dataset with imputed
minimum values
Table I.10. Performance of the pulmonary hypertension prediction model derived
from the imputed dataset in the datasets using imputed minimums and maximums of
missing values
β
p-value
HR
95.0% CI HR
Age at onset
0.030
0.004
1.031
1.010
1.052
DLCO
-0.030
<0.001
0.970
0.960
0.981
Cr
0.004
0.006
1.004
1.001
1.007
ATA
-0.497
0.228
0.608
0.271
1.364
ARA
1.103
0.027
3.013
1.134
8.007
AFA
1.088
0.009
2.969
1.317
6.695
RC5y
-2.839
0.048
0.059
0.004
0.970
DLCO*RC5y
0.034
0.107
1.035
0.993
1.079
Age at onset
0.033
0.002
1.034
1.012
1.056
DLCO
<0.001
-0.033
0.968
0.956
0.980
Cr
<0.001
0.004
1.004
1.002
1.006
ATA
-0.782
0.062
0.458
0.201
1.040
ARA
0.681
0.141
1.975
0.798
4.890
AFA
1.485
0.001
4.414
1.903
10.237
RC5y
-4.093
0.011
0.017
0.001
0.394
DLCO*RC5y
0.050
0.019
1.051
1.008
1.096
Although the final models that were derived during the sensitivity analysis had some differences
from the ones derived in the multiply imputed datasets, the predictor variables included did
overlap to great extent. In the majority of cases the different predictor variables reflected similar
clinical problems, for example serum creatinine levels, proteinuria and history of SRC. This
suggests that, even though the missing value imputation has affected the specific variables found
to be predictors of survival and pulmonary complications, the general associations described in
the prediction models are independent of the methods used for handling of missing data.
13
II. Statistical Analysis
Standardised mortality ratio analysis
Standardized mortality ratios (SMRs) were calculated by dividing number observed and
expected deaths in our cohort and 95% CIs for SMRs were calculated as SMR±1.98xSESMR
=SMR±1.98 x number observed deaths1/2/number expected deaths (4). Yearly expected numbers
of deaths were calculated by multiplying gender-specific mortality rates and number of patients
at risk for each age group. We used the published interim life tables for England, publically
available on the website of the UK Office for National Statistics (5). As our group consisted of
patients with disease onset between 1995 and 1999, for the first year of follow-up we used
expected mortality rates from the 1996-1998 life table and then the consecutive yearly rate for
the subsequent follow-up years. At the time of data analysis the most recent life table available
was based on data from years 2008-2010, which allowed for 13 years of follow-up.
Pulmonary complications and survival prediction models building
To build pulmonary complications and survival prediction models, we used Cox regression
analysis. Initially, univariable analysis was used to identify variables that significantly predict
the outcome of interest (p≤0.05). They were subsequently included in multivariable analysis. In
addition, variables that did not show significant association, but were judged clinically relevant,
were forced in the multivariable models, to assess for potential interaction effects. Proportional
hazards assumption was tested using log minus log plots and Schoenfeld residual plots. If there
was an indication for proportionality violation, extended Cox regression models, allowing for
use of time-dependent covariates, were used to fit time interaction terms. These were kept in the
final model, if statistically significant (p≤0.05). Difference between -2 Log likelihood ratios was
used to compare the fit of nested models and Harrell’s c was calculated to assess discrimination
of the final models where possible.
14
III. Threshold analysis
In order to make the prediction models easier to apply in practice, threshold analysis was used to
look for appropriate categorisation of continuous variables. We compared KM survival estimates
and Cox regression-derived hazard ratios to identify predictor variable cut-off points associated
with the most significant separation in outcome. The models were then run using the categorised
predictor variables. We used the sum of rounded β estimates or doubled β estimates where
appropriate, for each variable category to calculate risk scores for death, csPF and PH.
Threshold analysis for survival model
Continuous predictor variables were stratified to find optimal levels for categorisation. Age was
divided into groups of <20, 21-30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, 61-65, 66-70, 7175 and >75 years. Cox regression analysis showed that hazard of death increased significantly
for patients with age at disease onset >60 years (HR 3.3, 95%CIs 2.3-4.7, p<0.001). Similarly
DLCO was divided into groups with levels of ≤30, 30-35, 35-40,40-45, 45-50, 50-55, 55-60,6065,65-70, 70-75, 75-80, 80-85 and >85% and the risk of death was significantly higher for
patients with DLCO<65% (HR 2.3, 95%CIs 1.6-3.3, p<0.001). For haemoglobin we used the
normal range as cut-off points and patients with Hb<11.5g/dL had significantly increased hazard
of death (HR 1.9, 95%CIs 1.2-3, p=0.004). Finally, serum creatinine levels were divided into
groups of <70, 70-80, 80-90, 90-100, 100-150, 150-200, and >200μmol/L and in a univariable
Cox regression, levels greater than 100 μmol/L were associated with significant increase in the
hazard of death (HR 2.1, 95%CIs 1.3-3.5, p=0.004). The model with categorical variables is
described in Table 4.
15
Threshold analysis for pulmonary fibrosis model
Age at onset, FVC and DLCO were categorised to make the model easier to use. Age was split
into groups of ≤30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, 61-65 and >65 years. The greatest
separation in KM estimates of csPF development was seen between patients with age of up to 55
years and those above 55 years, although the difference remained non-significant, consistent
with the findings of the univariable analysis. FVC was divided into groups of ≤30, 31-35, 36-40,
41-45, 46-50, 51-55, 56-60, 61-65, 66-70, 71-75, 76-80 and >80%. Three groups had distinct
difference in association with development of csPF – patients with FVC>80%, FVC between
65% and 80% (HR 2.7, 95%CIs 1.6-4.6, p<0.001, compared to FVC>80%) and FVC<65% (HR
8.3, 95%CIs 4.88-14.1, p<0.001, compared to FVC>80%). Finally DLCO was similarly divided
into groups of ≤30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, 61-65, 66-70, 71-75, 76-80 and
>80%. DLCO of up to 55% was associated with HR 5.7 (95%CIs 3.46-9.52, p<0.001) compared
to DLCO>55%. The final model using categorised variables is shown in Table 4. Rounded
doubled β values were used as risk points. The interaction of ATA and disease duration had β of
0.141; therefore it could contribute a risk point for approximately every 4 years of follow-up.
Threshold analysis for pulmonary hypertension model
Threshold analysis, looking for optimal categorisation of the continuous predictors in the model
(age at onset, serum creatinine levels and DLCO) was undertaken to make the model easier for
practical application. Age was initially split into groups of <36, 36-40, 41-45, 46-50, 51-55, 5660, 61-65 and >65. Univariable Cox regression analysis demonstrated that hazard of PH
increased significantly for groups of age greater than 55. Further analysis showed that age of SSc
onset greater than 55 was associated with HR 2.2 (95% CIs 1.3-3.8, p=0.002) and at the end of
follow-up 22% (n=24) of those patients had developed PH compared to 13% (n=37) among
those aged 55 or younger, p=0.029. Similarly creatinine levels were initially split into groups of
16
<70, 70-80, 80-90, 90-100, 100-150, 150-200, 200-300 and >300μmol/L and significant increase
in HR was seen for groups with levels above 90, although separation was relatively mild. Further
analysis demonstrated that the greatest separation in risk for PH was seen with a cut-off point of
85 units. Levels of 85 or above were associated with HR of 2.3 (95% CIs 1.3-4, p=0.004)
compared to levels below 85 units. Finally, DLCO was split into groups of <55, 55-60, 61-65,
66-70, 71-80 and >80%. There was no significant increase in hazard for PH development in the
groups with DLCO>65% and the hazard associated with DLCO<55% was much greater than
that of DLCO between 55% and 65%; therefore DLCO was split into 3 groups. Univariably,
DLCO between 55% and 65% was associated with HR 2.9 (95% CIs 1.1-7.9, p=0.033) and
DLCO<55% was associated with HR 8.7 (95% CIs 3.8-19.9, p<0.001) compared to
DLCO>65%. Details of the model using categorical variables are presented in Table 4.
17
IV. Pulmonary complications and survival prediction models interpretation
Survival model
Each 1 year increment in age at disease onset increased hazard of death by 5%. Patients with
dcSSc were 51% more likely to die compared to patients with lcSSc, if other characteristics were
the same. A decrease of 1% in DLCO at baseline increased hazard of death by 2%; a decrease by
1 g/dL in baseline haemoglobin level increased hazard of death by 21% and an increase in serum
creatinine levels by 1 μmol/L increased hazard of death by 0.3%.
Pulmonary fibrosis model
Based on this, our findings show that patients with dcSSc have 77% higher hazard for
development of csPF and for each 1 year age increment at disease onset, the hazard of csPF
increases by 2%. If all other characteristics are the same, 1% lower FVC or DLCO is associated
with 3% higher hazard of csPF. As previously shown, ACA was protective and patients who
were ACA positive had over 80% reduction in the risk of csPF. On the other hand, ATA
positivity increased the risk for development of csPF by 16% for every year of disease duration.
For example, a patient who is ATA positive will have exp(0.149)=1.16-1=16% higher hazard of
development of csPF after one year of disease, compared to other patients with similar other
characteristics, but who do not carry ATA. That hazard will increase to exp(5x0.149)=2.101=110% increase in the hazard of csPF after 5 years of SSc.
Risk score points for scPF were calculated, based on the rounded doubled β values from the
regression analysis. The interaction between ATA and time in years was associated with β of
0.141. In order to have a β value of around 0.5 (corresponding to 1 risk point), time was
recalculated to reflect 4 yearly periods. This yielded β value of 0.566 associated with the
interaction. The interpretation would be that in patients, who are ATA positive, for every 4 years
18
of disease duration from onset, a 1 risk point should be added to the risk score predicting csPF
development. We believe these results are due to the strong association of ATA and csPF and the
early development of csPF in the disease course. The longer an ATA positive patient has had
SSc, the greater the risk of csPF development, if it has not developed already.
Pulmonary hypertension model
The model demonstrated that older age at disease onset, increase in serum creatinine and
presence of ARA or AFA are associated with increased risk of PH, while ATA positivity
reduced the hazard of PH. In particular, if other predictor variables were kept constant, patients
who carried ARA were more than 3 times more likely to develop PH and those who carried AFA
nearly 4 times more likely to develop PH compared to those who carried other autoantibodies.
Each additional year of age at disease onset contributed 3% increase in the hazard of PH, while 1
μmol/L increase in serum creatinine increased the hazard for PH by 0.4%. ATA positivity was
associated with 59% reduction in the hazard for PH. The interaction between history of SRC in
the first 5 years of disease and DLCO revealed an interesting effect, where the effect of one
variable could be positive or negative depending on the value of the other. When other covariates
were kept unchanged, in patients who did not develop SRC within the first 5 years of their
disease, a 1% lower DLCO was associated with (1-0.939=0.06) 6% increase in the hazard of PH.
On the other hand, in patients with history of SRC, 1% lower DLCO was associated in fact with
(1-0.939*1.082=-0.016) 1.6% reduction in the hazard of PH. Similarly, history of SRC also had
different effect on the overall hazard of PH depending on the DLCO level with associated HR
being greater than one (increased hazard) when DLCO>70.8% and less than one (reduced
hazard) when DLCO<70.8%. For that reason and in order to make the interaction easier to
interpret, DLCO was centered at 70.8%. Based on this, when DLCO is 70.8%, the HR associated
SRC is approximately equal to one (no effect) (95%CIs 0.2-5.7, p=0.999). With every 1%
19
increase in DLCO, presence of SRC increases the hazard of PH by about 8%, while for DLCO
lower than 70.8, presence of SRC reduces the hazard for PH. In practical terms this means that
when patients had preserved DLCO, history of SRC was associated with a small increase in the
hazard of PH, while when patients had low DLCO, history of SRC was associated with reduced
hazard of PH, which cancelled out the effect of the low DLCO. This suggests that while lower
DLCO strongly predicts PH in patients who have not had SRC, in patients who have had SRC,
DLCO levels are not a particularly powerful predictor of PH.
20
V. Analysis of cumulative incidence and antibody associations of pulmonary arterial
hypertension and clinically significant pulmonary fibrosis-associated
fibrosis associated pulmonary
hypertension separately
Cumulative incidence of PAH and PF-associated
PF associated PH in dcSSc and lcSSc patients
Figure A1. There was no significant difference in the cumulative incidence of PAH or PF
PFassociated PH between the two major disease subsets of SSc.
In addition, associations between
tween ACA/ATA and PAH/PF-associated
PAH/PF associated PH mirrored the results of
the analysis based on data for PAH and PF-PH
PF PH pooled together. As shown below, ACA was a
strong negative predictor of PF-PH
PF PH in keeping with its protective effect with relation to PF
development. ATA had significant negative association with PAH, similar to its negative
association with PH as a whole while it was not significantly associated with PF
PF-PH
development alone. ACA had no association with PAH.
PF-PH
ACA
ATA
β
-1.553
1.553
0.208
p-value
0.036
0.660
HR
0.212
1.231
95.0% CI for HR
0.050
0.900
0.488
3.102
PAH
ACA
ATA
β
-0.095
0.095
-2.161
2.161
p-value
0.804
0.035
HR
0.910
0.115
95.0% CI for HR
0.431
1.919
0.015
0.858
21
VI. References
1. Breslow N.E. & Day N.E. Rates and rate standardization. Statistical Methods in Cancer
Research. Volume II - The Design and Analysis of Cohort Studies. Oxford University
Press; 1987. p. 48-82.
2. http://www.ons.gov.uk
3. Thomas R. Belin, Ming-yi Hu, Alexander S. Young, Oscar Grusky. Using multiple
imputation to incorporate cases with missing items in a mental health services study.
Health services and outcomes research methodology. 2000; Volume 1, Issue 1, pp 7-22.
4. Mackinnon A. The use and reporting of multiple imputation in medical research - a
review. J Intern Med. 2010; 268(6):586-93. Review.
5. Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple
imputation for missing data in epidemiological and clinical research: potential and
pitfalls. BMJ. 2009; 338:b2393.
22